Developing Industrial Case-Based Reasoning Applications

Lecture Notes in Artificial Intelligence Edited by J. G. Carbonell and J. Siekmann Subseries of Lecture Notes in Compu...

Author: Ralph Bergmann | Klaus-Dieter Althoff | Sean Breen | Mehmet Göker | Michel Manago | Ralph Traphöner | Stefan Wess

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Lecture Notes in Artificial Intelligence Edited by J. G. Carbonell and J. Siekmann

Subseries of Lecture Notes in Computer Science

1612

3

Berlin Heidelberg New York Hong Kong London Milan Paris Tokyo

Ralph Bergmann Klaus-Dieter Althoff Sean Breen Mehmet Göker Michel Manago Ralph Traphöner Stefan Wess

Developing Industrial Case-Based Reasoning Applications The INRECA Methodology 2nd Edition

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Authors Ralph Bergmann University of Hildesheim, Germany E-mail: [email protected] Klaus-Dieter Althoff Fraunhofer IESE, Kaiserslautern, Germany E-mail: [email protected] Sean Breen CP Ltd., Ballinaclash, Co. Wicklow, Ireland E-mail: [email protected] Mehmet Göker Kaidara, Los Altos, CA, USA E-mail: [email protected] Michel Manago Kaidara, Paris, France E-mail: [email protected] Ralph Traphöner Stefan Wess empolis GmbH, Kaiserslautern, Germany E-mail: {Ralph.Traphoener;Stefan.Wess}@empolis.com

Cataloging-in-Publication Data applied for A catalog record for this book is available from the Library of Congress. Bibliographic information published by Die Deutsche Bibliothek Die Deutsche Bibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available in the Internet at . CR Subject Classification (1998): I.2, J.1, J.3 ISSN 0302-9743 ISBN 3-540-20737-6 Springer-Verlag Berlin Heidelberg New York ISBN 3-540-66182-4 (first edition) Springer-Verlag Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copyright Law. Springer-Verlag is a part of Springer Science+Business Media springeronline.com c Springer-Verlag Berlin Heidelberg 1999, 2003 Printed in Germany Typesetting: Camera-ready by author, data conversion by PTP-Berlin, Protago-TeX-Production GmbH Printed on acid-free paper SPIN: 10977200 06/3142 543210

Preface Adding CBR Back into the Lexicon: Today’s CBR Technology Not Only Works, It Makes Strategic Sense John Ragsdale, Giga Research Director, Forrester Research

Let me start by stating: case-based reasoning is not evil. This may come as a surprise to those of us who have been in and around the world of CRM (customer relationship management) and technical-support knowledge-base products for a number of years. I personally have been guilty of associating “case-based reasoning” (CBR) with systems that are hardcoded, inflexible, difficult and expensive to maintain, and hard to use effectively. But it is important to separate the concept of CBR from its (supposed) instantiations. Early knowledge-base products created for technical-support agents in the late 1980s and early 1990s were sometimes billed as CBR. The first such systems I encountered took a year to implement in my support center (all custom work by a systems integrator [SI]), and a week after go-live my agents staged a walkout. Either the system went, or they did. Talk about low-adoption rates. Many support managers were burned by systems sold as “CBR,” and I have encountered quite a few other ex-technical support or helpdesk managers, some of them now VPs of support or even at the CIO level, who have a visceral reaction to the term “CBR.” Their reaction is often a surprise to software developers and those from the more academic side of knowledge management. So before you begin this truly insightful and promising book about case-based reasoning and learn how to more effectively access and leverage corporate knowledge, let’s clear up what CBR is not: – CBR is not a set of solution documents organized in a hardcoded index “tree.” – CBR does not force agents or customers to go step by step down a predefined path through the minutia of choices that never exactly fit the situation or problem. – CBR does not mean a single static solution to a single static problem that requires a programmer to add, change or delete. Having worked with and for software vendors selling intelligent knowledgemanagement products geared toward technical support agents and/or customers performing Web self-service, I’ve often advised against using the term “CBR” for

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Preface

fear of backlash from prospects, who like me, may have been burned by a really bad implementation sold as CBR. But after reading the first edition of this book, and talking with Michel Manago, one of the co-authors, about what CBR actually means, I think it is time to revive the CBR term. It’s also time to stop punishing the entire CBR concept for the sins of a few early software vendors and greedy SIs. And it’s time to move forward with learning how to use CBR technology to streamline customer service operations, cut the operating costs of technical support centers, and offer stellar web self-service options to customers who have the need, the desire, or just the plain old moxie to want to figure things out on their own.

The Business Case for Easier Access to Knowledge In most customer service centers, customer interactions are increasing as much as 30 percent year after year, counting all of the new “e” interaction channels, such as e-mail and chat, in addition to phone calls. The percentage of interactions by channel is changing as well, as more customers adopt electronic channels. While the volume of phone calls received by the average support operation may remain the same, or increase slightly as other interaction channels grow in popularity, phone calls constitute a smaller percentage of overall interactions. E-mails are expected to pass phone calls in volume in most industries by 2010. All of this is forcing companies to fund initiatives for customer self-service, hoping to entice more and more customers to attempt self-service before calling for assistance or at least to streamline agent processing of customer questions with robust knowledge-base options. Ideally, these scalable solutions allow companies to compensate for increases in interaction volume year after year by minimizing both the number of interactions involving agents and the time agents spend processing each interaction. Expecting even a trained agent (let alone a novice customer) to find useful information in a static knowledge base by using only a full text search is unrealistic. Customers cannot possibly know all of the key “symptom” words to include, and the more textual clues the customer provides in the search string, the larger the number of possible matches returned. Most customers attempting selfservice will abandon all hopes of resolving their own problems when the text search returns over 1,000 possible matches. And such results train customers to never attempt self-service again, forcing them to migrate to the most expensive support channel: a live phone call with an agent. Case-based reasoning makes sense in those industries where internal or external customer support involves disparate or complex hardware or software configurations, multiple technical environments or platforms, and varying levels of service or warranty options, as well as vastly different experience levels across the enterprise or customer base.

Adding CBR Back into the Lexicon

VII

Why CBR Makes Sense Now — Technically CBR solutions have credibility today because three main technological areas have come a long way since the 1980s: linguistic analysis, consolidated customer databases, and intelligent analytics. Natural Language Searching (NLS) comes in many flavors, far too many to describe and differentiate here. But one area of NLS, linguistic analysis, has moved out of the laboratory and into packaged software products, allowing a system to intelligently understand what a customer (or agent) is asking, not just to perform a keyword search. It’s also about attribution, i.e., the amount of ancillary data that can be factored into a problem diagnostic session to more succinctly identify the correct answer or knowledge “chunk.” Before the days of XML and SOAP interfaces, only minimal data was passed between systems, and few environmental variables were stored in a single database. CRM has changed all that. A single CRM customer-information model can create a “360-degree view” of the customer, listing every product the customer has purchased, the technical environment in place (Unix vs. Linux vs. NT, or Oracle vs. Sybase vs. SQL), every problem the customer has experienced in the past, and any other information a company may choose to capture, such as the user’s skill or training level, preferred language, etc. With this amount of attributable information at the disposal of a CBR application, customers (or agents) need not manually supply background information (or follow an index tree answering basic questions). The context of each question is automatically established, allowing a CBR system to more granularly identify a case that meets the exact question in relation to all environmental variables. The more the system is used, and the more users indicate which solutions solved which problems, the more environmental data from all of those users can be captured within the case framework, thus allowing the system to continually “learn.” Add to this the advent of analytic engines that can make intelligent, real-time and even personalized decisions and recommendations based on any amount of available data, past history, and any rules in place. Though both software vendors and IT developers are wary of the term “artificial intelligence” (AI) — often because they were burned by early CBR systems that were said to use AI but failed to deliver — real-time analytics is here, and vendors have customer references to prove it. A lot of things have changed since the mid—1980s. Giving agents tools to increase productivity and offering robust customer self-service systems is no longer an academic exercise; it is the only way large support operations can even come close to meeting customer expectations in this age of budget cuts and staff downsizing. And today’s CBR-based products don’t take a year to implement; they can guarantee a positive return on investment in as little as three months. For many support environments, CBR is a logical choice.

Table of Contents

About This Book ...............................................................................................1 Acknowledgments .............................................................................................5

Part I :

Smarter Business with Case-Based Decision Support .......................................................................7

1. Making Smarter Business Decisions in Less Time .......................9 1.1 1.2

1.3

1.4 1.5

1.6 1.7 1.8

Why Case-Based Decision Support? ......................................................10 What Is Case-Based Decision Support?..................................................11 1.2.1 Case-Based Reasoning.................................................................11 1.2.2 Knowledge Discovery and Data Mining......................................13 Successful Applications of Case-Based Decision Support .....................14 1.3.1 Management of Industrial Knowledge.........................................14 1.3.2 Sales Support ...............................................................................14 1.3.3 Reliability Analysis......................................................................14 1.3.4 Technical Support........................................................................15 Benefits of Case-Based Decision Support ..............................................15 Case-Based Decision Support and Alternative Techniques ....................16 1.5.1 Statistics.......................................................................................16 1.5.2 Expert Systems ............................................................................16 1.5.3 Databases .....................................................................................17 Development Process and Case Collection .............................................17 How Are Cases Retrieved? .....................................................................18 How Are Cases Represented? .................................................................19

2. Case-Based Reasoning Approaches ..................................................21 2.1 2.2 2.3 2.4 2.5 2.6

The Textual CBR Approach ...................................................................22 The Conversational CBR Approach........................................................23 The Structural CBR Approach ................................................................25 Comparing the Different Approaches .....................................................28 Effort Required for the Different Approaches ........................................31 Overall Assessment of the Different CBR Approaches ..........................33

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3. Selected Applications of the Structural Case-Based Reasoning Approach...............................................................................35 3.1

3.2

3.3

3.4

HOMER: A Case-Based CAD/CAM Help-Desk Support Tool..............36 3.1.1 DaimlerChrysler’s Motivation .....................................................36 3.1.2 Initial Situation at the CAD/CAM Help-Desk .............................36 3.1.3 Project Goals and Benefits...........................................................38 3.1.4 Knowledge Representation ..........................................................39 3.1.5 The HOMER System ...................................................................40 3.1.6 Users and Roles ...........................................................................44 3.1.7 Lessons Learned ..........................................................................44 3.1.8 Results Achieved .........................................................................45 3.1.9 Summary......................................................................................46 A Case-Based Product Catalog of Operational Amplifiers.....................47 3.2.1 The Application Development Process........................................48 3.2.2 Benefit Analysis...........................................................................48 3.2.3 Similarity Functions Used in the Application..............................49 3.2.4 How the Software Works.............................................................50 3.2.5 Status of the Application .............................................................54 Case-Based Maintenance Support for TGV Trains.................................55 3.3.1 TGV’s Environment ....................................................................55 3.3.2 A Hierarchy of Data Models and Case Bases ..............................55 3.3.3 Technical Approach for Diagnosis ..............................................57 3.3.4 Example of the System in Action ................................................58 3.3.5 Integration into the End-User Environment.................................59 3.3.6 Status of the Application .............................................................61 The Fraunhofer IESE Experience Factory ..............................................61 3.4.1 Fraunhofer IESE’s Motivation.....................................................61 3.4.2 Initial Situation ............................................................................62 3.4.3 Sharing Project Experience..........................................................63 3.4.4 Benefits of Sharing Explicitly Stored Experience .......................64 3.4.5 Organizing a Best-Practice Repository ........................................64 3.4.6 Populating the Case Base.............................................................66 3.4.7 Envisioned Usage of Lessons Learned ........................................68 3.4.8 Conclusion ...................................................................................70

Table of Contents

Part II:

XI

Developing Case-Based Application with the INRECA Methodology ........................................71

4. Practical Guidelines for Developing Case-Based Reasoning Applications .........................................................................73 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9

Define Your Measures of Success ..........................................................74 Orient Your Customers ...........................................................................75 Get the Big Picture..................................................................................76 Think about Maintenance........................................................................77 Welcome Feedback and Testing .............................................................78 Measure the Success ...............................................................................79 Direct Access for the Customer ..............................................................80 Market Your Success ..............................................................................81 Expand the Scope....................................................................................82

5. Professional Case-Based Reasoning Application Development...............................................................................................85 5.1

Why Do You Need the INRECA Methodology?....................................86 5.1.1 How You Benefit from a Methodology .......................................86 5.1.2 What the INRECA Methodology Can Do for You......................86 5.1.3 Ensuring High-Quality Software .................................................87 5.2 Foundations from Software Engineering ................................................87 5.2.1 The Experience Factory and the Quality Improvement Paradigm .................................................................................................88 5.2.2 Software Process Modeling .........................................................90 5.3. The CBR Experience Base......................................................................92 5.3.1 Experience Captured in Software Process Models ......................93 5.3.2 Experience on Different Levels of Abstraction ...........................93 5.4 How to Apply the INRECA Methodology to New Projects ...................94 5.5 Capturing Your Own CBR Experience...................................................96 5.6 The INRECA Methodology and Quality Standards................................98

6. Documenting Case-Based Reasoning Development Experience...................................................................................................99 6.1

6.2

Into the Details of Processes, Products, and Methods...........................100 6.1.1 Software Processes: The Basic Activities..................................100 6.1.2 Technical, Organizational, and Managerial Processes...............101 6.1.3 How Processes Interact..............................................................101 6.1.4 Combining Processes to Process Models...................................103 Generic and Specific Descriptions ........................................................103 6.2.1 Generic Descriptions .................................................................104

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6.3

6.2.2 Specific Descriptions .................................................................104 Detailed Description of Process Models ...............................................105 6.3.1 Process Description Sheets ........................................................106 6.3.2 Product Description Sheets........................................................108 6.3.3 Simple Method Description Sheets............................................108 6.3.4 Complex Method Description Sheets ........................................111

Part III: Using the Methodology in Different Domains ................................................................................113 7. Developing Case-Based Help-Desk Support Systems for Complex Technical Equipment ........................................................115 7.1 7.2

7.3

7.4 7.5

7.6

Characteristics of Case-Based Help-Desk Support Systems.................116 Development and Use of Case-Based Help-Desk Support Systems for Complex Technical Equipment .......................................................117 7.2.1 Managerial Processes during System Development ..................117 7.2.2 Organizational Processes during System Development.............119 7.2.3 Technical Processes during System Development.....................121 Using the System ..................................................................................125 7.3.1 Managerial Processes during System Use .................................125 7.3.2 Organizational Processes during System Use............................126 7.3.3 Technical Processes during System Use....................................126 Process Model for a Help-Desk Project ................................................129 Impact of the Methodology in Developing and Using Help-Desk Support Systems....................................................................................133 7.5.1 Impact of the Methodology during Project Definition...............135 7.5.2 Impact of the Methodology during System Development .........135 7.5.3 Impact of the Methodology during System Use ........................136 Conclusion ............................................................................................136

8. Developing Intelligent Catalog Search Applications ...............139 8.1 8.2

8.3

Characteristics of an Intelligent Catalog Search Project .......................140 Vertical Platform for Catalog Search....................................................141 8.2.1 Case Structure and Domain Model ............................................141 8.2.2 Defining Similarity ....................................................................142 8.2.3 Customer Dialogs ......................................................................143 8.2.4 Integration with Product Database.............................................143 8.2.5 Web Interface ............................................................................144 Process Model for Catalog Search ........................................................145 8.3.1 Requirements Acquisition..........................................................145 8.3.2 Case Base Development ............................................................147 8.3.3 GUI Development......................................................................148

Table of Contents

8.4

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8.3.4 Implement CBR Engine.............................................................149 8.3.5 Integrate CBR and GUI .............................................................149 Benefits of the Methodology.................................................................150

9. Developing Maintenance Applications ..........................................153 9.1

9.2

9.3

Characteristics of a Maintenance Application for Complex Technical Equipment.............................................................................................154 9.1.1 Typical Input and Output...........................................................154 9.1.2 Technical Characteristics...........................................................155 Vertical Platform for Maintenance........................................................156 9.2.1 Domain Model: The Hierarchical Approach .............................157 9.2.2 Case Acquisition Process...........................................................158 Impact of the Methodology in a Maintenance Application of Complex Technical Equipment.............................................................160 9.3.1 Productivity for Building a Diagnostic Support Application.....161 9.3.2 Quality of the Results Obtained.................................................161 9.3.3 Improved Communication .........................................................162 9.3.4 Managing Decision Making.......................................................162

10. Developing Experience Factory Applications .............................165 10.1 Introduction...........................................................................................166 10.2 Experience Factory Application-Development Process........................168 10.2.1 Setting the Objectives ................................................................169 10.2.2 Setting the Subject Area ............................................................170 10.2.3 Identification of Scenarios .........................................................173 10.2.4 Development of the Experience Factory Ontology....................175 10.2.5 Definition of Experience Recording ..........................................179 10.2.6 Implementation ..........................................................................183 10.3 Lessons Learned....................................................................................185 10.3.1 A Systematic, Goal-Oriented Approach for Structuring Complex Experience Bases Is a Must ...................................................186 10.3.2 The Process Hierarchy Is a Framework for Organizing Method Libraries...................................................................................186 10.3.3 Further Lessons Learned............................................................187 10.4 Conclusion ...........................................................................................187

A. Compilation of INRECA Methodology Applications ..............189 A.1 A.2 A.3 A.4

ANALOG DEVICES – Sales Support for Integrated Circuits..............189 ANSALDO – Maintaining the Metro in Naples ...................................190 bol.com – Find Books Fast ...................................................................191 DAIMLERCHRYSLER – The Intelligent Hotline ...............................192

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A.5 FM-Ultranet: Fetal Malformation Ultrasonography Training Networking Platform ............................................................................193 A6 Fraunhofer IESE – eParticipation in Software Engineering Projects (indiGo).................................................................................................194 A.7 FREIGHTLINER LLC – Online Knowledge Repository for Truck Diagnostics............................................................................................196 A.8 GICEP ELECTRONIQUE – Diagnostic Help for Circuit Boards Used in Halogen Stove-Tops ................................................................198 A.9 IRSA – Expertise Knowledge-Base ......................................................198 A.10 LEGRAND – Rapid Cost Estimation for Plastic Parts Production .......199 A.11 MET-ERICSSON – Analyzing Telecommunication Cards and Electronic System Test Data .................................................................200 A.12 MÜRITZ ONLINE – Tourist Information on the Internet....................202 A.13 NATIONAL SEMICONDUCTOR – Failure Analysis of Semiconductors.....................................................................................202 A.14 NATIONAL SEMICONDUCTOR – Online Sales Support of Electronic Devices ................................................................................204 A.15 Neckermann – Intelligent Product Assistant.........................................205 A.16 ODENSE STEEL SHIPYARD – Improving Performances of ShipWelding Robots ....................................................................................206 A.17 otto.de – Intelligent Product Search for Internet Shoppers ...................207 A.18 PSA PEUGEOT CITROEN – Improving Vehicle Repair to Enhance Customer Loyalty..................................................................................209 A.19 SAG Knowledge Center – A Comfortable Way of Customer Support .............................................................................210 A.20 SIEMENS – SIMATIC Knowledge Manager.......................................211 A.21 SNECMA SERVICES – Troubleshooting Boeing 737 Engines...........212 A.22 THE COMPENDIUM “PRECISION FROM RHINE-LANDPALATINATE” – Product and Catalog on the Internet .......................213 A.23 VITAC – The Virtual Technical Assistant Center for SIEMENS Employees.............................................................................................213 A.24 WARTSILÄ NSD – Extending Marine Engine Life Cycles and Easing Their Maintenance ....................................................................214 A.25 Worldwide Content Network for Bertelsmann Created with Retrieval Technology ...........................................................................................215

Glossary ............................................................................................................217 References ........................................................................................................223 Index ..................................................................................................................233

Developing Industrial Case Based Reasoning Applications The INRECA Methodology Ralph Bergmann, Klaus-Dieter Althoff, Sean Breen, Mehmet Göker, Michel Manago, Ralph Traphöner, Stefan Wess

References

Aamodt, A. & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI-Communications, 7(1):39–59. Aha, D., & Breslow, L. (1997). Refining conversational case libraries. In: Leake, B. & Plaza, E. (Eds.), Case-Based Reasoning Research and Development (ICCBR-97), Lecture Notes in Artificial Intelligence. Springer, Berlin, Heidelberg. Aha, D., & Weber, R. (Eds.) (2000). Proceedings of the Workshop on Intelligent Lessons Learned Systems at 17th National Conference on AI (AAAI-00). American Association for Artificial Intelligence. Aha, D., Breslow, L. & Munoz-Avila, H. (2001). Conversational case-based reasoning. Applied Intelligence. Akkermans, H., Spiel, P.-H. & Ratcliffe, A. (1999). Problem, opportunity, and feasibility analysis for knowledge management: An industrial case study. In: Proceedings of the Twelfth Workshop on Knowledge Acquisition, Modelling, and Management (KAW ’99), Track on “Knowledge Management and Knowledge Distribution throughout the Internet.” Banff, Canada. URL: http://sern.ucalgary.ca/KAW/KAW99/papers/Akkermans1/kaw99-jma.pdf. Althoff, K.-D. (2001). Case-Based Reasoning. In: Chang, S.K. (Ed.), Handbook on Software Engineering and Knowledge Engineering. Vol. 1 “Fundamentals ,” pp 549–588. World Scientific. Althoff, K.-D., Auriol, E., Barletta, R. & Manago, M. (1995). A Review of Industrial Case-Based Reasoning Tools. AI Perspectives Report, AI Intelligence, Oxford, United Kingdom. Althoff, K.-D., Becker-Kornstaedt, U., Decker, B., Klotz, A., Leopold, E., Rech, J. & Voss, A. (2002). The indiGo Project: Enhancement of Experience Management and Process Learning with Moderated Discourses. In: Perner, P. (Ed.), Data Mining in E-Commerce, Medicine and Knowledge Management, Lecture Notes in Computer Science. Springer.

224

References

Althoff, K.-D., Birk, A., Gresse von Wangenheim, C. & Tautz, C. (1998). Casebased reasoning for experimental software engineering. In: Lenz, M., BartschSpörl, B., Burkhard, H.-D. & Wess, S. (Eds.), Case-Based Reasoning Technology from Foundations to Applications, Lecture Notes in Artificial Intelligence 1400, Chapter 9, pp 235–253. Springer. Althoff, K.-D., Birk, A., Hartkopf, S., Müller, W., Nick, M., Surmann, D. & Tautz, C. (1999). Managing Software Engineering Experience for Comprehensive Reuse. Proceedings of the Eleventh Conference on Software Engineering and Knowledge Engineering, Kaiserslautern, Germany, June 1999. Knowledge Systems Institute, Skokie, Illinois, USA. Althoff, K.-D., Bomarius, F., Müller, W. & Nick, M. (1999a). Using Case-Based Reasoning for Supporting Continuous Improvement Processes in Hospitals. In: Wrobel et al. (1999). Althoff, K.-D., Bomarius, F. & Tautz, C. (2000). Knowledge Management for Building Learning Software Organizations. Information Systems Frontiers 2(3/4):349–367. Kluwer Academic Publishers. Althoff, K.-D., Decker, B., Hartkopf, S., Jedlitschka, A., Nick, M. & Rech, J. (2001). Experience Management: The Fraunhofer IESE Experience Factory. In: Perner, P. (Ed.), Proc. Industrial Conference Data Mining, 24–25 July 2001. Institute for Image Processing and Applied Computer Sciences, Leipzig, Germany. Althoff, K.-D., Nick, M. & Tautz, C. (1999b). Improving organizational memories through user feedback. In: Bomarius, F. (Ed.), Workshop on Learning Software Organisations at SEKE’99, pp 27–44, Kaiserslautern, Germany, June 1999. Fraunhofer IESE, Kaiserslautern, Germany. Althoff, K.-D. & Pfahl, D. (2002). Integrating Experience-Based Knowledge Management with Sustained Competence Development. In: Aurum, A., Jeffery, R., Wohlin, C. & Handzic, M. (Eds.), Managing Software Engineering Knowledge. Springer (forthcoming). Althoff, K.-D. & Wilke, W. (1997). Potential uses of case-based reasoning in the experience-based construction of software systems. In: Bergmann, R. & Wilke, th W. (Eds.), Proceedings of the 5 German Workshop in Case-Based Reasoning (GWCBR’97), LSA-97-01E, Center for Learning Systems and Applications (LSA), University of Kaiserslautern, Germany. Arango, G. (1994). Domain analysis methods. In: Schäfer, W., Prieto-Díaz, R. & Matsumoto, M. (Eds.), Software Reusability, pp 17–49, Ellis Horwood Ltd., New York, USA.

References

225

Bailin, S., Simos, M., Levine, L. & Creps, D. (1996). Learning and inquiry-based reuse adoption (LIBRA): A field guide to reuse adoption through organizational learning, version 1.1. STARS Asset A1098, Lockheed Martin Tactical Defense Systems, February 1996. URL: http://direct.asset.com/. Bartlmae, K. (1999). An Experience Factory Approach for Data Mining. In: Wrobel et al. (1999), pp 5–14. Bartsch-Spörl, B. (1999). Keys of success for CBR applications in practice. In: Proceedings of the Industry Day at ICCBR ’99, Monastery Seeon, Germany, July 1999. Basili, V.R. (1985). Quantitative evaluation of software methodology. In: Proceedings of the First Pan-Pacific Computer Conference, Melbourne, Australia. Basili, V.R., Caldiera, G. & Cantone, G. (1992). A reference architecture for the component factory. ACM Transactions on Software Engineering and Methodology, 1(1). Basili, V.R., Caldiera, G. & Rombach, H.D. (1994). The experience factory. In: Marciniak, J. J. (Ed.), Encyclopedia of Software Engineering, Vol. 1, pp 469– 476. Wiley, New York, USA. Basili, V.R. & Rombach, H.D. (1988). The TAME Project: Towards improvementoriented software environments. IEEE Transactions on Software Engineering SE-14(6):758–773. Basili, V.R. & Rombach, H.D. (1991). Support for comprehensive reuse. IEEE Software Engineering Journal 6(5):303–316. Benjamins, V.R., Fensel, D. & Gómez-Pérez, A. (1998). Knowledge management through ontologies. In: Proceedings of the Second International Conference on Practical Aspects of Knowledge Management (PAKM ’98), Basel, Switzerland. Bergmann, R. (2001). Highlights of the European INRECA projects. In: Aha, D. & Watson, I. (Eds.), Proceedings of the 4th International Conference on CaseBased Reasoning (ICCBR-2001), Lecture Notes in Artificial Intelligence. Springer. Bergmann, R. (2002). Experience Management: Foundations, Development Methodology, and Internet-Based Applications, Lecture Notes in Artificial Intelligence, Vol. 2432. Springer. Bergmann, R. & Göker, M. (1999). Developing industrial case-based reasoning applications using the INRECA methodology. In: Anand, S., Aamodt, A. & Aha, D. (Eds.), IJCAI-99 Workshop ML-5 on the Automatic Construction of Case-Based Reasoners.

226

References

Bergmann, R. & Althoff, K.D. (1998). Methodology for building CBR applications. In: Lenz et al. Bergmann, R., Breen, S., Fayol, E., Göker, M., Manago, M., Schumacher, J., Schmitt, S., Stahl, A., Wess, S. & Wilke, W. (1998). Collecting experience on the systematic development of CBR applications using the INRECA-II Methodology. In: Smyth, B. & Cunningham, P. (Eds.), Advances in Case-Based Reasoning (EWCBR'98), Lecture Notes in Artificial Intelligence, pp 460– 470.Springer, Berlin, Heidelberg. Bergmann, R. & Stahl, A. (1998). Similarity measures for object-oriented case representations. In:Smyth, B. & Cunningham, P. (Eds.), Advances in CaseBased Reasoning (EWCBR’98), Lecture Notes in Artificial Intelligence, pp 25– 36. Springer, Berlin, Heidelberg. Bergmann, R., Wilke, W., Althoff, K.-D., Breen, S. & Johnston, R. (1997). Ingredients for developing a case-based reasoning methodology. In: Bergmann, R. & th Wilke, W. (Eds.), Proceedings of the 5 German Workshop in Case-Based Reasoning (GWCBR’97), LSA-97-01E, Centre for Learning Systems and Applications (LSA), pp 49–58, University of Kaiserslautern, Germany. Bergmann, R., Wilke, W. & Schumacher, J. (1997a). Using software process modeling for building a case-based reasoning methodology. In: Leake, B. & Plaza, E. (Eds.), Case-Based Reasoning Research and Development (ICCBR-97), Lecture Notes in Artificial Intelligence, pp 553–564. Springer, Berlin, Heidelberg. Birk, A. & Tautz, C. (1998). Knowledge Management of Software Engineering Lessons Learned. Proceedings of the Tenth Conference on Software Engineering and Knowledge Engineering, San Francisco Bay, USA. Skokie, Illinois, USA. Knowledge Systems Institute. Blázquez, M., Fernández, M., García-Pinar, J.M. & Gómez-Pérez, A. (1998). Building ontologies at the knowledge level using the ontology design environment. In: Proceedings of the Eleventh Workshop on Knowledge Acquisition, Modelling, and Management (KAW ’98), 1998. URL: http://ksi.cpsc.ucalgary.ca:80/KAW/KAW98/blazquez/. Booch, G. (1994). Object-oriented analysis and design with applications. Second Edition. Benjamin /Cummings Publishing Company. Chandrasekaran, B., Josephson, J.R. & Benjamins, V.R. (1999). What are ontologies, and why do we need them? IEEE Intelligent Systems, 14(1):20–26. Collier, B., DeMarco, T. & Fearey, P. (1996). A Defined Process for Project Postmortem Review. IEEE Software 13(4):65–72.

References

227

Davenport, T.H. & Prusak, L. (1998). Working Knowledge. How Organizations Manage What They Know. Harvard Business School Press, Boston, USA. Dellen, B., Maurer, F., Münch, J. & Verlage, M. (1997). Enriching software process support by knowledge-based techniques. In: Int. Journal of Software Engineering and Knowledge Engineering, 7(2):185–215. Göker, M. & Roth-Berghofer, T. (1999). Workshop on “The Integration of CaseBased Reasoning in Business Processes.” In: Proceedings of the ICCBR ’99 Workshops. Technical Report, LSA-99-03E, Department of Computer Science, Centre for Learning Systems and Applications, University of Kaiserslautern, Germany. Gómez-Pérez, A. (1998). Knowledge sharing and reuse. In: Liebowitz, J. (Ed.), The Handbook of Applied Expert Systems. CRC Press. Gómez-Pérez, A. & Benjamins, V.R. (1999). Overview of knowledge sharing and reuse components: Ontologies and problem-solving methods. In: Benjamins, V. R., Chandrasekaran, B., Gomez-Perez, A., Guarino, N. & Uschold, M. (Eds.), Proceedings of the IJCAI-99 Workshop on Ontologies and Problem-Solving Methods (KRR5), Stockholm, Sweden, August 1999. URL: http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-18/. Gresse von Wangenheim, C., Althoff, K.-D. & Barcia, R.M. (1999). Intelligent retrieval of software engineering experienceware. In: Proceedings of the Eleventh Conference on Software Engineering and Knowledge Engineering, pp128– 135, Kaiserslautern, Germany. Knowledge Systems Institute, Skokie, Illinois, USA. Gresse von Wangenheim, C. & Tautz, C. (1999). Workshop on “Practical CaseBased Reasoning Strategies for Building and Maintaining Corporate Memories.” In: Proceedings of the ICCBR ’99 Workshops, Technical Report, LSA99-03E, Department of Computer Science, Centre for Learning Systems and Applications, University of Kaiserslautern, Germany. Gruber, T.R. (1995). Toward principles for the design of ontologies used for knowledge sharing. International Journal on Human-Computer Studies, 43:907–928. Guarino, N. (1997). Understanding, building, and using ontologies. International Journal on Human-Computer Studies, 46(2/3):293–310. Haley, T.J. (1996). Software process improvement at Raytheon. IEEE Software 13(6):33–41.

228

References

Heister, F. & Wilke, W. (1998). An architecture for maintaining case-based reasoning systems. In: Smyth, B. & Cunningham, P. (Eds.), Advances in CaseBased Reasoning (EWCBR'98), Lecture Notes in Artificial Intelligence pp 221– 232. Springer, Berlin, Heidelberg. Henninger, S. (1995). Developing domain knowledge through the reuse of project experiences. In: Samadzadeh, M. (Ed.), Proc. of the Symposium of Software Reusability (SSR’95), pp 186–195. Houdek, F., Schneider, K. & Wieser, E. (1998). Establishing experience factories at Daimler-Benz: An experience report. Proc. 20th Internat. Conf. on Software Engineering (ICSE’98). Humphrey, W.S., Snyder, T.R. & Willis, R.R. (1991). Software process improvement at Hughes Aircraft. IEEE Software 8, pp 11–23. Jedlitschka, A. & Althoff, K.-D. (2003). Using Case-Based Reasoning for User Modeling in an Experience Management System. Forthcomming. Kitano, H. & Shimazu, H. (1996). The experience-sharing architecture: A case study in corporate-wide case-based software quality control. In: Leake, D. (Ed.), Case-Based Reasoning: Experiences, Lessons, and Future Directions, pp 235–268. AAAI Press/The MIT Press, Menlo Park, California, USA, 1996. Leake, D. (Ed.) (1996). Case-Based Reasoning: Experiences, Lessons, & Future Directions. AAAI-Press. Leake, D. & Wilson, D. (1998). Categorizing case-base maintenance: Dimensions and directions. In: Smyth, B. & Cunningham, P. (Eds.), Advances in CaseBased Reasoning (EWCBR’98), Lecture Notes in Artificial Intelligence, pp 196–207. Springer, Berlin, Heidelberg. Lenz, M., Bartsch-Spörl, B., Burkhardt, H.D. & Wess, S. (Eds.) (1998). CaseBased Reasoning Technology, From Foundations to Applications, Lecture Notes in Artificial Intelligence, Vol. 1400. Springer, Berlin, Heidelberg. Lenz, M., Hübner, A., & Kunze, M. (1998b). Textual CBR. In: Lenz et al. Lenz, M. & Ashley, K. (1998). Proceedings of the AAAI98 Workshop on Textual Case-Based Reasoning. AAAI Press. López, M.F. (1999). Overview of the methodologies for building ontologies. In: Benjamins, V.R., Chandrasekaran, B., Gómez-Pérez, A., Guarino, N. & Uschold, M. (Eds.), Proceedings of the IJCAI-99 Workshop on Ontologies and Problem-Solving Methods (KRR5), Stockholm, Sweden. Manago, M., Bergmann, R., Wess, S. & Traphöner, R. (1994). CASUEL: A Common Case Representation Language — Version 2.0 . ESPRIT-Project INRECA, Deliverable D1.

References

229

McGarry, F. & Pajersky, R. (1990). Towards Understanding Software — 15 Years in the SEL. Proc. of the 15th Annual Software Engineering Workshop, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA, Software Engineering Laboratory Series, SEL-90–006. Neal, M. (1997). Parametric search: Evolving information retrieval for the Web. CADIS Inc. IDM 2( 5). Nick, M., Althoff, K.-D. & Tautz, C. (2001). Systematic Maintenance for Corporate Experience Repositories. Computational Intelligence, 17(2):364–386. Racine, K. & Yang, Q. (1997). Maintaining unstructured case-bases. In: Leake, B. & Plaza, E. (Eds.), Case-Based Reasoning Research and Development (ICCBR-97), Lecture Notes in Artificial Intelligence, pp 553–564. Springer, Berlin, Heidelberg,. Rech, J., Decker, B. & Althoff, K.-D. (2001). Using Knowledge Discovery Techth nology in Experience Management Systems. Proc. 13 Workshop of the German Special Interest Group on Machine Learning, Knowledge Discovery, and Data Mining, Workshop Days of the German Society of Computer Science on “Learning, Teaching, Knowledge, and Adaptivity ,” University of Dortmund, Germany. Rech, J., Althoff, K.-D., Decker, B., Klotz, A., Leopold, E. & Voss, A. (2002). th Thoughts on Text Mining in Organizational Process Learning. Proc. 14 Workshop of the German Special Interest Group on Machine Learning, Knowledge Discovery, and Data Mining, Workshop Days of the German Society of Computer Science on “Learning, Teaching, and Adaptivity,” Learning Lab Lower Saxony (L3S), Hannover. Reinartz, T., Iglezakis, I. & Roth-Berghofer, T. (2001). On quality measures for case-base maintenance. Computational Intelligence 17(2). Richter, M. (1998). Introduction. Chapter 1 in Lenz, M., Bartsch-Spörl, B., Burkhardt, H. D. & Wess, S. (Eds.), Case-Based Reasoning Technology, From Foundations to Applications, Lecture Notes in Artificial Intelligence, Vol. 1400, pp 1–15. Springer, Berlin, Heidelberg. Richter, M. (2000). Personal communication. Rombach, H.D. (1996). New institute for applied software engineering research. Software Process Newsletter No 7, pp 12–14. Rombach, H.D. & Ulery, B.D. (1989). Establishing a measurement-based maintenance improvement program: Lessons learned in the SEL. Proc. of the Conference on Software Maintenance, pp 50–57. IEEE Computer Society Press.

230

References

Rombach, H.D. & Verlage, M. (1995). Directions in software process research. In: Zelkowitz, M. V. (Ed.), Advances in Computers, Vol. 41, pp 1–61. Academic Press. Schreiber, G., Wielinga, B., de Hoog, R., Akkermans, H. & Van de Velde, W. (1994). CommonKADS: A comprehensive methodology for KBS development. IEEE Expert, 6(6):28–37. Seshagiri, G. (1996). Continuous process improvement: Why wait till level 5? Proc. 29th Hawaii Internat. Conf. on System Sciences, pp 681–692. IEEE Computer Society Press. Shimazu, H. (1998). A textual case-based reasoning system using XML on the world-wide web. In: Smyth, B. & Cunningham, P. (Eds.), Advances in CaseBased Reasoning (EWCBR’98), Lecture Notes in Artificial Intelligence. Springer, Berlin, Heidelberg. Simos, M., Creps, D., Klingler, C., Levine, L. & Allemang, D. (1996). Organization Domain Modeling (ODM) guidebook, version 2.0. Informal Technical Report STARS-VC-A025/001/00, Lockheed Martin Tactical Defense Systems, Manassas, Virginia, USA. Smyth, B. & McKenna, E. (1998). Modeling the competence of case bases. In: Smyth, B. & Cunningham, P. (Eds.), Advances in Case-Based Reasoning (EWCBR’98), Lecture Notes in Artificial Intelligence, pp 208–220. Springer, Berlin, Heidelberg. Stolpmann, M. & Wess, S. (1999). Optimierung der Kundenbeziehungen mit CBR systemen-Intelligente Systeme für E-Commerce und Support . Addison Wesley Longmann (Business & Computing), Bonn, Germany. Studer, R. (1999). Knowledge engineering: Survey and future directions. In: Proceedings of the Workshop on Knowledge Management, Organizational Memory and Knowledge Reuse during Expert Systems ’99 (XPS-99), pp 1–23, Würzburg, Germany. Surma, J. & Tyburcy, J. (1998). A Study on competence-preserving case replacing strategies in case-based reasoning. In: Smyth, B. & Cunningham, P. (Eds.), Advances in Case-Based Reasoning (EWCBR’98), Lecture Notes in Artificial Intelligence, pp 233–238. Springer, Berlin, Heidelberg. Tautz, C. (2000). Customizing Software Engineering Experience Management Systems to Organizational Needs. Ph. D. thesis, Department of Computer Science, University of Kaiserslautern, Germany. Fraunhofer IRB Verlag, Stuttgart.

References

231

Tautz, C. & Althoff, K.-D. (1997). Using Case-Based Reasoning for Reusing Software Knowledge. In: Leake, D. & Plaza, E. (Eds.), Case-Based Reasoning Research and Development, Second International Conference on Case-Based Reasoning (ICCBR’97), pp 156–165, Springer. Tautz, C. & Althoff, K.-D. (2000). A Case Study on Engineering Ontologies and Related Processes for Sharing Software Engineering Experience. In: Proc. 12th International Conference on Software Engineering and Knowledge Engineering (SEKE’00). Tautz, C., Althoff, K.-D. & Nick, M. (2000). A Case-Based Reasoning Approach for Managing Qualitative Experience. In: Aha & Weber (2000). Thomas, H., Foil, R. & Dacus, J. (1997). New technology bliss and pain in a large customer service center. In: Leake, B. & Plaza, E. (Eds.), Case-Based Reasoning Research and Development (ICCBR’97), Lecture Notes in Artificial Intelligence, pp 166–177. Springer, Berlin, Heidelberg. Uschold, M. & Gruninger, M. (1996). Ontologies: Principles, methods, and applications. The Knowledge Engineering Review, 11(2):93–136. Vollrath, I., Wilke, W. & Bergmann, R. (1998). Case-based reasoning support for online catalog sales. IEEE Internet Computing, 2 (4):47–54. Wartik, S. & Davis, T. (1999). A phased reuse adoption model. Journal of Systems and Software 46(1):13–23. Weibelzahl, S. & Weber, G. (1999). Benutzermodellierung von Kundenwünschen durch Fallbasiertes Schließen. In: Wrobel et al. (1999), pp 295–300. Wiig, K.M. (1995). Knowledge Management Methods: Practical Approaches to Managing Knowledge. Schema Press, Arlington, Texas, USA. Wilson, D. & Leake, D. (2001). Maintaining case-based reasoners: Dimensions and Directions. Computational Intelligence 17(2). th

Wrobel, S., Henze, N. & Perner, P. (Eds.) (1999). Proc. 11 Workshop of the German Special Interest Group on Machine Learning, Knowledge Discovery, and Data Mining, Workshop Days of the German Society of Computer Science on “Learning, Knowledge Discovery, and Adaptivity,” University of Magdeburg.

Part III Using the Methodology in Different Domains

Part III, which consists of the chapter 7 through 10, shows, for four major application domains, how to develop CBR applications according to the INRECA methodology. Building on the foundations laid in part II of the book, lessons learned and reusable software process models are presented in detail. Chapter 7 “Developing Case-Based Help-Desk Support Systems for Complex Technical Equipment” describes the development process for CBR applications in the area of complex help-desks. Chapter 8 “Developing Intelligent Catalog Search Applications” explains how to develop sales support applications. Chapter 9 “Developing Maintenance Applications” focuses on the development process for maintenance of technical equipment. Chapter 10 “Developing Experience Factory Applications” shows the development process for experience factory applications for software development. In each of these chapters, the impact of applying the INRECA methodology is evaluated.

Part II Developing Case-Based Application with the INRECA Methodology

This second part of the book, which consists of the chapter 4 through 6, explains in detail the core of the INRECA methodology. We explain how a CBR system is developed and how development experience is captured, documented, and reused to enable effective and successful CBR projects. Chapter 4 “Practical Guidelines for Developing Case-Based Reasoning Applications” introduces the general steps that occur in almost every CBR project. It focuses on practical managerial aspects. Chapter 5 “Professional Case-Based Reasoning Application Development” introduces the foundations of the INRECA methodology, which are essential for cost-efficient CBR application development. Chapter 6 “Documenting Case-Based Reasoning Development Experience” describes in detail how to use software process models to structure and document CBR development projects.

Part I Smarter Business with Case-Based Decision Support

This first part of the book, which consists of the chapter 1 through 3, shows how captured experience stored as a set of cases enables smarter business in different areas such as help-desk support, technical diagnosis and maintenance, intelligent sales support, and software development. Chapter 1 “Making Smarter Business Decisions in Less Time” gives a brief introduction to Case-Based Reasoning and positions it with respect to related technologies, such as databases, statistics, and expert systems. Chapter 2 “Case-Based Reasoning Approaches” introduces and compares three different CBR approaches that are implemented in current commercial CBR software: the textual CBR approach, the conversational CBR approach, and the structural CBR approach approach. Chapter 3 “Selected Applications of the Structural Case-Based Reasoning Approach” describes three applications developed using the INRECA methodology: The case-based help-desk support system HOMER, the Analog Devices sales support application for operational amplifiers, the case-based maintenance application for the high-speed train TGV, and the Fraunhofer IESE experience factory.

List of Tables

2.1a. Comparison of the different CBR approaches.............................................29 2.1b. Comparison of the different CBR approaches.............................................30 2.2. Comparison of the efforts required for the different approaches.................32 7.1. 7.2.

Processes during case-based help-desk support system development and use.......................................................................................................117 Sample form for initial case acquisition ....................................................124

10.1. 10.2. 10.3. 10.4.

Evaluation of the selected subject area......................................................172 Subprocesses of “define knowledge collection” and their objectives........181 Results of performing the subprocess “define knowledge collection” ......182 “Record” subprocesses for lessons learned ...............................................183

List of Figures

1.1. 1.2. 1.3. 1.4.

Inexperienced car mechanic ........................................................................12 Experienced car mechanic remembering cases ...........................................12 Car mechanics learning new cases (cartoons by Yurdagün Göker).............13 Positioning case-based decision support and alternative techniques ...........16

2.1. 2.2. 2.3. 2.4. 2.5. 2.6.

User interface for the SIMATIC textual CBR system (see Annex A).........23 User interface of a conversational CBR system ..........................................25 An object-oriented domain model for sales support of PCs ........................26 Consultation screen from a structural CBR system .....................................28 Comparison of efforts for the different CBR approaches............................33 The CBR cycle according to Aamodt and Plaza (1994) ..............................34

3.1. 3.2. 3.3. 3.4. 3.5. 3.6. 3.7. 3.8. 3.9. 3.10. 3.11. 3.12. 3.13. 3.14.

Support layers at the CAD/CAM help-desk ................................................36 Software tools used by the help-desk operators...........................................37 Knowledge acquisition and transfer ............................................................38 Basic structure of a help-desk case..............................................................39 Domain modeling ........................................................................................41 The help-desk tool .......................................................................................42 System architecture .....................................................................................43 “Less is better” similarity graph ..................................................................50 Query screen for Op Amps product search (DYNAMIC) ...........................51 Query screen for Op Amps product search (OTHER).................................51 Results display (DYNAMIC) ......................................................................52 Results display (OTHER)............................................................................53 Results held from an individual search........................................................54 Subpart of the model hierarchy for a complex equipment “Chain of Traction”......................................................................................................56 Submodel CHQQT43 ..................................................................................57 Data transferred from the TGV for on-board network analysis...................59 Communication between the diagnostic tool and the databases ..................60 Excerpt of a case base for an exemplary guideline......................................66 Collecting cases through project analysis....................................................67 Splitting the project analysis report into cases.............................................67

3.15. 3.16. 3.17. 3.18. 3.19. 3.20.

XVI

List of Figures

5.1. 5.2. 5.3. 5.4. 5.5. 5.6.

The experience factory approach (adapted from Basili et al., 1994) ...........89 The quality improvement paradigm (QIP) ..................................................90 Basic elements of a process model ..............................................................91 Structure of the experience base..................................................................93 How to use the experience base...................................................................95 Adding new experience ...............................................................................97

6.1. 6.2. 6.3. 6.4. 6.5. 6.6. 6.7. 6.8.

Interaction among processes......................................................................102 Two alternative complex methods.............................................................102 Product flow for the complex method “Prototype Building” ....................103 Road map of description sheets .................................................................105 Example process description sheet ............................................................107 Example product description sheet............................................................109 Example simple method description sheet ................................................110 Example complex method description sheet .............................................111

7.1. 7.2. 7.3. 7.4. 7.5. 7.6. 7.7. 7.8.

Basic structure of a help-desk case............................................................125 Utilization of the case-based help-desk support system ............................127 Project planning and initialization .............................................................130 Implementation of a rapid prototype .........................................................131 Evaluation and revision of the prototype...................................................131 Implementation of the integrated case-based help-desk support system ...132 Evaluation and revision of the case-based help-desk support system .......133 Utilization of the Case-Based Help-Desk Support System........................134

8.1. 8.2. 8.3. 8.4. 8.5. 8.6. 8.7. 8.8.

Creating a specialized type ........................................................................143 IRSA search results dialog on the Web .....................................................144 Process model for catalog search application ............................................145 Process decomposition for requirements acquisition.................................146 Case base development process decomposition ........................................148 Graphical user interface development process ..........................................149 Implement CBR search engine process .....................................................150 Integrate CBR and GUI process ................................................................151

9.1. 9.2. 9.3.

The decomposition of a complex piece of equipment ...............................156 Process model for building the hierarchical models ..................................158 Process model for case acquisition ............................................................160

10.1. 10.2. 10.3. 10.4. 10.5. 10.6.

Subprocesses of “design experience base” ................................................168 Subprocesses of set objectives...................................................................169 Subprocesses of “set subject areas”...........................................................170 Subprocesses of “identify scenarios”.........................................................173 Reuse scenario “find solution” ..................................................................174 Retrieval goal of scenario “find solution” .................................................174

XVII 10.7. 10.8. 10.9. 10.10.

Subprocesses of “conceptualize”...............................................................176 Subprocesses of “record” ..........................................................................179 Subprocesses of “define record”................................................................180 Subprocesses of “infuse experience base”.................................................184

List of Contributors

Klaus-Dieter Althoff Fraunhofer Institute for Experimental Software Engineering Sauerwiesen 6 67661 Kaiserslautern – Germany

Sean Breen CP Ltd Abhainn Beag Ballinaclash Co. Wicklow – Ireland

[email protected] http://www.iese.fhg.de/Staff/althoff/

[email protected]

Eric Auriol Kaidara 15 rue Soufflot 75005 Paris – France

Mehmet H. Göker Kaidara 330 Distel Circle, Suite 150 Los Altos, CA 94022 – USA

[email protected] www.kaidara.com


Ralph Bergmann University of Hildesheim Data- and Knowledge Management Group PO Box 101363 31113 Hildesheim – Germany

Roy Johnston IMS Clara House Glenegeary Park Co. Dublin – Ireland

[email protected] www.dwm.uni-hildesheim.de

[email protected] www.imsmaxims.com

XXII

List of Contributors

Michel Manago Kaidara 15 rue Soufflot 75005 Paris – France [email protected] www kaidara.com

Sascha Schmitt University of Kaiserslautern Department of Computer Science P.O. Box 3049 67653 Kaiserslautern – Germany [email protected] wwwagr.informatik.uni-kl.de/~sschmitt

Bénédicte Minguy Kaidara 15 rue Soufflot 75005 Paris – France

Ralph Traphöner empolis GmbH Europaallee 10 67657 Kaiserslautern – Germany


[email protected] www.empolis.com

Michael Richter University of Kaiserslautern Department of Computer Science P.O. Box 3049 67653 Kaiserslautern – Germany

Stefan Wess empolis GmbH Europaallee 10 67657 Kaiserslautern – Germany

[email protected] wwwagr.informatik.uni-kl.de/~richter

Thomas Roth-Berghofer Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) GmbH Erwin-Schrödinger-Str. 57 67663 Kaiserslautern – Germany

[email protected] www.empolis.com

Wolfgang Wilke Avinci AG Halskestr. 38 40880 Ratingen [email protected]

[email protected] www.dfki.de

Glossary

Adaptation. Adjusting a retrieved case to fit the given problem situation by applying adaptation knowledge. Adaptation Knowledge. Adaptation knowledge is the expert domain knowledge about how to adapt a retrieved case from the case base so that it is better suited to the current problem. Adaptation knowledge is not mandatory for CBR applications but can be optionally used to improve the retrieved solutions. Agents. In the INRECA methodology, agents are humans who enact a method in order to perform a certain process. Attribute-Value Pair. A case can be represented as a flat set of attribute-value pairs where each pair encodes the value of a certain attribute for that case. Awareness. The success of the project depends on all prospective users being made aware both of its implications and of the possibility that “there is something in it for them,” thus generating motivation. Case. A case represents one piece of reusable experience. Typically, a case consists of two parts: a problem description and a solution. Case Base. This is the collection of all available cases. Case Base Administrator. She/he has all rights over the system, including the right to update the domain model. Updating should be done occasionally as new experiences arise. Case-Based Reasoning. Case-Based Reasoning (CBR) tries to model the acting by experience. It maintains a memory of experiences (case base) and solves new problems by retrieving similar cases from the cases. Case-Based Reasoning Cycle. The CBR cycle describes the basic steps involved in case-based problem solving: retrieve, reuse, revise, and retain. Case Buffer. This is a temporary location where current cases are stored pending a decision about whether or not to edit them into the case base.

218

Glossary

CASUEL. CASUEL (Manago et al. 1994) is the object-oriented case representation language developed by INRECA. It allows the developer to define domain models, cases, similarity measures, and adaptation knowledge. Common Generic Level. This is a level in the experience base of the INRECA methodology at which processes, products, and methods are collected that are common for a very large spectrum of different CBR applications. Conversational CBR Approach. An approach to CBR in which a case is represented as a flat list of questions and answers, and the list of questions is different for every case; there is no domain model. Cookbook Level. This is a level in the experience base of the INRECA methodology at which processes, products, and methods are tailored for a particular class of applications (e.g., help-desk, technical maintenance, product catalog). For each application class, the cookbook level contains a recipe (see below). Corporate Memory. When corporate knowledge becomes easily retrievable for decision support, one speaks of a “corporate memory.” CQL. CQL is the case query language that is based on CASUEL. It is used to formulate queries to a CBR retrieval engine. Decision Tree. This is one form of the output of an “induction engine.” It displays successive partitions of a case base into subsets differentiated from each other by the values of parameters. The first parameter selected for the subdivision is the one generating the greatest information gain as regards factors leading to the target outcome. The subdivision process is then repeated for all other parameters within each branch. See also fault tree. Deployment. When a system has been developed to the satisfaction of the users, it is usual to generate a “runtime version” of the software, in which all changes made to date are embedded robustly, and system users can use it without help from the developers. The system is then said to be “deployed.” Domain Model. In the structural CBR approach, the domain model is a set of attributes, with either defined sets of symbolic values or defined ranges of numerical values sufficient to characterize each unit of knowledge in the knowledge domain. Each case is represented using the attributes, each of which is given one of the allowed values. Experience Base. The experience base is the collection of software development experience within the experience factory. Experience Factory. An experience factory is a logical and/or physical organization that supports project development by analyzing and synthesizing all kinds of experience, by acting as a repository for such experience, and by

Glossary

219

supplying that experience to various projects on demand. An experience factory packages experience by building informal, formal, or schematized models. Fat Client. When the CBR system is working in client-server mode, the masterversion of the system resides on the server. However, for the system to work at an acceptable level of performance, a client with lots of computation capabilities might be necessary. This is called a “fat client,” since it could require significant time to download to the client machine. Fault Tree. A fault tree is a decision tree where the branches are based on the evidence leading to the diagnosis of a fault. GUI (Graphical User Interface). The graphical user interface is usually customized interactively with the user to meet the user’s needs. The information should be presented without overwhelming the user. Hierarchical Model. Hierarchical models breakdown a complex system into a hierarchy of submodels, each of which has its own case base. A rule of thumb for the scale of a submodel is that it should have about 15 or so attributes. Induction. Induction is the generation of rules or decision trees for achieving a desired outcome. The rules or decision trees are generated automatically from the analysis of cases in a case base. This induction process abstracts from the experience of many expert decisions stored as cases in the case base. Initial Domain. This is a subset of the total target domain that is used in initial trials of the CBR approach. It should be selected so that positively perceived results are obtained rapidly, thus creating awareness. Integration. Integration means bringing together the various parts of the CBR application: The search engine, GUIs, case base, related database, and so on. Knowledge Base. Knowledge base is a generic word for an assembly of chunks of formally represented, distilled knowledge, some or all of which may be in the form of cases. Knowledge Container. Knowledge container model, introduced by Michael Richter (see Richter 1998), describes the knowledge that a CBR system uses. The containers are the vocabulary, i.e., the domain model, the case base, the similarity measure, and the adaptation knowledge. In principle, each container could be used to represent most of the knowledge, but for efficient application development it must be carefully decided which knowledge to put into which container. Managerial Process. The primary goal of managerial processes is to provide an environment and services so that software that meets the product requirements and project goals can be developed. The services enact the technical and the organizational processes.

220

Glossary

Method. A method is a particular way of achieving a specific goal. A method can be simple or complex. In the latter case, it can embody a number of subprocesses and intermediate products. Methodology. A methodology is a collection of methods and guidelines that enables a person to work effectively and efficiently in the domain for which the methodology has been developed. Nearest Neighbor Retrieval. This is a search approach that selects experience based on some geometrical distance computed in the attribute space. The search engine evaluates the n-dimensional “distance” between the query and all cases in the case base, taking into account the weights. The results are presented in order of n-dimensional “proximity.” Organizational Process. Organizational processes cover those parts of the business process that need to be changed in order to make best use of a new software system. They address those parts of the user organization’s business process in which the software system will be embedded. New processes have to be introduced into an existing business process, such as the training of end-users or the technical maintenance of the system. Existing processes may need to be changed or reorganized in order to make best use of the new software system. Precision. This is the proportion of retrieved cases that turn out to be relevant to a user who needs specific knowledge. Process. A process is an activity that has the goal of transforming some input product(s) into some output product(s). It is a clearly defined step in a development project. Process Input Product. A process input product is a product that is consumed by a process in order to generate the desired output product. Process Model. Process models identify and document the processes, products, and methods in a clear and understandable way. In the INRECA methodology, process models are used to document the experience stored in cookbook-level recipes and on the common generic level. Product. A product is an object that is either consumed as input, modified, or created as output of a process. A “modified product” is a product that is changed during the enactment of a process. Typically, it existed before the process was executed. An output product is a product that is created as a result of the enactment of a process. This product did not exist before the process was executed. Project Plan. The project plan is the temporal and logical sequence of processes that have to be executed and the products that have to be consumed, modified, and created to achieve the goal of the overall project.

Glossary

221

Recall. This is the proportion of relevant cases from the cases base (in the context of the user’s current knowledge need) that where retrieved by the retrieval engine. Recipe. A recipe describes the processes used to build a CBR application for a particular application class. All recipes are collected in the cookbook level of the experience base of the INRECA methodology. Resources. Resources are objects that might be required to achieve a project goal. They can be financial, temporal, or material, as well as human resources. Retain. The retain phase is the fourth step in the CBR cycle. Retain means storing new experience in the case base. Retrieval. The retrieval phase is the first step in the CBR cycle. Retrieval means selecting a relevant case from the case base. There are different techniques for retrieval, like traversing an induction tree or nearest neighbor retrieval. Retrieval Engine. This is a software component that performs the retrieval, i.e., it selects a case from the case base. Reuse. The reuse phase is the second step in the CBR cycle. Reuse is a synonym for “adaptation.” It means modifying the retrieved case to fit the given problem situation. Revise. The revise phase is the third step in the CBR cycle. During revision the proposed solution case is applied and evaluated in the business environment. If necessary, the proposed solution can be improved. Second-Level Support. People in a company who have a greater depth of expertise than those in the “front office,” who possess routine knowledge. Frontoffice people may have to refer problems to these experts. Similarity Measure. A similarity measure is a computational function that computes the similarity between a case and a query. The similarity measure contains expert knowledge that evaluates whether a case contains information that is reusable in the current context defined by the query. Software Process Modeling. Software process modeling defines what processes must be enacted and what products must consumed, modified, or created within a software project. Specific Project Level. The specific project level describes experience in the context of a single, particular project. It contains project-specific information, such as a description of the particular processes that were carried out. Structural CBR Approach. This is a CBR approach that relies on cases that are described with a set of predefined attributes. These attributes are described in a domain model.

222

Glossary

Technical Process. Technical processes transform product information from the problem description to the final (software) system. They cover the development of the system and the required documentation itself. Textual CBR Approach. In this CBR approach, cases are represented in free-text form. Keyword matching techniques are used for retrieval. There is no domain model. Tool. A tool is a piece of software, or a hardware-software combination, used by an agent to enact a process according to a method. Vertical Platform. A vertical platform is a collection of several preconfigured software components together with development guidelines (a recipe on the cookbook level) particularly tailored for a restricted application domain, such as a help-desk. It is often implemented using a general purpose CBR engine but incorporates domain-specific modifications.

Acknowledgments

This book would never have been written without the contributions of several persons and organizations. The idea of the book came during the course of the ESPRIT IV project INRECA II (P22196), which is a successor of the ESPRIT III project INRECA I (P6322). We are, therefore, indebted to the Commission of European Communities Directorate General XIII and in particular to our projects officers Patrick Corsi and Brice Lepape. We would also like to thank the reviewers of the INRECA projects who have always pushed us in positive ways for improved results: Agnar Aamodt (University of Trondheim, Norway), Patricia Arundel (PMA Associates), Rick Magaldi (British Airways, England), Robin Muire (RollsRoyce plc, England), and last but not least Gerhard Strube (University of Freiburg, Germany). Because of the number of people involved in the writing of this book, it was unfortunately not possible to mention all the authors on the front cover, and we decided to include only one main author per organization involved in INRECA. The following is a list of additional authors for the current book: Eric Auriol, Roy Johnston, Bénédicte Minguy, Sascha Schmitt, and Thomas Roth-Berghofer. The following persons have helped by contributing background material for the book: Brigitte Bartsch-Spörl, Gaddo Benedetti, Martin Bräuer, Michael Carmody, Laurent Champion, Steven Clinton, Christophe Deniard, Stefan Dittrich, Derek Ennis, Nicola Faull, Sylvie Garry, Arlette Gaulène, Yurdagün Göker, Elena Levandowky, Gholamreza Nakhaeizadeh, Póol Macanultaigh, Thomas Pantleon, Carsten Priebisch, Michael M. Richter, Thomas Roth-Berghofer, Arnaud Schleich, Jürgen Schumacher, Reinhard Skuppin, Armin Stahl, Jutta Stehr, Markus Stopmann, Emmanuelle Tartarin-Fayol, and Wolfgang Wilke. For their important contributions, we offer our deepest thanks. We also thank Susan Branting for the great job she did on proofreading and editing the manuscript of this book within an extremely short period of time. She significantly helped to improve the readability, language, and spelling of the whole text.

About This Book

Scope of the Book With this book you can learn a proven method for solving business problems. And you’ll come to understand why this methodology is the right solution for you. It addresses issues that are important for both software managers and technical staff. These issues will be particularly useful before the start or the set-up of an investment in building a case-based application. This book aims to increase: ─

case-based decision-support software quality, case data quality, ─ process productivity for developing case-based applications. ─

It incorporates the results of 10 years of joint international research and development made by the leading specialists from industry and academia.

Who Should Read This Book This book is targeted at everyone who has an interest in practical issues about developing case-based applications, in particular integrators, and to everyone who wants to increase her/his understanding of the process and requirements for building such solutions. In particular, this book is addressed to: ─ ─ ─ ─ ─

consultants or integrators who want to develop case-based applications, end-users or prospective end-users of case-based technologies, IT managers, QA personnel, MIS or software engineering executives.

2

About this Book

The INRECA Consortium The methodology described in these pages was developed by INRECA, a European consortium created in 1992 by three companies and a university: ─

AcknoSoft, a French company specializing in data mining, tec:inno, a German company specializing in case-based reasoning (CBR), ─ Interactive Multimedia Systems, an Irish software development company, ─ University of Kaiserslautern in Germany, where a group headed by Prof. Dr. Michael M. Richter forms a renowned international research center with expertise in CBR. ─

The INRECA consortium received support from the European Commission through the ESPRIT program. The consortium’s goals were to develop innovative technologies to help people make smarter business decisions more quickly by using cases, and to integrate these technologies into a single software platform that would allow the technologies to be used more widely. By case, we mean a record of a particular past experience that can be reused in the future. For example, in a technical support application, a case is the description of a problem and its solution. In a sales-support application, a case is the description of the characteristics of a product and a reference to that product. These goals were achieved in late 1995: the consortium was successful both in developing and integrating technologies, and in demonstrating their usefulness for a large-scale industrial application (diagnosing Boeing 737 engines). In 1996, the INRECA consortium was expanded to include the multi-national company DaimlerChrysler, a large user of INRECA's technology. The emphasis shifted from technology to methodology, because case-based decision-support systems require quality cases that must be maintained over time. In 1999 the INRECA consortium published the first edition of this book, which was widely accepted and applied to implementing various successful case-based reasoning applications by the industrial partners of the INRECA consortium and others. Since then, the market for CBR applications has grown even larger and with it the companies involved. As a consequence, some of the companies have changed their business structures and names: ─

AcknoSoft is now called Kaidara ─ tec:inno is now called empolis Because the first printing of this book was so successful, we have revised and extended it, primarily by including an additional application area — experience factory applications. This allows us to illustrate a particular instance of software development, thus demonstrating the importance of CBR in knowledge management and sharing.

Contents of the Book

3

Why a Methodology? Case-based solutions are not ready-made solutions. A development process is necessary in order to acquire an appropriate set of cases and to customize the system. The latter includes customizing the user interface, as well as the way the system retrieves and processes cases afterwards. The resulting cases may be shown to the user in different ways, and/or the retrieved cases may be adapted. For example, in a cost-estimation application, a quotation that was made three years ago must be updated to take inflation into account. The developers of case-based solutions must master the development process. If the cases are of low quality, because they are poorly represented inside the computer, contain errors, or are missing information, then the quality of the decisions proposed also will be low. When the INRECA consortium started to address methodological issues, building a case-based solution was mostly an art mastered by only a few. Now that the INRECA methodology is available, building a case-based solution is an industrial process that enables: 1. developing case-based applications in a consistent manner, 2. building an experience repository about how to develop case-based applications in order to more quickly reuse this experience for similar projects and to more accurately estimate the efforts that will be required, 3. documenting the development in a standardized way and producing results that are in line with existing quality standards, such as ISO 9000 or SPICE.

Contents of the Book This book contains three parts: Part I “Smarter Business with Case-Based Decision Support,” Part II “Developing Case-Based Application with the INRECA Methodology,” Part III “Using the Methodology in Different Domains.” Part I: Smarter Business with Case-Based Decision Support. Chapters 1 through 3 show how captured experience stored as a set of cases improves business in different areas, such as help-desk applications, technical diagnosis and maintenance, and intelligent sales support. Chapter 1 gives a brief introduction to case-based reasoning and positions it with respect to related technologies, such as databases, statistics, and expert systems. Chapter 2 introduces and compares three different CBR approaches that are implemented in current commercial CBR software: the textual CBR approach, the conversational CBR approach, and the structural CBR approach. Chapter 3 describes four applications of the structural CBR approach developed using the INRECA methodology: The case-based helpdesk support system HOMER, the Analog Devices sales support application for

4

About this Book

operational amplifiers, the case-based maintenance application for the high-speed train TGV, and the Fraunhofer IESE experience factory. Part II: Developing Case-Based Applications with the INRECA Methodology. Chapters 4 through 6 explain in detail the core of the INRECA methodology. We explain how CBR development experience is captured, documented, and reused to enable effective and successful CBR projects. Chapter 4 presents practical guidelines for CBR application development, introducing the general steps that occur in almost every CBR project. Chapter 5 introduces the foundations of the INRECA methodology, which are essential for professional CBR application development. Chapters 6 describes in detail how to use software process models to structure and document CBR development projects. Part III: Using the Methodology in Different Domains. Chapters 7 through 10 describe how to develop CBR applications according to the INRECA methodology for four major application areas. Building on the foundations laid in part II of the book, lessons learned and reusable software process models are presented in detail. Chapter 7 addresses the application area of case-based help-desk support systems for complex equipment. Chapter 8 deals with developing intelligent catalog search applications. Chapter 9 shows how to develop maintenance applications for technical equipment. Chapter 10 demonstrates how to develop experience factory applications. Further, for these four application areas, the impact of applying the INRECA methodology is evaluated. Finally, Annex A presents a compilation of success stories of CBR applications developed according to the INRECA methodology.

1. Making Smarter Business Decisions in Less Time

A case-based information system helps to exploit data so that smarter business decisions can be made in less time and/or at lower cost. This is particularly important for customer relationship management (CRM). Customers need knowledge about products and often require help finding the best products to fit their needs. Once they have purchased a product, they often need technical support. Case-based decision support is uniquely able to help companies learn from experience, manage their corporate knowledge as a valuable asset, and support customers both in pre- and after-sales activities. Case-based decision support encompass both case-based reasoning (CBR) technology, which retrieves cases that are similar to a query, and data mining in order to analyze past queries to extract general patterns and rules. Consequently, case-based decision support plays a role both for operational CRM (front office) to help fulfill a short term customer request and for analytical CRM to extract knowledge about customers, enhancing a company’s understanding of their needs, behavior, and long-term expectations. This is increasingly important to corporations as they seek to establish long-term relationships with their customers.

R. Bergmann et al. (Eds.): INRECA-Methodology 2nd Edition, LNAI 1612, pp. 9–20, 2003. Springer-Verlag Berlin Heidelberg 2003

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1.1 Why Case-Based Decision Support? Thanks to modern electronic systems, the amount of information available in various forms has grown exponentially. On a $1,000 laptop computer, you can subscribe to information sources across multiple corporate data stores: electronic documents, faxes, emails, databases, data warehouses, CD-ROMs and Web servers. Not only might you feel overwhelmed, but being able to access the right piece of information at the right time is a major issue. We often hear users complain that their corporate databases work predominantly in write-only mode. Yet, this mass of data could reveal strategic knowledge: the marketing department wants to discover trends in buyer behavior, the customer support division must work more efficiently so that the company retains its customers, the financial department wants to more accurately assess risks, and the company as a whole must improve quality management and control. Going from data to decisions is not easy, however. A case-based information system helps exploit data so that smarter business decisions can be made in less time and/or at lower cost. This is particularly important for customer relationship management (CRM). Case-based solutions increasingly play a key role in customer service, sales automation, and even marketing automation (that is, in the front office). We also believe that case-based decision support can play a major role in analytical CRM, bringing value-added business domain knowledge on top of customer data marketsmarts. This is increasingly important to corporations as they seek to establish long-term relationships with customers. According to the Meta Group, “Companies have an opportunity to create a new class of applications based on collaboration components such as human expertise tracking, competitive intelligence, and product innovation. Collaboration will also be a major element in customer relationship management initiatives.” We believe that case-based decision support is strategic to achieving collaboration so that case knowledge can be preserved, even after the decision maker has left the company, ─ employees of the company can share their experiences as documented cases, and ─ case knowledge can be transferred from skilled experts to novices. ─

Case-based decision support is a broad area encompassing the front office flow and anything that exists to improve the relationship with both the customer and customer service. A case-based information system is best viewed as a set of processes that continually listens to and extract knowledge about customers, enhancing a company's understanding of their needs, expectations, and behavior, and thus enabling the company to dynamically respond to opportunities. The scope is vast because customer support covers both internal and external customers, as well as marketing, sales, post-sales, and, ultimately, quality-management functions.

1.2 What Is Case-Based Decision Support?

11

Case-based decision support has come along at just the right time. Customers need knowledge about products and often require help finding the best products to fit their needs. And once they have purchased a product, they need support. Casebased decision support is uniquely able to help companies learn from experience, manage their corporate knowledge as a valuable asset, and support customers both in pre- and post-sales activities. Early adopters of case-based decision support solutions, such as Analog Devices, Legrand, and Siemens, have empowered their customers to make better-informed product choices while providing them with efficient technical support tailored to their needs.

1.2 What Is Case-Based Decision Support? Case-based decision support is both a methodology that models human reasoning and thinking, and a methodology for building intelligent computer systems. Cases are stored in memory, and the case-based decision support system analyzes them to retrieve similar cases from memory for decision making. This is the principle underlying case-based reasoning technologies. Case-based decision support can also analyze cases to extract patterns and discover knowledge hidden in data. This is the principle underlying data mining technologies such as induction. 1.2.1 Case-Based Reasoning Consider a simple example of CBR1 that deals with car diagnostics. A case stored in the case base is a fault that has been solved in the past. The case description is made of client effects, such as observed symptoms (e.g., engine does not start) and context parameters (e.g., ignition key is turned on). It can also include measured parameters (e.g., state of the electronic control units as obtained using testing equipment). The solution is the maintenance operation. With CBR, you can make use of the experience captured in this case base to solve new diagnostic problems. If you encounter a new, unsolved diagnostic problem, a past case that is similar to your new problem will very likely contain an appropriate maintenance operation.

1

For technical and research-oriented background information about CBR consult one of the following books: Leake (1996), Lenz et al. (1998), Stolpman & Wess (1999), Bergmann (2002).

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Fig. 1.1. Inexperienced car mechanic.

How does this process relate to human reasoning? When confronted with a new problem, a technician with no or little experience may attempt to analyze the problem using a Fault Isolation Manual, if there is one and if this is not an overly time-consuming task. He might also try to find the source of the problem by himself, in which case he may end up changing the wrong parts. Finally, he might try ask for help, either by calling the car manufacturer’s technical support center or by asking a more experienced colleague.

Fig. 1.2. Experienced car mechanic remembering cases.

A more-experienced mechanic can recall past cases he has solved. His intuitive thinking process is, “Have I ever seen a similar problem before? If so, what did I do to solve it?” If the more-experienced mechanic can find the solution and fix the car, his less-experienced colleague will learn from this new experience and build up his own memory of solved cases. This human ability to learn is a key to human intelligence and reasoning.

1.2 What Is Case-Based Decision Support?

13

If the experiences of its employees is indeed a valuable asset to a company, it makes sense to try to capture this experience and store it in a such a way that it can be reused in the future and shared among the company's individuals.

Fig. 1.3. Car mechanics learning new cases (cartoons by Yurdagün Göker).

The principles of CBR are the same. Cases are captured and stored in a way that they can be reused. CBR retrieves the appropriate cases when they are needed. These are presented to the user so that he can decide whether the solution that worked in the past will solve the new problem. 1.2.2 Knowledge Discovery and Data Mining Consider a simple example of knowledge discovery. Once a person has accumulated a large number of similar cases, he starts making generalizations. When he sees a new instance of the same case, he no longer needs to recall previous ones. He just knows that for a certain pattern of faults, this is the solution that will work. For example, when he turns on the ignition key and there is no light on the control panel, he knows that it is because there is no power and that the battery is probably dead. This ability to learn from experience by extracting general laws is also a key to human intelligence. Certain data mining techniques, in particular induction, aim at discovering general knowledge that is hidden in data. Induction goes from the specific to the general, deduction from the general to the specific. In deduction, there are some general rules, such as “all humans are mortal.” From a specific fact, such as “Socrates is a human,” the new fact, “Socrates is mortal,” is deduced. Induction is the reverse process. If we know that “Socrates is mortal and Socrates is a human,” “Stefan is mortal and Stefan is a human,” “Ralph is mortal and Ralph is a human,” “Elena is mortal and Elena is a human,” etc., the general rule “all humans are mortal” can be induced, and we produce a generalization of the data.

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The benefit of having learned such general knowledge after analyzing cases is that the knowledge can be used to solve new problems more quickly. It might also help discover patterns in unwanted events. For instance, we may have an unusually high rate of problems with a certain pattern for a car with air conditioning. This information can be fed back to customer service so that a service bulletin can be issued to warn all mechanics about the problem. It also might be sent to the R&D department so R&D can make a design change to the vehicle that will prevent a reoccurrence of the problem. These patterns might also be used to make predictions and detect problems early (prognostic).

1.3 Successful Applications of Case-Based Decision Support Case-based decision support is being used daily to help make better business decisions in a variety of industries, such as automotive, manufacturing, electronics, computers, telecommunications, petroleum, transportation, banking, insurance, and health care, as well as in biology and environmental protection. The following sections give examples of domains for case-based decision support. 1.3.1 Management of Industrial Knowledge Knowledge about manufacturing processes and quality management is disseminated across various departments and databases: marketing, design, production, after-sales, etc. A case-based decision support system capitalizes on this know-how and helps preserve manufacturing knowledge, reducing productdevelopment cycles and improving quality. 1.3.2 Sales Support Selling, estimating production costs, making a quotation for products that are made on demand, or doing market analysis are all activities that benefit from case-based decision support. Companies can develop sales catalogs that feature only products that best match customer needs and that are ranked according to how well they match earlier patterns of buying. Such catalogs can be made available over the Internet in intelligent electronic commerce applications. 1.3.3 Reliability Analysis A systematic analysis of past incidents reduces the risk of accidents. By identifying human and equipment risk factors, monitoring machines, and analyzing alarms that

1.4 Benefits of Case-Based Decision Support

15

censors automatically generate, case-based decision support increases the reliability of mission-critical equipment. 1.3.4 Technical Support From the diagnostic tools of field technicians (mobile office) to the help-desk at the after-sales customer support department (front office), case-based decision support helps solve problems more quickly and accurately. Case-based decision support significantly reduces the cost of finding a solution.

1.4 Benefits of Case-Based Decision Support The following are benefits of using case-based decision support: finding the solution to complex problems more quickly discovering decision knowledge hidden in data ─ transferring experience from skilled specialists to novices ─ building a corporate memory by sharing individual experiences ─

─

Case-based technologies enable companies to build powerful decision-support solutions. The following are some examples of benefits from case-based applications: ─ ─

─ ─ ─

─

A leading aerospace corporation reduced airplane downtime by 25%. A help-desk system for robot maintenance, developed in fewer than six months, significantly improved customer support service and helped cut down costs for training new service technicians. A leading manufacturer of electrical supplies can more quickly and accurately estimate the cost of manufacturing plastic parts. A small company saved 25% on the cost of repairing electronic boards. A leading US manufacturer of electronic devices delivered an intelligent sales support system on 120,000 CD-ROMs and on the Internet to find products that best matched customer demands, increasing sales significantly. In just two months, a major company in the business of computer maintenance went from 5% problems solved over the phone without sending a technician to the customer's site to 30% problems solved on first call. This generated paybacks for the project in fewer than 10 weeks.

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1.5 Case-Based Decision Support and Alternative Techniques Several technical publications have been published to allow comparison of casebased decision support and alternative techniques, such as classical statistics, expert systems, neural networks, and databases (see, for example, Althoff et al., 1995). The difference between case-based decision support and these alternative techniques is not only a question of which algorithms are used. Case-based decision support is designed to help people make decisions about individual cases. It does not aim at finding the right solution but at helping people to find the right solution by presenting the appropriate cases when they are needed. In the following, we briefly position case-based decision support with respect to these alternative techniques (see also Fig. 1.4). 1.5.1 Statistics Techniques such as statistics are primarily used to analyze populations so decisions about individual cases can be made. Should safety and reliability system in a nuclear power plant warn the user that a particular case looks very similar to the pattern of the Chernobyl accident, or should the case be ignore because it is not statistically significant? 1.5.2 Expert Systems Compared to expert systems, case-based decision support systems do not rely on rules that are supplied by a specialist. Specialists work out concrete problems and describe them, using their every-day language, in order to set up the domain model, if there is one, and the case base. It is a natural approach: the specialist is never asked to supply diagnostic rules or to define formal specifications of a decision process. The knowledge is easier to acquire and is easier to maintain because the system can learn new cases.

Data

Knowledge

Records

Closed Model Knowledge entered upfront

Expert Systems

Case Based Decision Support Systems

Open Model Data records

Databases

Fig. 1.4. Positioning case-based decision support and alternative techniques.

1.6 Development Process and Case Collection

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1.5.3 Databases Classical database technology offers languages such as SQL, which can retrieve cases that exactly match the query the user enters. On the other hand, case-based decision support can find cases that are similar but not necessarily identical. It performs fuzzy searches (also called parametric searches) and automatically finds keys for discriminating among cases. Similarity-based parametric searches are far more flexible than other parametric search techniques, because they do not rely on assumptions, such as “parameters that are entered first are more important.”

1.6 Development Process and Case Collection A case-based system is not a ready-made solution; cases are different from application to application: a case that describes an airplane engine fault for a technical support application is described differently than is a case for a product that can be bought in a sales-support application. The general development process is to build and maintain a case base customize the user interface ─ tune the way the information system operates

─

─

For example, the system’s developer can indicate how to compute the similarity for a given feature that describes a case, how the case base is organized inside the computer memory, and/or how individual cases are reused and adapted once they have been retrieved. If the case base evolves over time, as it usually does, an organization and the appropriate process must be put in place. An existing organization may also be adapted to support case collection and quality control. For example, in the aerospace industry, manufacturers often have technical representatives in the airlines who can collect and validate the technical content of the data that is fed back to the case base. A case-based application is only as good as the case base on which it operates. The effort required to build an application varies drastically. In a technical diagnostic application, building up a well-populated, high-quality case base can take six months and consume 80% of the effort; while customizing the user interface and the retrieval uses only 20%. This ratio might be completely reversed for a sales-support application where the data is an existing catalog of products. The catalog only requires some reformatting, but the bulk of the effort goes into customizing the user interface and the way the retrieval engine operates. This is one reason editors of case-based tools have produced vertical platforms for specific domains, for instance, different vertical platforms for help-desk applications and sales-support applications.

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To help you understand the concept of a vertical platform, we’ll use an analogy. Software that is used to build accounting software is not a specific application. For instance, the accounting module of an enterprise resource planning (ERP) system, such as SAP or Baan, can be used to build different accounting applications. Each application may require some specific customization, and the customization effort may sometime be quite significant. A company that has stocks will require a different interface and implement different accounting rules than will a pure consulting firm that does not have stocks. Customizations can even include national parameters: a French company will use different accounting rules than a British one, yet the same accounting module can be used with some level of customization to develop both. The accounting software relies on a standard generic database management system (DBMS), such as Oracle or Sybase, but it is simpler for a software integrator to use the vertical platform to develop a specific accounting application than it is to develop it from scratch using Oracle or Sybase. The concept of a vertical platform for developing case-based decision support applications is the same. A vertical platform is dedicated to specific tasks, such as building a help-desk. It is often implemented using a general purpose case-based decision support engine but incorporates application-specific modifications and add-ons, such as in the following instances: 1.

2. 3.

There is a default, pre-defined business process and a proposed development methodology. For example, there is a default process to acquire and control the quality of a case base in a help-desk environment on a local network. Cases are put on a server with different statuses (current case, closed case that has not yet been reviewed, reference case reviewed by a steering committee). There may be additional specific modules that are tailor-made for the market that is being addressed. For example, a call-tracking module is usually offered for help-desk applications. It is easier to market and to license the software to users and integrators (e.g., value-added resellers) who not only don’t understand the core technology but don’t care.

1.7 How Are Cases Retrieved? A CBR system needs some mechanism for retrieving similar cases from the case base. There are different approaches, depending on the case representation that was chosen. Text-based CBR systems use a keyword-driven approach. Conversational CBR systems usually have a hard-coded selection approach. CBR systems that follow the structured data approach sometimes offer the possibility of customizing the way the similarity is computed. The underlying technique used is called nearest neighbor retrieval. The global similarity between cases can be computed, for example, as the weighted sum of a local similarity that is computed for each

1.8 How Are Cases Represented?

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feature used to describe a case. Different metrics are employed for computing the local similarity for each individual feature according to its data type (numbers, symbols, etc.). Each feature may have a weight associated with it to increase or decrease its overall importance. In some systems, the way the local similarity is computed can be customized for a specific feature. This is achieved by programming a function that returns the similarity between the two cases for the specific feature or by using a pre-defined similarity editor. If the case base is reasonably large, it must be indexed. Different indexing mechanisms are available. Trees can be used to index large case bases. A decision tree is a hierarchical partitioning of the stored cases, based on the feature values. Its root node contains all the cases, while lower nodes progressively partition cases into subsets according to the various features applied in order of discriminatory power. The decision tree determines the order in which tests should be applied during consultation so cases can be retrieved. In some case-based decision support systems, decision trees are produced automatically by analyzing the cases using induction, based on data mining techniques. Induction can discover the most discriminatory features and use these to produce trees that are well balanced and not very deep so that cases can be efficiently retrieved by checking the value of a minimal number of features. Static decision trees generated by induction may perform badly if the value of a particular feature is not known during case retrieval (i.e., during the consultation by the case-based decision support system). An alternative is to generate the decision tree on the fly and, if the value of a given feature cannot be known, use the next best one etc. In case of an interactive consultation system where a question is associated with the feature (e.g., what is the state of the lights), the order of the questions is not fixed but, rather, context dependent. This technique is known as dynamic induction. If the questions are asked one by one, it is called system-guided dynamic induction. This is well adapted for low-skilled users who want to be guided step by step. Sometimes a list of questions is displayed on the screen, with the “best” (most discriminatory) questions at the top of the list, and the user can select the question(s) he prefers to answer. This is called user-guided dynamic induction. This kind of induction is appropriate for users who have a high level of expertise and who want to answer directly the questions they believe are relevant.

1.8 How Are Cases Represented? The first step in building a case-based application is to decide how to represent a case inside the computer. This will have an impact on how the case is stored (in a database, in a binary file, in an electronic document, etc.), how the CBR retrieval engine will perform, and what kind of knowledge can be discovered by the data mining engine. In commercially available systems, there are different approaches to case representation and, related to that, different techniques for case-based

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reasoning: the textual CBR approach, the conversational CBR approach, and the structural CBR approach. In the textual CBR approach, cases are represented in free-text form. In the conversational CBR approach, cases are lists of questions and answers. For every case, there can be different questions. In the structural CBR approach, the developer of the case-based solution decides ahead of time what features will be relevant when describing a case and then stores the cases according to these. For a real estate application, these features might include price, number of rooms, number of square feet, location, etc, and the outcome may be the specific apartment that can be sold or rented. This structured set of features is known as the domain model In chapter 2, we describe these three approaches in more detail and compare them.

2. Case-Based Reasoning Approaches

Case-based reasoning means learning from previous experiences. Given the fact that this is a very general approach to human problem-solving behavior, it is more than natural that there are different approaches for implementing this process on computer systems. In commercial CBR systems, there are three main approaches that differ in the sources, materials, and knowledge they use. The textual CBR approach eases case acquisition. It is very useful in domains where large collections of know-how documents already exist and the intended user is able to immediately make use of the knowledge contained in the respective documents. The approach is well suited when there are not too many cases at a time (less than a couple of hundred) and when each case has a short description (three sentences at most). Otherwise, textual CBR retrieves a large number of cases that are irrelevant. The cost for controlling the quality of textual CBR is high. The conversational CBR approach is very useful for domains where a high volume of simple problems must be solved again and again. The system guides the agent and the customer with predefined dialogs. However, the case base is organized manually by the case author, which is a complex and costly activity when the cases are described by many attributes (questions). The conversational approach is well suited for applications in which only a few questions are needed for decision making. Maintenance costs are high because the developer must manually position each new case in a decision tree-like structure and update the ordering of the questions. The structural CBR approach relies on cases that are described with attributes and values that are pre-defined. In different structural CBR systems, attributes may be organized as flat tables, or as sets of tables with relations, or they may be structured in object-oriented manner. The structural CBR approach is useful in domains where additional knowledge, beside cases, must be used in order to produce good results. The domain model insures that new cases are of high quality and the maintenance effort is low. This approach always gives better results than the two others, but it requires an initial investment to produce the domain model.


22


2.1 The Textual CBR Approach The developer of the case-based solution may decide to have cases recorded as free text (Lenz & Ashley 1998, Lenz et al. 1998b, Shimazu 1998). For example, this may be product descriptions or service reports. The CBR retrieval engine then uses various keyword matching techniques to retrieve cases. This can be based on string searches or on N-grams. N-grams is a technique that consists in separating words into lists of N characters. For example, for tri-grams, the word “akunamatata” is turned into the list of three characters (“aku” “kun” “una” “nam” “ama” ”mat” “ata” “tat” “ata”). Both the query and the case base are transformed, and case matching is performed on these. This technique is robust when there are spelling mistakes. A word like “aknamatata” would be recognized as similar to “akunamatata”. The main disadvantage of N-grams is that when there are large block of texts with many words, almost anything matches everything. To assess the quality of a textual CBR system, two parameters are used: recall and precision. Recall measures how many cases from the set of existing relevant cases are retrieved. Precision measures how many of the retrieved cases are relevant. As the case base grows, there is often high recall but low precision. This means that the system retrieves many cases, but only few of these are relevant. Example: Frequently Asked Questions. A company has a collection of documents that represent the current set of frequently asked questions at the company’s hotline. Each document contains a single problem and its solution. Navigating and searching in the list of documents is difficult for customers as well as for the people working on the hotline. To avoid this unfriendly search process, customers tend to call the company’s hotline instead of solving the well-known problems on their own. The result is that the traffic at the hotline is constantly increasing. The goal of the CBR system is to help customers and employees find solutions to problems whose solutions are already known and documented. The following box presents an example of a textual case and Fig. 2.1 shows the user interface of an example textual CBR system. Both are from the SIMATIC Knowledge Manager application, described in Annex A. Frequently Asked Question 241 Title:

Order numbers of CPUs with which communications is possible.

Question: Which order numbers must the S7-CPUs have to be able to run basic communications with SFCs? Answer:

In order to participate in communications via SFCs without a configured connection table, the module concerned must have the correct order number. The following table illustrates which order number your CPU must have to be able to participate in these S7 homogeneous communications.

2.2 The Conversational CBR Approach

23

Fig. 2.1. User interface for the SIMATIC textual CBR system (see Annex A)

2.2 The Conversational CBR Approach A second approach is called conversational CBR (Aha & Breslow 1997, Aha et al. 2001). The principle is to capture the knowledge contained in customer/agent conversations. A case is represented through a list of questions that varies from one case to the other. There is no domain model and no standardized structure for all the cases. This may produce unreliable behavior of the resulting CBR system, which may ask, during consultation, questions that are totally irrelevant (e.g., questions about electrical problems when the user specified the fault is mechanical). To work around this problem, questions to describe the cases are sometime organized into tree-like structures. The developer creates groups of questions that are used to describe a certain subset of the case base. Each case is then positioned by hand in this structure, which must be maintained manually by the developer. This task can be compared to developing an expert system that relies on decision trees.

24


To index the cases, the case author must also define the order in which the user is asked to answer the questions during the consultation. The work required to develop a conversational approach CBR solution can thus be compared to building an expert system. The difference is that this “expert system” displays cases to the user during the consultation. Example: Call Center for Printer Problems. A company is producing a wide range of different printers for the consumer market. For customer support, it maintains a call center with several agents solving product-related problems by phone. Since the main target market of the company is consumer oriented, most of the problems are simple to solve but the volume of the daily calls is very high. To provide the necessary staff for the call center, the company has hired several parttime agents who should be able to solve most of the simple calls directly. For more advanced problems, the company maintains a highly qualified second-level support team of product specialists. Since the training effort for the first-level agents is constantly increasing and cannot be done just on time anymore, there is a trend that more and more calls are forwarded to the second-level support team. This is not convenient for the customer and causes workload and priority problems at the second level. The CBR system should empower the first-level support agents to solve as many problems as possible on the phone without forwarding the call to the second-level support. The following box presents an example of a conversational case and Fig. 2.2 shows the user interface of an example conversational CBR system. Case: 241 Title: Printer does not work in the new release What kind of problem do you have? Does the printer perform a self-test? Does the printer work with other software? Did you just install the software? Did you create a printer definition file? What release did you install?

Printer Problem Yes Yes Yes Yes 4.2

Problem: Installation procedure overrides printer definition Action: Reinstall the printer from disk

2.3 The Structural CBR Approach

25

Fig. 2.2. User interface of a conversational CBR system

2.3 The Structural CBR Approach The idea underlying the structural CBR approach is to represent cases according to a common structure called the domain model (Manago et al. 1994, Bergmann 2002). The domain model specifies a set of attributes (also called features) that are used to represent a case. This set of attributes is structured by the domain model. In simple applications, the structure may be a flat table like the one produced by a spreadsheet. In more complex domains, the case description cannot be a flat table. For example, for a robot maintenance application such as Sepro's (see Annex A), electrical problems are described using different attributes than mechanical problems. A relational or an object data model can be used in such applications. In a relational model, there are several tables that are indexed by a primary key. Each case has a unique key, and the case description is distributed in several tables. The distribution is defined by the relations of the database. In an object-oriented database, objects are decomposed into subobjects. For example, in a PC salessupport application, a PC points to several subobjects, such as screen and hard disk. Each object has its own set of attributes, such as price and the manufacturer (see Fig. 2.3).

26


Fig. 2.3. An object-oriented domain model for sales support of PCs

Another important aspect of the structural CBR approach is that each attribute has a specific type. The attribute can be a symbol that takes one or several predefined values in a list. For example, the manufacturer of the PC may be Dell, IBM, Hewlett Packard, etc. The data type can also be a number (integer or real) within a certain range. For example, the price of a PC is a number between $800 and $6,000. Some CBR systems provide additional powerful attribute types. For example, the symbol can be ordered (for example, small, medium, high). A taxonomy type can describe relations of generality among attribute values. For example, for the attribute processor, values P100, P133 are Pentium; P266, P300 are Pentium II, etc. The ability to represent and use this type of background knowledge can significantly improve the quality of the retrieval for the CBR engine. Background knowledge can be used to discover additional similarities between two cases. For example, if the customer wants to buy a machine with a P366 processor, we would recommend a machine with a P300 (Pentium II) over a machine with a P266 (Pentium). To summarize, the domain model defines a standard way to represent all cases. Note that this is different from the textual CBR approach, which may contain a structure using languages such as SGML (for example, the question-and-answer headers in the example in the previous chapter), but where the content associated to each header is not standardized (it contains free text). Even if the content of these

2.3 The Structural CBR Approach

27

headers are stored in a database (using what is called text blobs), this is not sufficient to qualify for the structural approach. In the structural CBR approach, each attribute must be a field in the database and each value must be standardized. Example: Sales Support for Electronic Devices. A company has described a catalog of electronic products according to the different sales parameters (see the Analog Devices application in section 3.2 and Annex A). Parameters are used to describe technical characteristics of the product, as well as sales characteristics. The application’s goal is to find the best matching product based on the characteristics the customer enters for his or her desired product. There are so many characteristics that the chance of retrieving a product that is identical to the user’s query is very low (no exact match). Standard database queries almost never retrieve any products from the catalog database. The following box presents an example of a structural case and Fig. 2.4 shows the user interface of an example conversational CBR system. Both are taken from the Analog Devices application. In the real application, there are about 40 attributes to describe each case. The query is entered by assigning values to the attributes attached to objects input, output, supply, and other characteristics. The result is a list of products that can be compared and are ranked according to their similarity with respect to the query. Product Reference : AD8009 Price : 2.25 Input:

Input offset voltage : 2 mV Input bias current : 50 uA Open loop gain : 108 dB Input resistance : 110 kohms

Output: Output voltage : 1.2 V Output current drive : 175 mA Supply: Single supply : No PSPS : 70 dB Other:

Number of devices per package : single Maximum temperature range : IND Available Package(s) : SOIC

Additional domain knowledge can be entered. For example, information about the fact that for the temperature range a military space certification is more constrained than a SMD certification. When “SMD” is entered for the value of attribute “temperature range” in the query, products having the value “space” for this parameter also match the query. The structural CBR approach is very useful when quality control of the decision-support system (and the case base) is an important issue. It is also possible

28


Fig. 2.4. Consultation screen from a structural CBR system

to have a multilingual system with a single case base. A dictionary of attributes is then used to translate the attribute names and the values. Switching to another language amounts to displaying an alternative view of the same case base. This approach requires an initial investment to come up with the initial domain model. This model usually evolves over the lifecycle of the application: new attributes are added, new values are added. A maintenance process for the domain model is, therefore, required. This is why the INRECA methodology for building and maintaining the domain model and the case base is particularly important.

2.4 Comparing the Different Approaches From a user’s point of view, all these approaches look very similar. A query case is entered and similar cases are retrieved from the case base. From a managerial point of view there are important differences that deal with: 1. 2. 3.

the material required to set up the initial case base, the amount of work needed to maintain the case base, the effort required to control the accuracy of the CBR system afterward.

Tables 2.1a and 2.1b show a synthetic overall comparison of the different CBR approaches.

2.4 Comparing the Different Approaches

29

Table 2.1a. Comparison of the different CBR approaches Criteria Case Base

Query Case

Textual CBR

Conversational CBR

Structural CBR

A case is represented in

Cases are represented by

The case base is made out

free-text format. The cases

a list of

of records in a database.

may be structured according

question/answers. The

The records contain fields

to headers, but the content

list of questions and

whose values are

of the headers is in free-text

answers may vary from

standardized. Specific

form. The case base is the

one case to another.

cases are recorded by

collection of free texts that

There is no common data assigning values to the

may be, for example, in

structure. The questions

attributes that have been

electronic documents that

may be stored in a

defined. The initial case

are accessed on the Internet.

database but each

base may easily be

question is not a separate

extracted from an existing

field of the database.

database.

A query is represented by a

A list of questions and

The query consists of a set

question in free-text form

answers following a

of attribute values. These

whose content is similar to

dialog with the user.

may be obtained by

the description part of the

asking questions. In a

stored cases.

technical-support application, attributes are symptoms, operating conditions, and measures. In a sales-support application, attributes are characteristics of the desired products.

Results

A list of documents that

A list of possible actions

A list of database records

might be useful.

attached to the cases that

that describe cases.

have been retrieved. Domain model that

Background

Dictionaries of similar terms Ordering of questions,

Knowledge

like “install : setup” or

hierarchy of questions in

defines the case structure.

“printer : plotter” and

a tree-like structure,

Rules to deduce values.

information about the

possible answers for the

Information on how to

relationships between

questions that have been

compute the similarity

different words.

defined.

among attributes values.

30


Table 2.1b. Comparison of the different CBR approaches Criteria Initial Effort

Textual CBR

Conversational CBR

Structural CBR

Defining terms, synonyms

Storing dialogs within a

Defining the domain

and stop-words. Analyzing

case base, structuring the

model. Importing the

and setting up relationships

questions to organize the

database or collecting

between different words.

case base manually,

cases according to the

ordering the questions

structure of the domain

manually. This can be

model. This can be done,

done by entering existing for example, using an Case

Writing new documents

Creation

Maintenance

decision trees.

electronic questionnaire.

Adding new dialogs.

Adding a new record to

Depending on the

the database. A

structure of the dialog,

questionnaire (forms) can

new questions must be

be used in order to use the

entered.

same vocabulary.

Maintaining the dictionary

Maintaining the list of

Maintaining the case base

of controlled terms and

questions and answers.

and the domain model.

sentences. Analyzing user

Eliminating doubles,

The CBR system must

queries and results.

combining answers and

keep up with these

questions. Reordering

modifications. It can

the questions by hand for automatically compute the the consultation.

order of the questions during consultation

Multilingual

Almost impossible. Must

Difficult, no standard-

Case Bases

have different case bases.

ized thesaurus of terms.

single case base.

Advantages

Existing documents can be

The approach is intuitive

An existing database can

used as cases. No initial

and easy to understand.

be used to produce the

investment is required for

The system asks

initial case base. Creating

modeling the cases.

questions which are

additional cases can be

Easy to implement with a

answered by the user and done without any this dialog is stored in

knowledge about how the

the case base.

CBR system will use the cases afterward.

Drawbacks

It may be difficult to

The user is not guided. The

No explicit knowledge

CBR system must cope with

represented in the system create a predefined case

spelling mistakes. The

for computing similarity.

structure. This can usually

quality of retrieval depends

The interdependencies

be achieved within most

on the syntax and not on the

among different dialogs

technical domains but

real content of the cases.

cannot be predicted.

becomes harder in softer

High costs for quality

High maintenance costs.

domains. High investment

control of the CBR system.

costs.

2.5 Effort Required for the Different Approaches

31

If the material already available in the company corresponds to the material expected by the intended user of the system, choosing the CBR approach is natural and easy. The effort needed to set up a CBR system is always minimized in this case, and if the maintenance process for this material is already in place, the CBR system will often directly fit into it. Unfortunately this is rarely the case. The material available is rarely the kind of material that is really needed for the case base. For example, in a maintenance management system, historical data about machine faults is usually available, but, unfortunately, it rarely contains the technical information that is needed for decision making. When it does, the information is usually very difficult to use. For example, it is not unusual in legacy maintenance management systems to see case descriptions such as the following. Problem: Machine was not working and customer was very unhappy. Action: Decided to use a workaround procedure to fix the problem. Such material cannot be used to build a CBR system, because the technical information that is needed does not appear in the case description. Reusing free text fields of a database to initialize a case base is often difficult because the data was collected without guidelines and each case author had complete freedom about how he recorded the information.

2.5 Effort Required for the Different Approaches Table 2.2 summarizes the effort for the different CBR approaches. Each numbers in parentheses refers to an explanatory remark given in the following section. Effort to reuse existing material indicates the amount of work that typically is necessary to initially create the case base out of existing material and data. Effort for initial modeling refers to the work required to define dictionaries of terms (textual CBR), the initial domain model (structural CBR), or the lists of questions (conversational CBR). With case creation, we state the effort required to enter new cases into an existing case base. Effort to tune the CBR refers to how the developer or the integrator controls how the CBR engine retrieves cases. This can be, for instance, achieved by tuning the similarity measures, by defining similarities among attributes values, or by setting weights attached to attributes. For example, in a maintenance application, it is preferable to perform two tests that are quick and easy, such as looking at a fault code on a control panel or checking whether a lamp is on, over one that is time consuming and requires, for example, dismantling part of the equipment. With quality control, we look at the effort for verifying that the decision support system works correctly and displays accurate results. Maintenance refers to the work required for updating the case base and the CBR system when new cases are added.

32

2. Case-Based Reasoning Approaches Table 2.2. Comparison of the efforts required for the different approaches Effort per Task

Textual

Conversational

Structural

Reuse existing material

Very Low

High (1)

Medium

Initial Modeling

High (2)

Low

High (3)

Case Creation

Low

High (1)

Medium

Tuning CBR

Very high (4)

High/Impossible (5)

Medium-Low

Quality Control

Very high (4)

High (6)

Low

Maintenance

Low for the case

High (6)

Very Low

base, High to tune the retrieval (4)

(1) Cases cannot be loaded directly from a database. For each case, the questions are positioned by the developer manually in a tree-like structure. The ordering of the questions is not done automatically like in the structural approach. (2) This corresponds to defining the terms, synonyms, stop words. (3) This corresponds to defining the database structure, the attributes, and their type (domain model). (4) Retrieval is performed using keyword searches or other techniques such as “N-grams.” The ability to retrieve a document depends on the syntax of the information reported in the document, not on its true meaning. It is difficult to tune the system so that the right documents are retrieved, and it is also very difficult to maintain this afterward. New documents can drastically alter the retrieval quality (that is, recall and precision). (5) This technology does not allow the developer to define similarity, and the system functions as a black box. Developer sometimes defines fake attributes/questions in the case in order to work around the problem. (6) The behavior of the retrieval process can be altered when new cases are entered and new questions are defined to describe cases. The ordering of questions is done by the developer, unlike in the structural approach where this is done automatically. In Fig. 2.5, we compare the effort require to build the initial system and the effort required to maintain it. We see that a structural CBR system requires more effort to create the domain model, but less maintenance effort. The conversational approach requires less work initially but the most work for maintenance. The textual approach is half way between the other two.

2.6 Overall Assessment of the Different CBR Approaches

Structural Approach

Investment Effort

high

33

Textual Approach Conversational Approach

low low

Maintenance Effort

high

Fig. 2.5. Comparison of efforts for the different CBR approaches

2.6 Overall Assessment of the Different CBR Approaches The basic idea behind all approaches is to retrieve problem-solving experience that has been stored as a case in a case base, adapt and reuse it to solve new problems and, if not successful, learn from failures. On the abstract level, the CBR process can be described as four main steps (Fig. 2.6): retrieve, reuse, revise, and retain. While the names for these tasks may vary from one process model to the other, the basic ideas stay the same. During retrieval the most similar case or cases in the case base are determined, based on the new problem description. During reuse the information and knowledge in the retrieved case(s) is used to solve the new problem. The new problem description is combined with the information contained in the old case to form a solved case. During revision the applicability of the proposed solution is evaluated in the real-world. If necessary and possible, the proposed case must be adapted in some way. If the case solution generated during the revise phase must be kept for future problem solving, the case base is updated with a new learned case in the retain phase. Putting knowledge to work by using CBR is not something that can be done immediately. A process has to be put into place to capture business parameters that are used for decision making, to acquire quality cases that are described according to these parameters, and to maintain the quality of the case base over time. Sometimes, this is not an easy process. One reason for having the INRECA methodology and this book is to make this process clear and understandable, and to

34


Problem

New Case

RETAIN

Learned Case

Retrieved Case Previous Cases General Knowledge

REV ISE

Confirmed Solution

New Case

REU SE

Tested/ Repaired Case

RET RIE VE

Solved Case

Suggested Solution

Fig. 2.6. The CBR cycle according to Aamodt and Plaza (1994)

document it in a standardized way in order to make it consistent and reproducible. The following chapters describe the INRECA methodology and its applications in detail.

3. Selected Applications of the Structural Case-Based Reasoning Approach

In this chapter we describe four applications that have been developed using the structural CBR approach and the INRECA methodology: The case-based help-desk support system HOMER, the Analog Devices’ product catalog, the case-based maintenance application for the high-speed train TGV, and the Fraunhofer IESE experience factory. The four systems represent common application areas for CBR techniques. They are distinct in terms of their complexity, the representation and similarity measures that are needed to develop the systems, and the processes that have to be put into place to operate the applications. Hence, they illustrate the versatility of the INRECA approach and the bandwidth of solutions that can be developed with it.


36


3.1 HOMER: A Case-Based CAD/CAM Help-Desk Support Tool

3.1.1 DaimlerChrysler’s Motivation The CAD/CAM help-desk at DaimlerChrysler in Sindelfingen, Germany, provides support for the engineers developing Mercedes-Benz cars. Approximately 1,500 engineers work on workstations, using, on average, 30 different applications. The amount of hardware and software will increase drastically during the next years. Also, each year, one third of all hardware is replaced with new models and each program has about one major and up to four minor updates. Since the number of help-desk personnel will remain constant and the help-desk operation is a central and time-critical operation, some kind of computer support that goes beyond the classical trouble-ticketing systems was needed. 3.1.2 Initial Situation at the CAD/CAM Help-Desk The help-desk in Sindelfingen is organized into three layers (Fig. 3.1). The endusers are engineers developing Mercedes-Benz cars. When the engineers have problems with their software or hardware, the first person they contact is usually the “key-user.” This is an engineer working in the same group who has more experience with the software and/or hardware. If the key-user cannot help, the firstlevel support is contacted. This level is divided into several groups. The system help-desk gives support on the operating system and on simple hardware problems. The application help-desks specialize in certain software programs and provide support in their use and function. If the first-level help-desk cannot solve the problem, the problem goes to the second level, i.e., the system administrators and Service Level

End-User Key-User System Help-Desk

Application Help-Desk



System Administration, Application Specialists Maintenance, OEMs

Fig. 3.1. Support layers at the CAD/CAM help-desk

1

2 3


37

application specialists. Personnel at this level consist of highly skilled and specialized system administrators. Problems are transferred to specific system administrators based on their areas of expertise. Because the overlap in the areas of expertise among the administrators is small, problems have to wait if the required administrator is not available. Problems that cannot be solved by second-level support are transferred to the hardware and software vendors, some of which have in-house representatives. Initially, the operators at the help-desk were using several software tools to support them during help-desk operations (Fig. 3.2). HIT is a “Help-desk Information Tool” that was developed in-house to give the help-desk operators information about the user and his default environment from the network (NIS). CIVIS is an inventory system that provides maintenance and hardware configuration information to the help-desk operator. FES is a trouble-ticket tool that is used to record, track, and escalate calls. Even though these tools aid the help-desk operators in performing their tasks, they do not give support in diagnosing problems and cannot serve as a knowledge repository. The growing amount of hard- and software, as well as the goal to drastically shorten development cycles, greatly increases the responsibility of and pressure on the help-desk. Although the personnel at the help-desk are well trained, they cannot be knowledgeable in all areas. The complexity and the dynamics of the domain make it infeasible for one help-desk operator to understand and manage all the hardware and software, as well as the network. The end-users, i.e., the engineers, get frustrated when they have to be transferred from one help-desk operator to another, and the operators’ self-esteem suffers as well. Since most problems recur, the operators end up rederiving solutions to problems that a colleague has already solved. This not only frustrates the help-desk operator and the engineer, but also wastes company resources. NIS

HIT

CIVIS DATABASE

Help-Desk

FES

Fig. 3.2. Software tools used by the help-desk operators

38


3.1.3 Project Goals and Benefits To increase the efficiency and effectiveness of the help-desk, and thereby increase the productivity of car development, it is in the interest of DaimlerChrysler to accumulate the experiences of the help-desk operators in a knowledge repository and make them available to all operators on all levels (Fig. 3.3). This way, corporate knowledge, one of the company’s most valuable assets, is preserved and extended, ensuring that solutions to previous problems are available to all levels of the help-desk, whenever and wherever they are needed. The training of new helpdesk operators is also expedited. With the accumulated knowledge of the help-desk at the finger-tips of each operator, the quality of the support increases and hardand software downtime decreases. The repository can also be used to analyze the recurrence of problems to determine weak spots in hard- and software, as well as to check what to prepare for during updates or migration. Once operators’ experiences have been captured, the experiences can also be transferred among various sites of DaimlerChrysler worldwide. This ensures the same high standards of quality at every location. The knowledge repository that contains the experience of the help-desk operators, as well as the tools to access this knowledge, must be easy to create, maintain, and update. Consistency, usability, and validity are essential. Creating and maintaining a model-based system of such a complex domain is impossible. Obviously, a rule-based, expert system approach is also bound to fail. Since

Fig. 3.3. Knowledge acquisition and transfer

HOMER

2nd Level Expert 3

2nd Level Expert 2

2nd Level Expert 1

1st Level Support

Sub-Domain A

Sub-Domain B

Sub-Domain C

100 % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Domain Knowledge

Sub-Domain D

Knowledge Acquisition

Knowledge Transfer


39

solutions in the form of cases do exist, and because of the obvious advantages in terms of ease of maintenance, the CBR approach was chosen for HOMER, the case-based help-desk support tool (Hotline mit Erfahrung — Hotline with experience). To estimate the financial gains that could be achieved by supporting the helpdesk with a case-based system, we based our calculation on the calls that required more than 10 minutes and less than one day to solve. We assumed that the operators would not use the system for trivial calls (fewer than 10 minutes) and that problems that took longer than one day to solve typically required new hardware or software. Our calculations showed that the project would cover its costs in one year if we managed to reduce the average time needed to answer a call by 50% for 40% of the incoming calls, i.e., if 60% of the calls were solved in the same time and 40% were solved in half the time. The initial tests surpassed our expectations. With the initial case base, HOMER enabled the help-desk operators to solve 32% of the relevant calls in 6% of the time. The average time needed to solve a problem with HOMER was 9 minutes as opposed to 141 minutes with conventional methods (see section 3.1.8). 3.1.4 Knowledge Representation To perform the initial test, printer-plotter problems were selected as the target domain and the second-level help-desk as the target user-group. Both the userinterface and the knowledge representation were developed accordingly. When we started system development, representing the domain in a flat list of attribute value pairs or putting the experience of the operators in a textual, question-answer-oriented case base was very tempting (c.f. Thomas et. al. 1997). Since the modeling effort would be low, the operators could enter cases themselves, and knowledge acquisition could proceed swiftly. However, we soon realized that the cost of this “shallow” approach was the maintenance effort the company would need to put into the case base after it reached a certain size. Since the system had very limited knowledge about the structure and semantics of the domain, it could not aid in structuring the cases.

Failure • Topic • Subject • Behavior

Symptoms • Hardware • Software • ....

Fig. 3.4. Basic structure of a help-desk case

Solution • Fault • Remedy

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Similarity calculation in such systems is based purely on surface features: the number of identical answers to questions or textual pattern matching of the cases. We therefore decided to use a structured, object-oriented approach to model the domain. Even if the effort necessary to create such a model was higher in the beginning, in the long run it is easier to maintain in complex domains. We also found the model very useful in guiding the help-desk operators while they described and entered cases, and during similarity calculation. The cases in the help-desk domain were modeled based on the approach the help-desk operators use to solve problems (Fig. 3.4). The first thing a help-desk operator has is the Failure description given by an end-user. This description is a subjective description and may or may not have something to do with the actual cause of the problem. Failure in the case model comprises: the Topic: the area in which the problem is located (hardware, software, network, printing, etc.), ─ the Subject: the entity that the failure is related to (specific software, printer, screen, etc.), ─ the Behavior: the way the subject (miss-) behaves (crashes, wrong print size, screeching sound, etc.). ─

The System Description contains attribute-value pairs that help the help-desk operator diagnose the fault. They contain the minimum amount of information necessary and sufficient to diagnose the problem. They describe the hardware and software environment, the process interactions as well as the users and their tasks. The Solution contains the Fault, i.e., what the cause of the problem was, and the Remedy, i.e., how to solve the problem. In the actual implementation of the system there is also some administrative information regarding the specifications needed to apply this remedy and how long it takes to solve the problem. Each failure description can be the result of various system states, which in turn can be the result of various faults, which can be solved by applying various remedies. Every complete path from a failure to a solution is a case in our system. 3.1.5 The HOMER System The CBRWorks Server. Domain modeling, case and model maintenance, and initial case acquisition is done using the CBR-Works Server, a tool developed by empolis GmbH (at that time tec:inno). Because CBR-Works is written in Smalltalk, it is hardware independent. The system at DaimlerChrysler currently runs on a Windows NT machine. The CBR-Works Server stores the model and the case base and provides the tools that the administrator needs to model and maintain the domain.


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Figure 3.5 shows a snapshot of a small part of the domain model of HOMER. The tree-view on the left displays the hierarchical structure of the domain concepts. We have selected the HelpDeskCase to show the attribute slots. The slot Problem contains a concept Cproblem, which describes the failure as mentioned above. Situation describes the system. These are structured in subconcepts to ease the maintenance of the domain model and to speed up the retrieval process. Loesung holds the solution to the given problem and Administrativa stores organizational and statistical information. Loesung and Administrativa are not discriminant, i.e., these slots are not taken into account during similarity calculation. The cases are stored in two areas: the case buffer and the actual case base. The case buffer stores the newly created cases that have not yet been confirmed. The confirmed cases reside in the main case base. The help-desk operator has the choice of restricting the search to the confirmed cases or of using the case buffer as well. The HOMER-Client. The HOMER-Client is the interface for case retrieval, case acquisition and, case browsing to the CBR-Works Server and was written in Java. Earlier versions were developed as HTML-pages and later with JavaScript, but the dynamics and complexity of the domain model made development with these tools infeasible. Java was chosen because of its networking capabilities and the ease of making it available to the help-desk operator through a web server.

Fig. 3.5. Domain modeling

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The HOMER-Client gives the operator all relevant information he/she needs in an easily understandable manner at one glance (Fig. 3.6). It assists the operator with two modes of execution. In a user-driven mode the operator can build a query based on the domain model. This mode is meant for the experienced help-desk operator who does not need guidance on what to ask, knows the case structure, and wants to enter the data directly. The operator enters the query by selecting and specifying the relevant attributes in the domain structure shown in the tree view on the left side. This tree view can be switched off if the operator wants to go into the system-driven mode. For the operator with less experience or the operator who wants support in what to ask, he/she can switch to the system-driven mode. In this mode the help-desk tool presents the most relevant questions to the operator. The tool generates the questions by selecting the attributes with the highest information gain based on the current case base. The help-desk operator can choose which question to ask the end user from the list at the top of the left side of the screen (Fig. 3.6). The second list on the right side displays already-answered questions and the corresponding values in the most similar cases. The list on the bottom of the screen shows the suggested solutions in the order of relevance. The help-desk operator can view these in the case-browser by double-clicking them.

Fig. 3.6. The help-desk tool


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If the system cannot find an appropriate solution in the case base and the operator solves the problem in the conventional way, he/she enters this solution into the system and stores it, along with the query (problem and situation description) and administrative information, as a new case. The new case is stored in the case-buffer and later examined by the case-base editor (see section 3.1.6). System Architecture. To ensure every help-desk operator accesses the same, upto-date case base from every point in the network, the system is implemented using a client-server architecture. This enables us to use one central domain model and case base. The domain model and case base are also easier to maintain. Figure 3.7 shows the main components of the system. The HOMER Clients access the CBR-Works Server through the high bandwidth intranet of DaimlerChrysler. In terms of client-server systems, the HOMER Client is a fat client. It loads the whole domain model on start-up. Based on the domain model, it

HOMER Client

GUI

HOMER Client

GUI

Hotline

CaseBrowser

CaseBrowser

Hotline

Domain Model

Domain Model

CQL

CQL

Intranet

CQL CBR-Works Server Retrieval Engine

Case-Base

Case-Buffer

Domain Model

Domain Modeller

Fig. 3.7. System architecture

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can build up queries as well as cases, and sends requests to the server only when needed. This reduces network traffic. The communication language used is CQL (case query language), which was derived from CASUEL (Manago et al. 94), a query language developed in the INRECA project (Althoff et. al. 95, Bergmann 2001). 3.1.6 Users and Roles The complexity and size of the domain, as well as the demanded accuracy and consistency of the captured and reused experience, require the implementation of processes for case modeling, case acquisition, case maintenance, and case review. As mentioned above, the case base is separated into a case-buffer and a main case base. The main case base contains reviewed, prototypical cases. The experience in the main case base represents the approved way of solving problems for the CAD/CAM help-desk. The case buffer contains the cases that were created by the help-desk operators during their daily work. These cases may or may not be worth transferring to the main case base. Since they do contain information that is relevant to the daily operation of the help-desk, they are available to all help-desk operators but marked as being “unconfirmed.” To implement the necessary processes to operate HOMER, we defined three user types: the System Administrator, the Case Base Editor and the Help-Desk Operator. The Help-Desk Operator has the lowest access rights in the system. He/she is the person that uses HOMER on a daily basis to solve the problems of the endusers. The main tasks of the help-desk operator are case-acquisition and caseretrieval. The Case Base Editor is responsible for case maintenance and case approval. He/she checks the cases in the case buffer and transfers relevant cases to the main case base. The case-base editor also has the duty to check for redundancy and consistency in the case base. He/she may modify the value ranges of the attributes in the domain model but is not allowed to modify the domain model itself (i.e., add/remove attributes, move concepts, etc.). The System Administrator has the highest access rights. He/she performs domain model maintenance as well as user-administration. 3.1.7 Lessons Learned Technical Issues. The design and layout of the user interface turned out to be essential in the development of the system and its acceptance by the help-desk operators. Even though the help-desk operators are experienced computer users, they want the user interface to be clearly structured and not inundated with


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information. They prefer to have less information on one screen then to have everything on the screen at once, and they prefer to switch to the respective screen whenever they need structural or more detailed information. The help-desk operators use the cases that have been retrieved along with the suggested questions as guides while solving problems. Case focusing and question reordering based on information gain are perceived as very useful features in the help-desk system. In situations where the system cannot retrieve a solution, the help-desk operators use the structured domain model as a guide while solving the problem. The domain model also allows us to make reliable similarity calculations and support the help-desk operator during case-entry. Organizational Issues. Knowledge acquisition has been (and is) much more time consuming than we expected in the beginning. There are three main reasons for this: The help-desk operators have little time to spare from their operational duties, they are inexperienced in formulating their knowledge, and we want the resulting case base to contain prototypical cases that are accurate and consistent. When we started the project, we thought that we could use the 17,000 trouble-tickets that had been stored for the last years as cases. We soon found that these lacked essential information and were useless (except for reminding the help-desk operators what else they had to take into account). A CBR system is a means of storing, sharing, and reusing experience. If the experience stored in a CBR system is used only by the person who enters it, the system’s usefulness will be limited. The goal of a CBR system is to create a experience repository that can be used and filled by a group of people. However, as soon as a system is being used by a user-group, maintenance processes have to be put into place, and tasks and responsibilities have to be defined. We discovered that it is important to put the processes for system maintenance in place as soon as possible. These tasks have to be performed by dedicated personnel. Managerial Issues. An aspect that is often neglected, but which we found crucial to the project’s success, is involving a corporation’s management in the specification, development, and utilization of a system. Since a knowledge repository is only as good as the quality of the knowledge contained within it, continuous maintenance is necessary. This, however, requires dedicated resources and must be approved by management. By ensuring that management is involved and informed during the specification and development processes, awareness can be created, motivation can be kept high, and continuos support can be achieved. 3.1.8 Results Achieved After initial knowledge acquisition in HOMER was finished, the system was put to the test at the second-level, printer-plotter help-desk in Sindelfingen. For two

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months, incoming calls were monitored and, depending on their applicability, solved with HOMER. Since we did not want to interfere with the normal operation of the help-desk and the end-users, we decided to have the help-desk operator first solve the problem in a conventional manner. After the call had been resolved, the operator solved the same problem using HOMER. If no solution was found, the case was entered as a new case into the case base. During the two-month test period, 102 calls were handled by the second-level help-desk operator who performed the test. Forty-five of these calls were either trivial, directed to the wrong help-desk, or could not be reproduced (the failure disappeared). Of the remaining 57 problems, HOMER solved 18 (i.e., 32%). While the average problem resolution time for these problems was 141 minutes without HOMER, the help-desk operator needed only 9 minutes on average to solve problems with HOMER. This result was much better than what we based our calculations on when the project was initiated. As a next step, HOMER and the printer-plotter case base are being transferred to another production site of DaimlerChrysler. This site is currently implementing the same process chain for its printer-plotter environment as was done in Sindelfingen. Since the help-desk operators as yet have no experience with the process chain, the printer-plotter cases in HOMER are of great value to them. An interesting side-effect is that in the course of initial knowledge acquisition, the help-desk operators gained a much deeper insight into the processes involved in the domain that was being modeled. This resulted in several enhancements and has reduced the number of calls that arrive at the help-desk as well. 3.1.9 Summary Using INRECA technology, DaimlerChrysler and empolis developed a system that stores the experience of the help-desk operators in a case base and enables them to access, reuse, and extend the knowledge in a natural and straightforward manner. The project allowed the experience of the help-desk operators to be gathered and preserved, ensuring that solutions to previous problems are available. Since the time needed to solve problems decreased drastically, the productivity of the supported departments increased. Currently, the first version of HOMER, the Hotline with Experience, is operational at the second-level printer-plotter help-desk in Sindelfingen. Knowledge acquisition and development, are continuing. The system has also been used as a technology demonstrator to initiate several other help-desk projects at different locations of DaimlerChrysler.

3.2 A Case-Based Product Catalog of Operational Amplifiers

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3.2 A Case-Based Product Catalog of Operational Amplifiers Analog Devices, Inc., (ADI) designs, manufactures, and markets a broad line of high-performance linear, mixed-signal, and digital integrated circuits (ICs) that address a wide range of real-world signal processing applications. Analog sells its products worldwide through a direct sales force, third-party industrial distributors, and independent sales representatives. The company has direct sales offices in 17 countries, including the United States. Founded in 1965, Analog Devices employs approximately 6,000 people worldwide. Many of Analog Device’s customers are small electronics firms or electronics departments of large firms. A design engineer is the usual contact point with the central applications department. Analog Devices, at the time of building the application, employed up to 15 engineers to provide applications support to its customers and potential customers. The engineer takes the customer’s requirements for a product over the phone and tries to find a match in the AD product range. This process can involve weighing dozens of constraints while at the same time interacting with the customer to get an assessment of the customer’s priorities. Engineers handle about 40 calls per day, 50% of which relate to product selection. The remaining 50% relate to technical support in the use of a specific product. In 1997, ADI decided to support the customer consultation process using a CBR system. The application development focused initially on operational amplifiers, a product category within the Analog Devices product catalogue. Analog Devices has over 130 operational amplifiers in its catalog, each one specified by up to 40 parameters. Parameters can be real, integer, symbolic, or Boolean. Of the 40 parameters 39 require specific similarity search functions so that a requested value can be compared to the value of a specific operational amplifier in the case base. Since the first application to operational amplifiers, ADI, with support form Interactive Multimedia Systems, has applied the same technology to its Analog to Digital (ADC) and Digital to Analog (DAC) converters. A number of issues brought Analog Devices to the point where it wanted to provide a computer-based service to its customers. One of these was the cost of field-applications support engineers. At issue was not just the cost of salaries but also the cost of training a person to the skill level he or she needed to match a product to a customer’s requirement. In addition, a fixed number of engineers can only provide service to a fixed number of customers. The need to cover multiple time zones further diluted this scarce resource. Although it is difficult to picture customer requirements over the phone, the log from a parametric search facility presents a very valuable picture of customer requirements. This resource can then act as direct input into the product design, marketing, and production planning. The solution to the problem was to provide the customer with access to an intelligent search facility based on the INRECA nearest-neighbor matching functionality. The facility allows customers to specify their requirements interactively with the parametric search software. A customer can specify cut-off values for most

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parameters, can optimize all parameters, and can assign priority to any parameter. In return for specifying parametric values, the customer gets a list of the top 10 products in Analog Devices’ range that are closest to meeting his or her specified requirements. The customer can then link directly to the detailed data sheet for individual products. The customer can further specify or refine the parametric search until he or she finds the solution that meets all requirements. Analog Devices’ competitors in the market place all offer some form of search capability over the Internet, but in every case the parameters are restricted to a selection of part number, product category, price, and possibly one other parameter. These other search facilities restrict the customer to one or two meaningful parameters or otherwise require that the customer know the product before starting the search. 3.2.1 The Application Development Process The first phase of the development process involved gathering requirements for the application through interviews with technical and business staff from ADI. This step required about four meetings in total followed by analysis of the discussions. From this, during a period of four weeks, software developers prepared separate search- and user-interface demonstrations. ADI provided us with detailed feedback on these separate demonstrations and this input was used to create the engineered and integrated application. The first application took, in total, six months to produce and involved about 15 analyst days and about 35 developer days. The second application took fewer than six weeks and the third application fewer than 10 days. These savings were possible, by in large, by the automation of certain steps in the process. 3.2.2 Benefit Analysis Analysis of the project one year later allows us to list the following benefits related to the use of an intelligent parametric search facility. For the Customer The customer has full flexibility and the opportunity to “negotiate” with the system in order to select a product. The customer can do this using all 40+ parameters of the device. ─ The service short cuts the normal research activity and, therefore, reduces testing, design, and direct search costs for the customer. ─ The search engine provides an immediate equivalents list, which can save significant time in research and gives greater flexibility at the production stage. ─ The parametric search facility always returns a solution, unlike traditional SQLstyle searches, which only return a value if all conditions are exactly met. ─

3.2 A Case-Based Product Catalog of Operational Amplifiers ─

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The facility offers a selection of alternatives, just as an engineer or sales person might.

For the Manufacturer ─ ─ ─

─

─

The service frees up the experienced field-applications engineers, allowing them to spend more time addressing complex customer-support requirements. The service leads to greater product sales at the product-design phase (by supporting engineers), and this implies greater sales at the production phase. A valuable source of marketing information is generated when the customer specifies his application requirements to the parametric search facility. The information can provide input into the design and production of the products. The service is better than anything available and therefore will improve the image of the manufacturer. This progressive image should result in increased product listings by distributors. The manufacturer gets to know his customer better and can use the information gathered by the search engine to target promotional materials to specific customers and to tailor other services to customer needs.

3.2.3 Similarity Functions Used in the Application The Analog Devices application is in a technical domain where high-exponent -18 values for individual parameters are normal. Values can go down to 10 and up to 12 10 . This makes calculating similarity to the correct precision a real challenge. We therefore set about defining similarity functions that met the requirements of this technical domain (Vollrath et al. 1998, Bergmann 2002). We list the key approaches below. Similarity Calculation for Numerical Parameters. The similarity calculation for “less is better” is shown in Fig. 3.8. The similarity calculation for “more is better” is simply a mirror image of this “less is better” graph. The step from 1.0 to 0.5 is necessary to sufficiently differentiate between values that just meet the cut-off and those that just miss the cut-off. The cut-off when specified by the user is very important and must influence the search. Similarity Search for Model Number. The search engine allows searching by model number. The model number is an alpha-numeric with between four and ten characters. The following is the similarity search function for model number. ─

Example: Query value = OP?62: Any model number for which the only different character is that specified by “?” returns a value of 1; all others return a value of 0.

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1

0.5 V1

Ø X1

Min Value

Query Value

Max Value

Fig. 3.8. “Less is better” similarity graph: any case value less than or equal to the query value returns a value of “1”. Any case value greater than the query function returns a value calculated by the following: V1 = 0.5 *[(max - X1) / (max - query)].

Example: Query value = AD8*: Any model number for which the first characters are the same as those coming before the “*” returns 1; all others return 0. ─ Example Query value = AD7580: Only the model number that matches the query exactly returns 1; all others return 0. ─ It is possible to combine the previous examples in a comma-separated list. Example: Query value = OP?3, AD75*, OP34. ─

3.2.4 How the Software Works Searching. A requirement for a specific product is expressed in terms of the desired parameters for that product (see Figs. 3.9 and 3.10). The parameters are organized into groupings that can be selected by clicking on the appropriate tab. The groupings that apply to Op Amps are DYNAMIC, INPUT, OUTPUT, SUPPLY, OTHER, and SPECIAL. Much the same groupings would apply to other categories of electronic devices; however, the parameter list would be quite different.


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For any numerical parameter, the user can enter a cutoff or type “best,” but not both. For any non-numerical parameter, the user can either select a value from a list or type a desired value. For any parameter, the user can check “priority.” For any parameter without optimize (best) (generally non-numerical parameters), the user can select “priority” only AFTER a desired value (or cutoff) has been entered.

Fig. 3.9. Query screen for Op Amps product search (DYNAMIC)

Fig. 3.10. Query screen for Op Amps product search (OTHER)

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Numerical values can include a place-holder suffix. The first letter following a number entered by the user is interpreted on the server side to signify a unit for the value entered. (e.g., 9.9E3=9900). Clicking on any parameter abbreviation will link to a complete description of that parameter. RESET will clear all entered data from all parameters. If a parameter has a “=>” symbol to the left of the data-entry field, this means that the value selected or entered by the user is to be interpreted as “more is best” or more properly “greater than or equal to is best.” This indication does not, however, exclude products that might have a lower value for this parameter but meet all other requirements. It thus avoids the problem of the near miss in which a product that matches all but one of the requirements is not returned by a normal database search. Results. The search procedure is almost instantaneous and when it is complete, the application hands the results over to the Results Display component of the software (see Fig. 3.11 and 3.12). The resulting product specifications are displayed in two groups of five ordered by their similarity with the user’s requirements. From here, clicking on any part number takes the user to the data sheet of that part, which appears as a PDF document in Adobe Acrobat Exchange. Clicking on the NEW SEARCH button will take the user back to the Query screen. “Optimize” under the request column indicates that the user specified “best” in the query page. An “!” after a value in the request column indicates that the user decided to select this

Fig. 3.11. Results display (DYNAMIC)


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parameter. Any value in the results column that fails to meet the user’s request is shown in red. If one or more values fail for a given product, the “Part Number” at the top of the column is shown in red. The “Hold” checkbox at the top of each column allows the user to store this part in the Selected Devices list. In Fig. 3.12 three of the symbolic parameters have values selected, one with a priority. The assessment of whether a value meets a requirement is different for each of these parameters. For number of devices per package, the similarity is calculated based on a matrix with a value ranging from (0.0 to 1.0) specified for each cell in the matrix. In the case of temperature range, the approach is different as this is an ordered symbol. Here in the AD8002, temperature range with value industrial is lower than mil(SMD) in the ordered list and therefore fails to meet the requirement. Thus it returns a similarity of ~0.6 and appears in red. The OP249 has a temperature range with value mil(space), which is higher than mil(SMD) in the ordered list and thus returns a similarity of 1.0 and appears black. Available packages is specified as SOIC and returns a value of 1.0 if present in the list or 0.0 if not. The individual similarities for each of these parameters are combined to give an overall measure of similarity for the part in question. This overall measure is weighted based on priorities selected by the user and is used to list the parts in order of closeness— all in a fraction of a second. The user can elect to hold certain parts from the results of a given search and,

Fig. 3.12. Results display (OTHER)

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over multiple searches, assemble a list of the parts he or she is interested in. The “Selected Devices” list allows the user to compare parts from multiple searches side by side and to print the full list if required (see Fig. 3.13).

Fig. 3.13. Results held from an individual search

3.2.5 Status of the Application To date, the Analog Devices parametric search system has been delivered on CDROM to all customers worldwide and has been available on the company’s Intranet for engineers. Reactions from ADI customers have been consistently positive, and the service has clearly distinguished ADI as a pioneer in the production of an intelligent product catalog for electronic devices. The INRECA methodology allowed the CBR application developers at IMS to routinely build additional catalog search applications based on the methods developed during the Analog Devices projects. Since then, IMS has focussed on automating steps in the application-development process, allowing them to deliver applications quickly and pass on savings to their customers.

3.3 Case-Based Maintenance Support for TGV Trains

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3.3.1 TGV’s Environment With over 24,000 employees worldwide, Alstom is one of the world’s leaders in the transportation market, whether for trains, subways, street cars, or its renowned high-speed train, the TGV. Located in the Paris region, the company’s department of Electronic Equipment Systems (EES) designs and manufactures all of the electronics equipment and information systems embedded in its transport vehicles. Such embedded electronics are becoming ever-more important, because they not only improve train performance, but security and energy consumption as well. One of the key factors in reducing vehicle operation costs is to increase the vehicle availability, which means improving vehicle reliability and accelerating diagnostics when the vehicles break down. To create a new computer-aided maintenance system, Alstom engineers studied several technologies for developing a reliable and evolutionary diagnostic tool: expert systems, case-based reasoning, and model-based reasoning. Finally, they chose a combination of case-based reasoning and an inductive technique as proposed in the INRECA approach. The system was implemented using software from Kaidara (formerly Acknosoft). 3.3.2 A Hierarchy of Data Models and Case Bases Being able to create a diagnostic system that is flexible and capable of enriching its own knowledge base over time were key factors in Alstom’s decision. The problem of case-base maintenance is critical in this application. Indeed, any major module of a train can be hierarchically decomposed into subsystems, or minor modules. However, these subsystems are often linked to one another. Difficulties arise when the model changes. For example, suppose that the state of a relay is added to the case model to differentiate two types of problem that affect a subsystem. This change has an impact over all the cases, including the ones that are on a different subsystems. A new attribute has to be added to the other subsystems. However, the service technicians cannot invest the effort to go back to fill the attribute value for these other cases. Thus, the attribute is unknown in all cases that are located in these other subsystems. Unfortunately, this high rate of unknown values creates problems for both the induction and the dynamic induction. For instance, for induction, the same subtrees are repeated over and over in different parts of the decision tree. The decision tree is, therefore, difficult to validate and to maintain for the application developer, and harder to use for the maintenance technician.

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Thus, a single model and a single case base are not easy to create, validate, and maintain. The technical solution Alstom chose was to break down the model that describes the all the equipment into hierarchically ordered submodels that describe subsystems of the equipment. In parallel, the case bases are broken down into smaller chunks. A top-level case base aims at identifying the final diagnostic OR points at another subcase base, etc. When a subcase base is modified, this only has a local effect and does not create problems for other case bases. However, an attribute (e.g., start of relay xyz) can be shared among several case bases, and the system must inherit the state of the relay from a higher-level case base if it is required lower in the hierarchy. Figure 3.14 displays a subpart of the model hierarchy for the equipment called “chain of traction” (this includes the engine, the brakes, the alimentation, etc.), which is characterized by a set of fault codes called “30/40.” Note that, for instance, the submodel “CHQQT43” and, thus, the corresponding subcase base, appears seven times in this hierarchy.

Fig. 3.14. Subpart of the model hierarchy for a complex equipment “Chain of Traction”


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Each submodel that appears in this hierarchy is very simple (fewer than 15 attributes on average). Each subcase base is limited, on average, to 30 cases. Figure 3.15 displays the data model related to submodel “CHQQT43.” 3.3.3 Technical Approach for Diagnosis The diagnostic runs in two phases: 1. A depth-first approach is proposed first, which quickly guides the technician towards the most probable faulty subequipment. This is achieved through an

Fig. 3.15. Submodel CHQQT43. Most of the attributes are Boolean with values “0” and “72” (this represents a voltage measure). “Elément déposable” contains the list of LRUs: QQT43, QT43, etc. (relays, cards, etc.). The case base that corresponds to this data model contains only 13 cases.

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2.


induction process. The system uses either fault trees that have been previously generated in each case base or dynamic induction where the most relevant questions are dynamically generated. When the system proposes navigating into several different case bases, the technician must decide the most accurate choice. This process is repeated in the hierarchy of case bases until the user obtains a diagnosis. At the end of the induction phase, a CBR query is fired to see if another problem, whose fault signature is similar, can be found, possibly in different subcase bases. The query case has to be reconstituted from the different levels in the hierarchy. The process is fairly complicated from a technical standpoint (it is documented in a process diagram in section 9.2.3,) but it works and has been accepted by Alstom.

3.3.4 Example of the System in Action We present now an example of how the system is used for diagnostic support of the TGV on-board computer network. While a TGV is in circulation, its embedded electronics equipment continually receives data about the train’s functions. Each car of the TGV has an on-board controller, which communicates through a dual network with the on-board controllers of the other cars (and even with the cars of another TGV when they are in duplex mode). These controllers are in charge of temperature control, doors control, light control, etc. To monitor the correctness of the communication within the network, each controller provides some information about the data it receives and sends: Percentage of inactivity, percentage of lost information, etc. When these percentages reach some predefined thresholds, alarms are propagated through the network. All this information is logged into batch files, which can be transmitted in real time to the maintenance center or analyzed latter. Figure 3.16 shows a display of the HTML interface that receives this information from the on-board computers (this was built by Alstom engineers as part of the application). From this synthesized information, the maintenance technician must decide which controller is faulty (because of the network connectivity, it is not always the one that displays the alarm), and what the fault is: bad wire connection, faulty card, etc. As explained in the previous section, the maintenance technician must start the consultation process with the top-level case base, which describes the overall network. The diagnostic system guides him through the case base hierarchy towards the correct LRU (a card, a wire, or something else) he must check. If the check is correct, he can enlarge the search to other LRUs in other case bases by starting the CBR search.


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Fig. 3.16. Data transferred from the TGV for on-board network analysis

3.3.5 Integration into the End-User Environment Indeed, the diagnostic tool is not used alone by the maintenance technicians, but as part of a larger information management system. The end-user has access to this information-management system through a Internet/Intranet link to the application server. Thus, at this phase we plan the link among the databases of the maintenance platform. This includes the equipment databases (and the train itself), the historical databases, and the technical documentation: Information retrieval in the equipment database with respect to a parameter required by the diagnostic tool (plan, scheme, technical documentation, explanations, etc.). This retrieval is based on a common indexing scheme between the data models used by the diagnostic tool and the databases of the maintenance platform. This is supported by a tool developed by Alstom as part of the information management system. ─ Information retrieval in the train database with respect to a parameter required by the diagnostic tool (state of a relay, position of a door, etc.). The diagnostic tool can send a request to the train database to receive and exploit the answer. The communication is based on the TCP/IP protocol.

─

Figure 3.17 depicts roughly the links between the diagnostic tool and the various databases.

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The diagnostic tool uses HTML interfaces for the end-users, allowing them to access the central web server. It proposes a series of actions and simple questions (type of answer should be yes/no). It is possible to directly access the equipment data and/or the historical data known on the considered equipment. All the end-user’s actions are logged into a report file, which allows them to be analyzed off-line for data models and for updating case bases. ─ The diagnostic tool also includes specialized interfaces dedicated to the Alstom specialists in charge of managing the data bases: modification of the data models and of the model hierarchy, creation of analytical cases, validation of new cases created by the technicians of the after-sales service, analysis of the log reports, etc. These processes are documented in process diagrams in chapter 9 (see sections 9.2.1 and 9.2.2). ─

WEB Browser HTTP server

Diagnostic tool (html pages)

Inf. System

Train Case bases

Equipment database

Train database

Fig. 3.17. Communication between the diagnostic tool and the databases. All communications are processed by the information management system: Static communication with the online documentation (equipment database) and dynamic communication with the train (train database).


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3.3.6 Status of the Application The diagnostic tool has been redesigned by Alstom and is now being scaled up for Alstom’s customer service division on portable PCs and on its Intranet. The tool will then be used 24 hours a day by transportation operators’ maintenance crews. Beyond the specific application in the case of the TGV, Alstom aims to extend the proposed approach in a generic computer-aided maintenance system that could be used with any type of transport vehicle and associated electronics equipment. This maintenance system will be part of the application, which will be provided to the buyer of new equipment. From a business point of view, this will allow Alstom to expand its expertise from purely a provider of technical material to a provider of solutions for maintenance support.

3.4 The Fraunhofer IESE Experience Factory The explicit management of project experience (lessons learned on project planning and execution) has become of strategic importance to many organizations. This section describes how to set up and run a repository, implemented with case-based reasoning technology, for sharing project experience using an industrial-strength case study. The case study shows that it is both effective and efficient for the information seeker to have an experience repository at his disposal. In addition, lessons learned regarding the organization and population of such an experience repository are reported. An outlook describes the potential of additionally planned exploitations of experiences learned from projects. 3.4.1 Fraunhofer IESE’s Motivation The Fraunhofer Institute for Experimental Software Engineering (IESE) in Kaiserslautern, Germany, was founded in the beginning of 1996. It was created to transfer innovative software engineering technologies into practice through applied research. Software engineering technologies are tailored to company-specific needs and are constantly evolving. During the transfer, different people in several departments and groups of the institute gain project experience. People learn what Fraunhofer IESE´s customers need, how the people can transfer and tailor software technologies more effectively and efficiently, as well as how to improve the institute’s business processes. Expectations are that, with time, Fraunhofer IESE will transfer more technologies that are increasingly better tailored to companies’ needs (using the same or less effort). To do so, the institute must find better ways to effectively search for, reuse, and tailor technology. Therefore, the sharing of experience is a

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very immediate and obvious problem. This fact led to the launch of the Fraunhofer IESE Experience Factory (IESE-EF) in 1999. 3.4.2 Initial Situation At that time, Fraunhofer IESE consisted of 80 full-time employees with a share of 50 full-time scientists, and it was still growing. It was distributed over three locations. From a business perspective the institute had reached an age in which only few people remained who had overseen past projects. Insufficient information flow across groups and departments resulted in reinventing the wheel and in situations where mistakes were too often repeated. Business processes became too complex and new people had a hard time getting on board. Experts who created bottlenecks or who left the organization caused availability problems of key personnel. In addition, after more than three years of existence, the intranet was outdated. Thus, IESE needed to take its own medicine for the same reasons its customers did. From a research perspective, an experience factory was originally a logical and/or physical infrastructure for continuous learning from experience and includes the experience base (EB), a repository for the storage and reuse of knowledge and experience (see also Chapter 5). The experience factory approach was invented in the area of software engineering in the mid eighties (Basili 1985, Basili & Rombach 1988, Basili & Rombach 1991, Basili et al. 1992, Basili et al. 1994). Practice showed that it was substantial for the support of organizational learning, and that the project organization and the learning organization were separated (Basili et al. 1994). This was the main feature of the experience factory. It was based on the Quality Improvement Paradigm, a goal-oriented learning cycle for experience-based improvement and evaluation of project planning, project execution, and project analysis (Basili et al. 1994). The initial example for an operating experience factory was the NASA Software Engineering Laboratory (SEL) (Rombach & Ulery 1989, Basili et al. 1992, McGarry & Pajersky 1990). In the meantime several experience factory applications were started in the USA and also in Europe (Haley 1996, Humphrey et al. 1991, Seshagiri 1996, Rombach 1996; Houdek et al. 1998). The great potential of such applications gave the ignition to study experience factories more intensively regarding the methodology for building up and running one. An important step forward was to use case-based reasoning as a technology and a methodology for implementing the experience factory approach (Henninger 1995, Althoff & Wilke 1997, Tautz & Althoff 1997). The application described below was the first to encompass a case study on this topic and can be viewed as a breakthrough for experience factory applications (Tautz & Althoff 2000, Tautz et al. 2000).

3.4 The Fraunhofer IESE Experience Factory

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3.4.3 Sharing Project Experience The effort required to set up and run an experience-transfer program (especially for the project members providing the experience) forced IESE to start with a limited scope. In such a situation, there was, basically, a choice between two types of experiences: (a) experience on the applicability and limitations of software engineering technologies (product experience) or (b) experience on the planning and execution of projects (project experience). IESE decided to start with the latter, because the former was already partly covered by project reports and scientific publications. Project experience is always related to a particular business process of an organization (in this case, the planning or execution of a project). In addition, it may be related to a particular situation in a particular project (situation-specific experience), a project in general (project-specific experience), a customer (customer-specific experience), or a topic (technology/competence-specific experience). Alternatively, it may be related to a combination of projects, customers, and/or topics. Anything an experience is related to is referred to as the context of the experience. Two types of contexts can be distinguished: the context in which the experience was gained (root context) and the context in which the experience is applicable (application context). Consequently, it is insufficient to store experience without its context. Without its root context, the application context of the experience cannot be deduced systematically (see below). Without the application context, it is not clear under what circumstances the experience can be used. Over time, the application context can be deduced from the root context(s). A conservative approach is to use the root context as the application context. If the same experience is gained in several different root contexts, a single abstract application context can be constructed. Experience can also be applied in contexts that are not explicitly documented. However, this decision usually requires a human expert. Since it is not possible to document all aspects of the root context (neither effort-wise nor intellectually, since much of it is tacit knowledge), it is important to know a contact person that can provide further information. Hence, experience should never be stored anonymously. Personalized experience also leads to an increased motivation to use the stored experience. An experiment comparing the effectiveness and efficiency of the traditional human-based experience transfer (“ask your colleagues”) and the repository-based experience transfer (“query the repository with personalized experience without asking the originators”) clearly demonstrated this (Tautz 2000). The experiential results showed that both approaches complement each other in terms of the project experience found. For example, the effectiveness of the repository-based approach is improved by the human-based approach by at least 50% on average with an error probability of less than 0.1%. Here, the improvement

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is measured in the number of experience items found by the human-based approach but not by the repository-based approach, divided by the number of useful experience items found by the repository-based approach. In addition, the participants of the experiment were asked which of the two approaches they would apply in “real” projects. Twenty-eight out of 29 participants answered “both.” Many participants added that they would use the repository-based approach first to identify interview partners for the human-based approach. 3.4.4 Benefits of Sharing Explicitly Stored Experience So far, we assumed that experience should be stored explicitly (at least partly) to make it available to others. However, we have not discussed the alternative: setting up and using an expert network. For this purpose, IESE conducted an experiment (Tautz 2000). On a statistically significant level well below 1%, the results show that the repository-based approach is more efficient than the human-based approach for those seeking project experience. In the experiment, the repository-based approach took 12.5 minutes or less to retrieve one useful experience item in 90% of the cases. In 50% of the cases, it took 3.8 minutes or less. In contrast, the human-based approach took 18.5 and 10 minutes for 90% and 50% of the cases respectively. These numbers are based on the effort needed by the information seeker to get the experience items. If the availability of the experts is taken into account, the repository-based approach is far more efficient. In the experiment, it took 29.3 hours or less in 90% of the cases and 4.6 hours or less in 50% of the cases to get a useful experience item using the human-based approach. Using the repository-based approach, it took only 12.5 minutes and 3.9 minutes respectively. In addition, the repository-based approach improves the effectiveness of the human-based approach by at least 50% on average, with an error probability of less than 0.5%. 3.4.5 Organizing a Best-Practice Repository Project experience (lessons learned) can take on different forms (Birk & Tautz 1998). The two most important forms are observations and guidelines. Observations are facts that are of interest to future projects, often expressing some baseline (e.g., “it took 10% of the total effort to manage the project”) or some positive effect (e.g., “the customer was happy because we provided him with a ready-to-use tutorial”). Guidelines are recommendations for how a particular business process should be performed. For example, a guideline could be the following: “Interact with the customer frequently, at least twice a month.” In contrast to observations, a guideline is always based on some problem that has


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occurred in the past (e.g., “the expectations of the customer were not met”). In this way, relevant guidelines are restricted: Only guidelines that are really needed (to avoid the recurrence of a problem) are stored. Another form of a lesson learned is the sequence of immediate countermeasures taken by a project team in response to a recognized problem. We refer to such a sequence of countermeasures as a correction. While a guideline aims at preventing a problem from occurring in the first place, a correction is applied after a problem has already occurred. To store and retrieve the various kinds of experiences, IESE used INTERESTS (Intelligent Retrieval and Storage System; Althoff et al. 2000). It consists of three major parts (Tautz 2000): application tools for recording and updating experience over the web, an Experience Base Server for synchronizing access to the experience base, and a commercial case-based reasoning tool (CBR-Works). The latter enables similarity-based, context-sensitive retrieval. Case-based reasoning has been recognized as a suitable technology for implementing knowledge management applications (Gresse von Wangenheim & Tautz 1999, Göker & RothBerghofer 1999). The various experience forms are represented as different case concepts. The following concepts were defined: observation, guideline, problem, correction, business process, and project. The latter two concepts are used to store the root and application context of the experience. Semantic relationships between cases are represented by references. For example, an observation references the business process for which it is relevant and the project in which it was gained (root context). All cases are represented by attribute value pairs. Values can take on the special value “don’t care.” This allows abstract contexts to be stored. For example, a project case with all attribute values set to “don’t care” except for the attribute “customer” represents a customer context. Experiences referencing this project case are thus customer-specific. Similarly, topic/competence-specific experience can be represented. Using this structure, the state-of-the-practice is represented by the descriptions of the business processes, which are complemented by context-specific observations, guidelines, and corrections. The similarity-based querying facility of INTERESTS/CBR-Works allows for the finding of experience that was captured in contexts similar to a context at hand. Thus, potentially applicable experiences are identified (even if the application context has not yet been generalized from the root context). The personalized experience enables a project member to ask the original experience provider for more detailed information if necessary. Figure 3.18 shows a (simplified) exemplary guideline with its context. The example indicates that not all experiences are stored explicitly. For instance, TM Spearmint ,an innovative process-modeling tool developed in-house, is not stored because everybody at the institute knows it. The guideline is applicable whenever a project is set up for projects similar to “CAP” (application context). The root context is given by the problem underlying the guideline. If a project manager (responsible for setting up a project) is in doubt about whether to apply the

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Guideline: Title: Process modeling notation Description: If you want to use SpearmintTM, check whether the customer will accept its modeling notation. Originator: DP

Correction: Title: Switch notation Description: Discontinue using Spearmint and draw process model by hand using X’s notation Result: X accepted Originator: DP

Problem: Issue/Title: Suitability of SpearmintTM notation Situation: The project members wanted to represent their process model for purpose Y. Description: The notation of SpearmintTM was critized by X. Cause: X uses a different process modeling notation. Problem reporter: DP

Business Process: Title: Set up Project Owner: FB Responsible role: project mgr. Involved roles: {project member} Description: http://intra…

Project: Title: CAP Project mgr: DP Project members: {DP, MO} Goals: … Industrial partners: {X} IESE products: {SpearmintTM} ...

Fig. 3.18. Excerpt of a case base for an exemplary guideline

guideline, he can look at the underlying problem and decide whether the problem might also occur in the project at hand. Even if the guideline is not applied and the problem recurs (later in the project), the stored correction will probably lead to a quick solution. The project depicted in Fig. 3.18 acts as the root context for the problem. It also further restricts the application context of the guideline (see dotted line). A more abstract project description may be used as the application context TM for the shown guideline (only “Spearmint ” is relevant for the applicability of the guideline). In fact, for customer X the guideline need not be applied, because the answer is already known. The description of a guideline for customer X should TM state “do not use Spearmint for X.” 3.4.6 Populating the Case Base Experience needs to be explicated and stored before the repository-based approach can be applied. To do so requires the following steps (Althoff et al. 1999): 1.

Collect: In a first step, the experience must be collected. In this case, it was done using project analysis interviews (Fig. 3.19). At IESE, such interviews are conducted either at the end of a project (Collier et al. 1996) or — in case the project lasts more than nine months — periodically, that is, every six months. The interview results are documented as project analysis reports (PARs) (Fig. 3.19). A PAR contains an updated characterization of the project, things to watch out for in similar projects, things that went well, and things


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Project t eam

Project Analysis Repor t

Project team

Review

Experience engineer (EE)

Project Analysis (PA) Int erview (Collect)

Project Analysis Report

EE

Case Base

Split & Qualify

Fig. 3.19. Collecting cases through project analysis

2.

3.

that the interviewed project team would do differently if it had to do the same project again. Store: The collected experience is stored by copying it into the repository, splitting the experience into reusable parts (see Fig. 3.20), and initially characterizing each reusable part. In this case, the PARs are split into individual cases: The project characterization in the case base is updated; problems, corrections, guidelines, and observations are extracted out of the PARs and entered into the case base (Figs. 3.19 and 3.20). Qualify: Each case is qualified by analyzing its quality (e.g., its comprehensibility) and checking whether a similar case is already stored in the case base. If a similar case is already stored, the new case may be rejected, may be merged with the already-stored case — possibly generalizing its context — or may replace the stored case. PAR

...

Observat ion Observat ion

Question 8: What did you learn about the cust omer? A-LL: Company A likes to do as much as possible on their own, even if available resources are insufficient. A-GQM: • Company A wants t o change as lit tle as possible. • It is important to agree to a customer’s wishes. A-LL and A-GQM : • Even if those Company A employees involved in a project react very positive within meetings, it can happen that IESE suggestions are not seized because of the unavailability of the required resources/money to be provided by the responsible manager. • IESEproject members have to learn to different iate between customer employees being present during meetings and the responsible managers who have to provide the required resources/money. • IESEproject members have to learn to t alk to their customers »close to a possible offer« and in such a way t hat it is realistic for the customer.

...

Guideline IESEproject members have to learn to differentiat e between customer employees being present during meetings and the responsible managers who have to provide the required resources/money.

Problem Even if t hose Company A employees involved in a project react very po sit ive wit hin meet ings, it can happen t hat IESE suggesti ons are no t seized because of t he un availabilit y of t he required resources/money t o be provided by t he responsi ble manager.

IESE project members have t o learn t o t alk t o t heir cust omers »close t o a possible of fer« and i n such a w ay t hat it is realist ic for t he cu stomer.

Guideline

Fig. 3.20. Splitting the project analysis report into cases

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4.

Publish: After the new experience has been qualified, it is made available for retrieval, thus enabling the new experience to be shared. 5. Inform: Finally, everybody who may be interested in the new experience is informed. At IESE, the experience factory team is responsible for populating the case base. This includes making the appointment for the interview, conducting the interview, writing the PAR, and following up (activities from storing to informing). Such a dedicated team for populating is necessary, because providing experience to future projects is not one of the (prime) objectives of a project (Basili et al. 1994). Therefore, it cannot be expected that project teams will provide experience on a voluntary basis. In fact, many of the interviewees said they are willing to participate in future interviews but would not organize such a meeting on their own. Although population seems quite effort-intensive at first, it is important to recognize that only a systematic process guarantees high-quality experience. In return, high-quality experience encourages its usage, because it is perceived as useful. Thus, experience returned as the result of a query should exhibit a minimal quality. At IESE, interviews take at most two hours. Eighty-three percent of the interviews are one hour or less. The total effort of the experience factory team is approximately four person hours per interview, whereas a project member spends only about 1.7 hours per project and interview, including preparation for the interview and proofreading the PAR. This effort is negligible for projects running several months. The particular process of capturing experience (as described above) also has the positive side effect that most project members view the collection step as beneficial (without using the repository!). During the interviews, the project members learn how their colleagues view(ed) the project and thus have a chance to reflect on the project — something that is rarely done during the typical business day. Consequently, they get feedback they would not have gotten otherwise. In addition, they learn what to look for in future projects. This is reflected by the results of a survey IESE conducted. Thirty-four project members were asked to rate the importance of project analysis interviews. Sixty percent responded with “very important,” while 31% responded with “important,” Only 2% answered with “unimportant.” 3.4.7 Envisioned Usage of Lessons Learned Up to now, the sharing of project experience has been the focus of the IESE-EF. However, the potential of the captured project experiences goes far beyond that. This section outlines future scenarios for using the captured observations, guidelines, and problems.


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Organizational Problem Solving. During the population of the case base, critical or suboptimal project situations are stored as problems. A project-independent improvement squad meets periodically (e.g., once a month), scans the problem list, and assigns so-called creativity teams to solve clusters of similar problems. In this way, solutions that are optimal across the institute can be developed, as opposed to the locally optimized corrections developed within the projects. Recurring problems can be avoided by providing guidelines or by changing the description of business processes. In the context of the IESE-EF, an improvement process with the following outstanding characteristics was developed and exemplified: ─ Identification of small problems: Because problems are actively captured during the project analyses, a constant supply of problems is guaranteed. Without a “problem collection procedure,” only serious problems that deeply affect project members would be reported. In the past, problems that made project members feel uneasy (but were not serious with respect to project results) were typically not reported. ─ Effective problem solving: Due to the systematic problem-solving process, problems are not forgotten (or ignored). Problem reporters are kept informed at every stage of the problem-solving process. This shows employees that their problems are taken seriously and thus encourages problem reporting. ─ Efficient problem solving: The effort to solve a problem depends on the complexity of the problem. A typing error is corrected by a single person, more complex problems are discussed by the improvement squad and solved by an individual person, whereas really complex problems are solved by an assigned creativity team. Consolidation of Project Experience. As already outlined in the beginning of this section, the context of project experience can and should be generalized. Here, further machine learning techniques may be very helpful, especially from the perspective of looking at all observations, guidelines, and problems related to a particular business process (Althoff et al. 2002). Interesting questions to be addressed include, what percentage of projects were completed on time within budget? Or, what parts of the process description are hard to comprehend? Context-specific guidelines for problems that have occurred often in various contexts may be “upgraded” to (more) general guidelines and thus become part of the process description. The performance of such analyses requires a considerable number of stored lessons learned. Again, machine learning techniques may help here (Rech et al. 2001, Rech et al. 2002). In addition, the kind of mentioned analysis tasks are related to the issues of evaluating the case base according to the underlying goals and maintaining it (Nick et al. 2001).

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Roll-Out – The Need for More Technological Support. The management of project experience is of prime importance to IESE (project planning and execution are knowledge-intensive processes). However, the experience management processes described in this section can (and will) also be applied for business processes other than project planning and execution (Althoff et al. 2002). Dealing with these various kinds of experiences in the processes of capturing and extracting, as well as analyzing and evolving, will require further technological support. Case-based reasoning technology could, for instance, play the role of an intelligent platform for (other) machine learning procedures. Such techniques should support learning of case structure/contents from databases and/or textual descriptions (e.g., textual CBR, text mining: Rech et al. 2002) or the learning of user-adapted access structures (n-step CBR: Weibelzahl & Weber 1999, Bartlmae 1999, Richter 2000; Rech et al. 2001, Althoff et al. 2002; Jedlitschka & Althoff 2003). 3.4.8 Conclusion This application describes how project experience (lessons learned about project planning and execution) can be shared across projects using a case-based reasoning approach. One of the fundamental underlying principles of this approach is that project experience is never stored without its context. Case-based reasoning allows employees to retrieve experiences that were captured in a context similar to the one at hand. Experiences on implementing such a lessons-learned program at Fraunhofer IESE are reported. On this basis, scenarios for future exploitations of the captured experiences are presented and discussed. The case study described in this section must be seen as a first step towards a comprehensive experience-management program. Such a comprehensive experience management should not include only the management of process experience (a generalization of project experience to all business processes; Althoff et al. 2002). Instead, the management (i.e., systematic definition and capturing) of products, competencies, skills, and learning materials available at Fraunhofer IESE should be an integral part of comprehensive experience management (Althoff & Pfahl 2002).

4. Practical Guidelines for Developing Case-Based Reasoning Applications

Since the first commercial CBR applications in the early ’90s a number of examples of successful deployments of CBR have contributed to the practical experience with this technology. This chapter introduces those aspects that need to be considered during planning, implementation, and actually running a CBR system. It is a nine-step path towards the long-term success of CBR applications (see figure below). The INRECA methodology for building CBR applications, introduced in the subsequent chapters of this book, gives detailed descriptions and guidance on how to enact these steps. The view presented within this chapter focuses on the practical managerial aspects.

Advertise your CBR Application

8

9 Expand the Scope of the CBR Application

Measure the success

6 Take Maintenance Seriously

4

5

7

Provide Access for your Customers

Utilize Feedback and Testing

Aim at the Customer

2 1

3

Make a Purposive System Design

Define Clear Objectives


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4. Practical Guidelines for Developing CBR Applications

4.1 Define Your Measures of Success As in almost every software project, you should formulate clear objectives and measures of success. Each project requires well-defined measures of success for its execution. Our experiences with many projects have shown us that the clearer the objectives, the greater the quality of the results and the project’s success. The impact of such measures is twofold: 1. 2.

There is a unique goal for the project team that focuses the teamwork from the beginning onwards, all team members having a common understanding of the planned results. The management can control the project: problems and deviations from the project plan can be observed early and contingency plans deployed effectively.

Many of today’s knowledge-management projects still have very fuzzy objectives, such as “improve the capture and re-use of the available corporate knowledge.” In practice, it is impossible to measure success against a vague goal like this, which prevents control. A well-defined objective provides quantifiable, or at least observable, qualitative measurements, which allow monitoring and control of the project. As Philip Klahr, Vice President of Inference, said: “The real question to pose is: What is the single most important thing this knowledge base must do for management to call it successful?” There are many and sometimes conflicting objectives that have been targeted by different CBR projects. The following list summarises some of these goals of CBR systems (the examples have been chosen from different types of applications, such as help-desks, call-center support, diagnosis, and catalog search): ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─

improve the quality and consistency of responses to customers, improve customer as well as employee satisfaction, reduce the time, and therefore the cost, of solving a customer problem, reduce the cost-per-customer interaction, solve the customer’s problem during the first contact, reduce cost by freeing experts from routine tasks, reduce the number of inquiries that need to be escalated to the second level support, optimize employee training, standardize customer interaction, improve control of customer support, improve flexibility of staffing, increase self-servicing, decrease number of appointments for service technicians, improve efficiency of the service organization,

4.2 Orient Your Customers

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reduced cost of catalog distribution by online catalogs, reduce routine work of customer consultants with intelligent CBR catalogs, ─ ensure the customer always receives an offer; CBR catalogs always make an offer.

─

─

All the above objectives have been met by successful CBR systems; however, they sometimes compete with each other in practice. For example, “reducing the cost per call at a hotline help-desk by reducing the call duration” contradicts with the objective “to handle more complex problems directly during the first contact by the hotline operators.” Consequently, the design of a CBR system for a quick call handling at the hotline will differ from the one that is designed to solve as many problems as possible. Similar examples can be found in all other application areas. This, again, stresses the need to formulate goals and objectives as precisely as possible. These goals should be realistic and measurable, i.e., enable previous and subsequent stages to be compared. Rule of Thumb: Be as clear and relevant as possible when defining the objectives of your CBR system.

4.2 Orient Your Customers You must understand your customers and your users. A knowledge-based application is successful if it fulfills the requirements and expectations of its users. It must respect the experience and technical knowledge of its users, as well as their diagnostic and analytical competence. Here, the term “user” or “customer” refers to everyone who actually uses the application, including both clients and call-center agents. Since they all consume the services offered by the system, they all should be viewed as customers. However the “real” customer, or client, is the most important one. The performance of both after-sales service and the sales force primarily benefits the customer. The inexperienced user needs step-by-step guidance throughout the problemsolving process, while the experienced one prefers instant access to the solution. In some cases, it may be additionally necessary to take the language and cultural background of the user into consideration. For example, the American user usually likes to play around with a software system while the English user does not, even though they speak the same language. Furthermore, in different cultures the same term may have a different meaning. You cannot expect the client to be familiar with corporate terminology. Usually, he or she does not know the identification numbers of parts or goods. In the case of

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a troubleshooting procedure he or she will need help and support during the capture of the required information. Also, legal aspects influence the application design. For reasons of security, for example it is not sufficient to request that a customer opens the casing of an electrical device to check the voltage of the power supply. Similarly, you would not ask explicitly for the password of an online customer, even if it is the cause for his logon problem. The right question might be whether she or he has used some special characters for his password. Case base design must enable the successful use of the system by all intended users. It should encourage them to trust the proposed solutions, both at present and in the future. This affects the querying of required information as well as the presentation of the results. Whether an application is accepted by its users strongly depends on whether it is understood by the target user groups, which should be motivated to use the system. In many cases, different target groups will use the same case base. However, analysis reveals consultation with the system is initiated by the customer-user, regardless of whether it is a self-service application or a call-center that handles customer’s problems. This implies that the design of the case base is ruled by the customer’s needs and terminology, and that it is the customer who benefits from the results. Rule of Thumb: You must consider the specific requirements, abilities, and restrictions of the primary users of the system — your coworkers and customers.

4.3 Get the Big Picture Foresee, during the early days of the system design, the context in which the final application will be deployed. During the first two steps we analyzed two key aspects that are decisive for the success of a CBR project: 1. 2.

the definition of objectives and measures of success, the focus of attention on the expectations and abilities of the user.

Additional aspects arise from the circumstances and the surrounding where the system will be deployed: its context and purpose. This includes organizational, technical, and financial questions, for example, “Which requirements need to be derived from the integration of the system into the existing IT environment?” or “Which level of data security has to be reached?” System integration may require the definition of interfaces with existing databases, for example, a call-tracking system, product-data management, or customer-data management system. These interfaces may even influence the design of the final application’s user interfaces. If the system is to be deployed at a

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call-center as well as on the Internet or CD-ROM, then this has to be considered during the design, to avoid expensive redesigns during the lifetime of the system. Knowledge of the domain itself must be covered as comprehensively as possible. This is a prerequisite for a viable domain model. Attributes and their ranges of value, as well as similarity measures and interrogation sequences, must be defined. The latter have a strong impact on the users’ acceptance of the system. To achieve an efficient and extendable CBR application, these aspects should already have been analyzed during the planning stage of the project. This ensures a long-term contribution by the CBR application to the corporate competitiveness. Therefore everyone involved in the planning, development, maintenance and use of the system must contribute to the design process. All design decisions should be documented (for example, as proposed in the methodology presented in this book) to ensure the consistency of the overall development process. Rule of Thumb: Consider the organizational, technical, and financial constraints of the application from the very beginning and at all subsequent stages.

4.4 Think about Maintenance The benefits of a CBR application stand or fall on the content’s topicality. Many companies succeeded through the first three steps — well-defined objectives, focus on the customer’s needs, and purposive design — and quickly meet the measures of success they had set. But to stay successful in the long term, it is necessary to establish knowledge-management processes that ensure that the knowledge, as expressed in the case data and the domain model, is updated and maintained on a regular basis. This must be a commonly agreed corporate objective. In all cases where this task and effort were neglected, the investment into the system turned out to be a waste of money, since the application was outdated as quickly as it was set up. Investing in the system’s maintenance always pays back: If the experience and domain knowledge of the users increase, and this gain is not reflected in the CBR application then the quality of the results of the system will decrease, followed by a decrease in the users’ satisfaction. Sooner or later they will start to boycott the system. Outdated knowledge leads to frustration and disappointment. For instance, a travel broker must, of course, sell actually available flights and trips. What is obvious in this example is often neglected in practice. Maintaining the data base not only requires that new knowledge be added to the case base but that old cases be edited to keep them up to date. Almost all large companies employ someone who is responsible for their databases: The database system administrator. A similar role is required for knowledge administration, and many companies have

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created the position of a Chief Information Officer (CIO), thereby establishing this function at the top management level. The task of a CIO is to implement and operate a powerful knowledge-management process. You should define such an explicit process within your organization to ensure a regular knowledge update. The elements of such a process are: 1.

2. 3.

The identification and capture of emerging corporate knowledge. New knowledge arises when new or additional products are introduced or when products are replaced. Such developments are reflected by new and extended lists of frequently-asked questions and new service reports, or simply by new product descriptions and catalogs. New knowledge also emerges when new problems are solved. The newly captured knowledge needs to be integrated into the existing knowledge base: The case base and the domain model have to be updated. The updated knowledge base has to be distributed to its users, when necessary, after localization (translation and adaptation to cultural particular).

Special attention should be paid to the solution of those problem cases that could not have been solved within the experience of the existing case base. These need to be dealt with quickly and fed back to the system, enhancing case base experience. Users will only trust the system when they observe regular updates of the system and when failures do not recur. In the case of self-service applications users repeatedly interrogate the system for unsolved problems to check whether it has been updated. They also check to see whether they are being taken seriously. This shows that if you cannot update your system immediately, you should at least include a notice that you are dealing with the topic. Rule of Thumb: Knowledge management is a continuous process. Ensure that your case base and the domain model are up to date. Be prepared to extend the system with new functionality and for new application areas.

4.5 Welcome Feedback and Testing Benefit from user feedback. During the introduction of the CBR cycle in section 2.6, the importance of the revision stage was stressed. To measure the success of a CBR application in practice, encourage users to provide feedback, for example, whether the result the system provided was sufficient. A system’s users are best suited to appraise its benefits. Since in most cases it is not possible to completely cover a knowledge domain, a CBR application sometimes will produce incomplete or even wrong results. If such a situation occurs, the overall interrogation process should be traced, analyzed, and evaluated by an expert in the domain. In addition, within a call center, how a

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customer’s request was solved (by intuition?) or further treated (by second-level support, call back, other?) should be recorded. This information can be used to improve the CBR application; it needs to be recognized as part of the overall knowledge-management process. The above should be seen as an opportunity for optimization, rather than as a failure of the technology or the knowledge-management approach. During the development stage, with its more-or-less clean software laboratory conditions, it is hard to foresee all the eventualities that might occur in the real world with real customers. To improve the system, you must allow it to benefit from the customers’ experiences in using it. Rule of Thumb: Test your case bases and the CBR application. Use user feedback to continuously optimize your system.

4.6 Measure the Success Only well-defined measures of success ensure the long-term control of success. As with any project it is important to be in control of the success or failure of the CBR application. With CBR and knowledge-management projects in general the calculation of the ROI and the break-even point is hampered by lots of criteria that are difficult to measure. We need to differentiate between these soft criteria and the hard criteria that are quantifiable and can be evaluated monetarily. During the introduction of a CBR application, the company usually estimates the cost of inaction. This is an estimation of the cost for personnel, administration, etc., after the system is introduced (i.e., an estimation of the expected increase of efficiency with the CBR application) as compared to the cost without the system. These projected costs enable quite good estimations of the ROI and the break-even point. It is important to define a measure to evaluate the project’s progress and success against the objectives and criteria that have been defined in the first step of the CBR application-building process, as described in section 4.1. Often, companies do not continuously measure progress, or at least not against the defined criteria. Here it is important to stress that a measurement on an occasional basis, e. g., after the system release or after six months, is not sufficient. Only continuous measurement and control ensure the constant quality of the results the application delivers and, thereby, ensure customer satisfaction. Tendencies such as changing access rates, quality of system response, or changes in the types of questions users ask are observable. The early recognition of these tendencies and a quick reaction guarantee the fulfillment of the overall objectives and the longterm payback of the application. The measurement of success can be based on various sources, correlated or used for dynamic trend analysis:

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quantitative data on the use of the application (access frequency, ratio of successfully answered requests, unsolved requests, the 10 most often asked questions, etc.), ─ qualitative statements from user feedback (satisfaction, access frequency, etc.), ─ quantitative data from connected systems (e.g., in the case of a call-tracking system: number of calls, number of call backs, duration of calls; in the case of a diagnosis of complex equipment: number of failures, time required to diagnose failures, percentage of “No Fault Founds” responses).

─

This data can be related to the forecasted volume of customer requests and the respective cost without the CBR system. The objectives of the measurement and control process are two: 1. 2.

to justify the investments and the spent efforts against the management and thereby indirectly influence the pricing of your company’s services and products, to provide guides for the optimization of the system, similar to an analysis of weak points.

Rule of Thumb: Define measures of success and establish control processes that enable continuous measurement of success and continuous improvement.

4.7 Direct Access for the Customer Reduce your costs by offering self-service. An important advantage of capturing knowledge and experience with a CBR system is the opportunity to provide the customer with direct access to corporate know-how. Even if this were not a priority in the first place, when planning the CBR application, you should make it a mid-term goal. The strategy behind such an approach is call avoidance. Enable your customers to get the information they require directly from your homepage on the Internet and you will empower them to solve minor or routine problems themselves. CBR offers access to your knowledge via the Internet or on CD-ROM with three definite advantages: 1.

2.

Reduce your cost of customer support: A successful strategy of call avoidance reduces the cost of personnel at the call center and the support department. Employees become more productive and can focus their attention on the important tasks, since the amount of routine requests will be reduced significantly. This also increases the employees’ satisfaction and thereby reduces the fluctuation of personnel at the call center. Worldwide 24/7 support: Because of the high cost, it is impossible in most cases to operate a call center 24 hours, 7 days a week. Additionally, the global

4.8 Market Your Success

3.

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competition results in a clientele from different countries or even continents. A centralized call center would be overstrained by the diversity of languages and the different time zones. A CBR-based self-service solution empowers the customer to permanently access sales and service information from any point in the world, while in the meantime, helping your company avoid the costs and problems of a centralized all-time call center. Surplus value for the customer: The continuous change of the business processes towards more and more self-service also reduces the customer’s overhead expenses. Additionally, the consumed service is perceived as being of higher value by the customer since he or she determines and controls it.

Based on our experiences in building CBR applications we recommend that each company plans a direct access for its customers at the beginning of the CBR project. The reasons are twofold: First, we observe an increasing tendency towards self-service solutions and a strong acceptance of such offers; second, such access has an influence on the structure and design of the case base, as well as on the design of user interfaces. In general, state-of-the-art CBR tools allow for the easy modification of the user interfaces without requiring a change in the underlying domain model and case base. If the belated introduction of the direct access for customers requires structural changes to the model and cases, a complete redesign of the application may result. However, in many cases it makes sense to open access for the customers later. The system should be in use internally in the company until the contents and results are sufficiently correct and completely cover the application domain. Never abuse the customer as a beta-tester of your services. Rule of Thumb: Reduce your service costs and offer an actual surplus value for your customers by providing direct access to your CBR applications. The benefits outweigh the risks of disclosing parts of your know-how.

4.8 Market Your Success Do not forget to advertise the use of your CBR system. The introduction of a CBR system will have the desired results only if the system and the contained knowledge are intensely exploited. Only then will the investments be paid back, weak points of the application be discovered and relevant optimizations take place. In many cases incentives have proven to be the right means to stimulate employees to use the new CBR system during the introductory. Incentives and competition are important internal marketing tools to boost the exploitation of the system. These can be combined with a commendation or bonus for employees who

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contribute to the system’s improvement or who add significantly to establishing the best practice in knowledge management within the company. The use of self-service solutions by customers must be advertised, too, e.g., note the URL of your Internet service pages on manuals, online-help texts, etc. Even the phone message at the queue of the call center can be used to raise the awareness of self-service offers. Customers who deliver valuable feedback should be taken seriously and be thanked explicitly or even given a gift. The aim of such measures is to support the call-avoidance strategy, as well as to intensify the customer relationship. Venturing into knowledge management, and thereby strategically improving service and support, is a strong marketing argument in itself for the affected products and the whole company. In the end, the surplus value for the prospect or customer is decisive, e.g., service around the clock, reduced cost of service, improved quality of service, etc. Investments in CBR systems pay back, if those who benefit from the application are aware of its advantages and how they have benefited. Therefore, marketing and advertising to disseminate these advantages are necessary. Rule of Thumb: The ROI of a CBR system depends upon its regular use. Stimulate it by promoting it with marketing.

4.9 Expand the Scope Expand the scope of your CBR application. The general objective of almost every knowledge management project is to disseminate and propagate the use of the best practices throughout an organization. Even if this started in a particular department, you should spread a successful application over the whole company and maybe beyond. We recommend you to start with a clear-cut application area. Anyway, you should always keep in mind the possibility of extending the coverage of the CBR application itself, as well as a wider deployment of the system. Look out for such opportunities. A case base may contain knowledge of the company, products, processes, problem solutions, technologies, and so forth. If the mission of the CBR system was to improve customer support it was to be used mainly by service employees and customers then there would be no reason to keep this restriction for all time. Other company departments such as marketing or research and development, might benefit from the application, too. The same holds for business partners, e.g., suppliers as well as retail and sales organizations. It is also possible to use external sources to keep the system up to date and to maintain it. Suppliers and sales organizations can provide valuable experience and know-how that should be integrated into the CBR application.

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By including business partners and diverse company departments into the use and the maintenance of the CBR system, new synergistic effects can be obtained. This may go as far as making a real business out of the application itself. “Start small and grow fast!” is a well-known maxim. It directly applies to the deployment of CBR systems. Start with a clear-cut area of application and a small group of users, but expand both whenever there is the chance to do so. There exist diverse possibilities and directions: ─ ─ ─ ─ ─ ─

extending the scope of the knowledge base, extending the area of application, providing access for more company departments, co-operating with business partners, establishing the CBR application as a profit-center, Licensing the system to other companies.

The investment in CBR systems and applications is a strategic decision. Setting up the first case base can be viewed as the first step towards full corporate knowledge management. It is a challenging and continuous process that offers lots of opportunities: optimized customer support, cost reductions, improved business practices, take up and dissemination of best practices, unique selling points, and new sources of revenue, to name a few. Rule of Thumb: Transfer successful CBR applications to other products and company divisions in a long-term plan to include your business partners in it and actually make it a source of revenue.

5. Professional Case-Based Reasoning Application Development

Today, contemporary IT companies are facing a market that demands large-scale CBR projects and CBR software that fulfills quality standards. Therefore, a systematic and professional methodology for developing CBR applications is mandatory. This chapter presents such a systematic approach. It is particularly suited for IT companies that develop professional CBR applications on a regular basis. In such cases, significant CBR experience accumulates and must be preserved as corporate knowledge. This chapter shows in detail a systematic procedure for capturing and representing CBR application development experience, based on recent software engineering methodologies and in line with current quality standards. Therefore, Chapter 6 shows how to represent CBR application development experience using software process models.


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5. Professional CBR Application Development

5.1 Why Do You Need the INRECA Methodology?

5.1.1 How You Benefit from a Methodology A methodology combines a number of methods into a philosophy that addresses a number of phases of the software development life cycle (Booch, 1994). The methodology gives the guidelines for the activities that need to be performed in order to successfully develop a certain kind of software, in our case, a CBR application. These guidelines must be expressed in a well-defined terminology that enables the exchange of software development experience to continuously improve the development process. Using an appropriate methodology should provide significant, quantifiable benefits in terms of: ─ productivity, e.g., reducing the risk of wasted efforts, ─ quality, e.g., including quality deliverables, ─ communication, i.e., formal and informal communication among members of the development team, ─ management decision making, e.g., planning, resource allocation, and monitoring (Bergmann et al. 1997). One of the main driving forces behind the development and use of a methodology is the need for quality in both the products and processes during development of computer-based systems. This general observation holds for software development in general and for CBR application development in particular. Some methodologies and approaches to software development in general already exist, but software engineering methodology is still a major issue in current Software Engineering (SE) research. By adapting techniques originating in this area, we present a methodology based on recent SE techniques, which have been enriched by the experience of building and maintaining CBR applications. 5.1.2 What the INRECA Methodology Can Do for You When building a CBR application for daily use in an existing client organization, the system developer must consider a large variety of different kinds of processes. To reach the goals described above, a methodology must cover the following aspects, which occur naturally, more or less, in every software-development project: ─ the process of project management (cost and resource assessment, time schedules, project plans, quality control procedures, etc.), ─ the specification of the different kinds of products or deliverables (including software deliverables) that must be produced,

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the process of (technical) product development and maintenance, which contains all technical tasks that are involved in the development and maintenance of the software, ─ the analysis and, if necessary, the (re-)organization of the environment (e.g., a department) in which the CBR system will be introduced.

─

All these processes have to be defined and tailored according to the needs and circumstances of the current client. This activity requires a lot of practical experience. Although this experience is available in the minds of experienced application developers, it is usually not collected and stored systematically. This creates serious problems, e.g., when employees depart or when companies grow and new staff must be trained. But there is also an additional reason for documenting the software-development process: ensuring the quality of the produced software. 5.1.3 Ensuring High-Quality Software A very important issue in general business life today is the demand that products fulfill certain quality standards. Customers are more fastidious with industrial products. Producing companies have to prove that the production of their deliverables follows processes that can guarantee certain degrees of quality. The most important international standards for so-called quality assurance systems are ISO 9000 and SPICE. Although they do not guarantee the quality of a product, they contain guidelines for developing a management system for quality assurance. The INRECA methodology supports recent quality standards. The documentation that is produced by following the INRECA methodology guarantees that the development process of the CBR applications can be traced.

5.2 Foundations from Software Engineering The INRECA methodology (Bergmann et al. 1997, 1997a, Bergmann et al. 1998, Bergmann & Göker 1999) has its origin in recent software engineering research and practices. It makes use of a software engineering paradigm that enables the reuse of software development experience by an organizational structure called experience factory (Basili et al., 1994), as already introduced in section 3.4. An experience factory is an organizational unit within a software development company or department that supports capturing and reusing software development experience and thereby supports project planning. It links with project execution so that lessons learned from previous projects can be reused. In the INRECA methodology, the experience factory provides the organizational framework for

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storing, accessing, and extending the guidelines for CBR application development, which are the core assets of the methodology. The guidelines themselves are documented in a process-oriented view. Software process modeling (Rombach & Verlage, 1995) is a well-established area in software engineering that provides fundamentals for this. Software process models describe the flow of activities and the exchanged results during software application development. In a nutshell, the INRECA methodology consists of collected CBR development experiences, represented as software process models and stored in the experience base of an experience factory. The experience factory itself provides the organizational structure to access this experience and to keep it alive and up-todate. We now describe the experience factory paradigm and the basics of software process modeling in more detail. 5.2.1 The Experience Factory and the Quality Improvement Paradigm The experience factory approach (Basili, et al. 1994) is motivated by the observation that any successful business requires a combination of technical and managerial solutions: A well-defined set of product needs to satisfy the customer, assist the developer in accomplishing those needs, and create competencies for future business; a well-defined set of processes to accomplish what needs to be accomplished, to control development, and to improve overall business; and a closed-loop process that supports learning and feedback. An experience factory is a logical and/or physical organization that supports project development by analyzing and synthesizing all kinds of experience, acting as a repository for such experience, and supplying that experience to various projects on demand. An experience factory packages experience and collects it in an experience base. The experience consists of informal or formal models and measures of various software processes, products, and other forms of knowledge. Figure. 5.1 shows a schematized description of the experience factory, its steps, and its relation to the project organization. The project organization is in charge of planning and performing (plan execution) the software development project in an IT company. The experience factory analyzes the lessons learned from executed projects and records them in the experience base. To enable best reuse, these experiences are further packaged, i.e., they are generalized, tailored, and formalized. Further, the experience factory provides access to its experience base and thereby supports the project planning, which goes on in the project organization.


Project Organization Plan Project - Characterize (QIP1) - Set Goals (QIP2) - Choose Process (QIP3)

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Experience Factory Characteristics

Project Support Package

Experience in Models

Project Plan

(QIP6)

Experience Base

- Generalize - Tailor - Formalize

Execute Project Plan (QIP4)

Lessons Learned

Analyze (QIP5)

Fig. 5.1. The experience factory approach (adapted from Basili et al., 1994)

All these steps are organized into a cycle that enables the quality of the software development to be improved continuously. This cycle is called the quality improvement paradigm (QIP, see Fig. 5.2) and orders the six basic steps from the experience factory. In more detail, the six QIP steps are: Characterize (QIP1). The aim of this step is to characterize the project and its environment based on the available information. Normally, a large variety of project characteristics and environmental factors can be used for this characterization, such as the application domain, susceptibility to changes, problem constraints, techniques, tools, programming language, existing software, available budget, the number of people, the level of their expertise, etc. This step provides a context for goal definition and for selecting reusable experiences from the experience base. Set Goals (QIP2). The goals of the project need to be defined. There are a variety of viewpoints for defining goals, like those viewpoints of the user, customer, project manager, corporation, etc. Goals should be measurable, depending on the business models. Choose Process (QIP3). On the basis of the characterization, the goals, and the previous experience from the experience base, appropriate processes for implementing the project must be chosen. This results in the overall project plan. Execute (QIP4). The project plan is enacted, causing the development project to be carried out. For further analysis, respective records of the development process must be made.

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6. PACKAGE

1. CHARACTERIZE

5. ANALYZE

4. EXECUTE

2. SET GOALS

3. CHOOSE MODELS

Fig. 5.2. The quality improvement paradigm (QIP)

Analyze (QIP5). At the end of each specific project, the data collected must be analyzed to evaluate current practices. Valuable, reusable experience must be identified. Package (QIP6). The experience in the experience base consists of a variety of models. These models have to be defined and refined. Such models can be, e.g., resource models, process definitions and models, quality models, lessons learned, etc. On the basis of the new experience, these models might get generalized, tailored to a particular kind of situation, or formalized, so that they can be reused in other projects. The experience factory/quality improvement paradigm is especially tailored for the software business. It can be compared to approaches used in other fields of businesses, e.g., Total Quality Management. The experience factory and the quality improvement paradigm, provide a mechanism for continuous improvement through the experimentation, packaging, and reuse of experiences based on the needs of a business. 5.2.2 Software Process Modeling Within the INRECA methodology, software process modeling (Rombach and Verlage 1995) is the means for documenting CBR experience. It provides a welldefined terminology. Software process models describe the engineering of a product, e.g., the software that has to be produced. Several documentation formalisms and several terminologies for process models have already been developed (Dellen et al. 1997). Although the particular names that are used in the different representation languages vary from one representation to another, all


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representations have a notation of processes, methods, and products.1 Figure. 5.3 shows a graphical representation of these main elements. Processes. A process is a basic step that has to be carried out in a software development project. It is an activity that has the goal of transforming some input product(s) into some output product(s). To enact a process, different alternative methods may be available that allow the required output products to be created in a systematic way, a process is typically defined by the following properties: a particular goal of such a basic step. The goal specifies what has to be achieved. ─ a set of different alternative methods that can be used to implement the step. Such a method specifies one particular way of carrying out the process, i.e., one way of reaching the process’s goal. ─ input, output, and modified products that describe which products are required at the beginning of the step, which products must be delivered at the end, and which products are changed during enactment. ─ a set of resources (agents or tools) that are required to perform the step. Here the necessary qualifications or specifications that an agent or a tool must have so that (s)he/it can be assigned to the process are defined. ─

Methods. Methods contain a detailed specification of a particular way of reaching the goal of a process. A method can be either simple or complex. While a simple method provides only a description of what to do to reach the goal of the associated process, a complex method specifies a set of subprocesses, a set of intermediate products (called byproducts), and the flow of products among the subprocesses. This allows the definition of very flexible process models in a hierarchical manner, because very different process refinements can be described by using alternative subprocess models.

Input Product

Output Product

Process

Complex Method

Simple Method

Fig. 5.3. Basic elements of a process model

1

Processes are also called tasks and products are called deliverables.

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Product. The main goal of processes is to create or modify products. Products include the executable software system, as well as the documentation, like design documents or user manuals. Resources. Resources are entities necessary to perform the tasks. Resources can be either agents or tools. Agents are models for humans or teams (e.g., managers, domain experts, designers, or programmers) that can be designated to perform a processes. The most relevant properties of agents are their qualifications. Tools (e.g., a modeling tool, a CBR tool, or a GUI builder) are used to support the enactment of a process and can be described by a specification. Therefore, by using the required qualifications and specifications defined in the generic process, it is possible to determine available agents and tools that can be assigned to a certain process.

5.3 The CBR Experience Base The basic philosophy behind the INRECA methodology is the experience-based construction of CBR applications. The approach is particularly suited because CBR application development is an activity that relies heavily on experience. In certain areas, application development is already a routine task with clearly identified processes. This holds, for example, in the area of simple electronic product catalogs or simple help-desk applications. However, in recent years, CBR has entered new, important application areas that require large-scale application development. During the course of INRECA, three major application areas have been explored and large-scale industrial applications developed. The experiences of these developments have been captured and are the core of the INRECA methodology. Part III of this book presents this experience in detail. We expect the market for many new CBR applications to open up in the future. Therefore, the involved IT companies need to build up their own area-specific experience. To explore such new fields systematically, the experience must be captured, represented, and accumulated for reuse. The experience factory idea in the INRECA methodology is perfectly suited for this purpose. A second big advantage in the context of a CBR methodology is that the basic idea behind the experience factory is closely related to the general idea of CBR, i.e., retrieving, reusing, revising, and retaining experience (Althoff et al. 1998, Althoff & Wilke 1997, see also section 3.4). The experience factory stores specific experience, namely software engineering experience, in a case base, which we call an experience base. Thus, the motivation and rationale behind the experience factory approach should be easily understandable by people who are familiar with the Case-Based Reasoning approach in general.

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5.3.1 Experience Captured in Software Process Models In the INRECA methodology, software process models represent the CBR development experience that is stored in the experience base. Such software processes can be very abstract, i.e., they can represent some very coarse development steps such as domain model definition, similarity measure definition, and case acquisition. Or they can be very detailed and specific for a particular project, such as analyzing data from Analog Device Inc. operational amplifier (OpAmp) product database, selecting relevant OpAmp specification parameters, etc. The software process modeling approach allows the construction of such a hierarchically organized set of process models. Abstract processes can be described by complex methods, which are themselves a set of more detailed processes. We make use of this property to structure the experience base. 5.3.2 Experience on Different Levels of Abstraction The experience base is organized on three levels of abstraction: a common generic level at the top, a cookbook level in the middle, and a specific project level at the bottom (Bergmann et al. 1998). These levels are shown in Fig. 5.4. Common Generic Level. At this level, processes, products, and methods are collected that are common for a very large spectrum of different CBR applications. The documented processes usually appear during the development of most CBR applications. The documented methods are very general and widely applicable, and give general guidance for how the respective processes can be enacted. At this common level, processes are not necessarily connected to a complete product flow that describes the development of a complete CBR application. They can be

Experience Base Common Generic Level Cookbook Level Specific Project Level

Building blocks for CBR development and maintenance

Recipe 1

Recipe 2

...

Appl. 1 Appl. 2 Appl. 3 Appl. 4 Appl. 5

Fig. 5.4. Structure of the experience base

Recipe n

... Appl. m

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isolated entities that can be combined in the context of a particular application or application class. Chapter 4 presented CBR development experience on such a common generic level. More detailed process models for this level are reported by Bergmann & Althoff (1999) and Bergmann (2002). Cookbook level. At this level, processes, products, and methods are tailored for a particular class of applications (e.g., help desk, technical maintenance, product catalogue). For each application class, the cookbook level contains a so-called recipe. Such a recipe describes how an application of that kind should be developed and/or maintained. Thus process models contained in such a recipe provide specific guidance for the development of a CBR application of this application class. Usually, these items are more concrete versions of items described at the common generic level. Unlike processes at the common generic level, all processes that are relevant for an application class are connected and build a product flow from which a specific project plan can be developed. Chapters 7, 8, and 9 describe three recipes from the cookbook level. Specific project level. The specific project level describes experience in the context of a single, particular project that has already been carried out. It contains project-specific information, such as the particular processes that were carried out, the effort that was required for these processes, the products that were produced, the methods that were used to perform the processes, and the people who were involved in executing the processes. It is a complete documentation of the project, which is more and more important today to guarantee the quality standards (e.g., ISO 9000 or SPICE) required by industrial clients.

5.4 How to Apply the INRECA Methodology to New Projects When a new CBR project is being planned, the relevant experience from the experience base must be selected and reused. This relates to the steps QIP1 to QIP3 of the quality improvement paradigm (see Fig. 5.2). The recipes at the cookbook level are particularly useful for building a new application that directly falls into one of the covered application classes. The recipes are the most valuable knowledge captured in the methodology. Therefore, one should first investigate the cookbook-level to identify whether a cookbook recipe can be reused directly. If this is the case, the new CBR project can be considered a standard CBR application, and the processes from the respective recipe can provide immediate guidance for setting up the new project. On the other hand, if the new project does not fall into one of the application classes covered by the recipe, then the new project enters a new application area. In that case, the new project plan must be constructed from scratch. However, the processes described in the common generic level can be used to assemble the new project plan. Figure 5.5, shows the overall

5.4 How to Apply the INRECA Methodology to New Projects

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procedure for applying the INRECA methodology. It is a special variant of steps 14 of the quality improvement paradigm. 1.

Characterize New CBR Project. Identify the application area of the new CBR project. The goal is to decide whether an existing recipe from the cookbook level of the methodology covers this application. 2. Recipe Available: Identify whether an appropriate recipe is available. If this is the case then continue with step 3a; otherwise continue with step 4a. 3a. Analyze Processes from Recipe. An appropriate recipe is available. The process model contained in this recipe must now be analyzed to see whether it can be mapped and tailored to the application at hand. 3b. Select Similar Specific Project. Analyze the project descriptions on the specific project level. If a similar project is available then it should be identified as to whether some of the specific experience can be reused within

1

Characterize New CBR Project

2

YES

NO

Recipe Available ?

Standard Application

3a

Analyze Processes from Recipe

3b

Select Similar Specific Project

3c

Develop Project Plan by Reuse

5

New Application Type

Analyze Processes from

4a Common Generic Level

4b

Develop Project Plan by Combining Processes

Enact Project and Record Project Trace

Fig. 5.5. How to use the experience base

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the scope of the current project. This might be a specific way to implement a certain process or software component. It can help to identify existing software components from previous projects that can be reused immediately on the code level. 3c. Develop New Project Plan by Reuse. Develop a project plan for the new project. This project plan is based mainly on a process model from the selected experience recipe. However, application-specific tailoring and pragmatic modifications are typically required. If components from similar projects can be identified for reuse, the project plan must take care of this fact to avoid redevelopment efforts. 4a. Analyze Processes from Common Generic Level. The set of processes described at the common generic level should be analyzed in the context of the new project. The goal is to identify those processes that are important for the new application. These processes can be considered the building blocks from which the new project plan can be assembled. 4b. Develop Project Plan by Combining Processes. Based on the selected processes, a new project plan must be assembled. For this purpose, the processes must be made more precise and operational. Depending on how innovative the new application area is, it might even be necessary to develop new methods or software components. 5. Enact Project and Record Project Trace. Execute the project by enacting the project plan. Document the experience during the enactment of this project. Particularly, note all deviations from the developed project plan. This is important for two reasons. First, to ensure that the development is performed according to the necessary quality standards and, second, to feedback the new experience into the experience base for reuse.

5.5 Capturing Your Own CBR Experience After a project is finished, it is very important that the experience and the lessons learned are not lost, but are captured for inclusion into the experience base, which is the core of the INRECA methodology. This is necessary for continuous improvement of the CBR software-development process. This maintenance activity is related to steps 5 and 6 of the quality improvement paradigm (see Fig. 5.2). For the INRECA methodology, a particular extension and update procedure for the experience base has been developed (see Fig. 5.6). 6.

Analyze Project Trace. Collect all information that had been captured about the finished project. Analyze this information and identify the processes that were actually performed during the enactment of the project.

5.5 Capturing Your Own CBR Experience

6

Analyze Project Trace

7

Add Specific Project

8

Create or Update Recipe

9

Update Generic Level

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Fig. 5.6. Adding new experience

7.

8.

Add Specific Project. Document the project by creating a specific process description that accurately describes what has been done in the project. Add this process description to the specific project level of the INRECA experience base. If appropriate, create links to the cookbook-level recipes from which this project had been derived. Create or Update Recipe (optional). Based on the experience collected from the new project, it might become necessary to update an existing recipe or even to create a completely new one. An existing recipe might need to be updated if the following situation was encountered: A recipe was used to setup the project plan for the current project (step 3a in Fig. 5.5) but the experience from enacting the project indicated significant deviations from this plan. In this case, an update of the recipe is required to cover this newly learned lessons. A new recipe might need to be created, if CBR was applied to a new type of application that was not covered by any of the existing recipes (steps 4a+b in Fig. 5.5). The process description for the new specific project can then be generalized into a new cookbook-level recipe. For this purpose, all projectspecific information must be abstracted so that only those pieces remain that are likely to be reused for new, similar applications. It is very likely that this generalization process can be achieved only after several projects of this new application area have been realized. This avoids generalizing single experiences and is more reliable.

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9.


Update Generic Level (optional). If during the previous steps some new generic processes, methods, or products can be identified that are of more general interest, i.e., relevant for more than one application class, then they should be added to the common generic level of the experience base.

5.6 The INRECA Methodology and Quality Standards Previously, we noted the increasing importance of quality assurance systems and how they provide the reader with information concerning the requirements of these standards. CBR applications based on the INRECA methodology fulfill these requirements. That means that a company developing such software products can easily get the audit for their product. The major advantage of developing a CBR application following the INRECA methodology is the systematic documentation in a predefined format of every process. Tools exist that support the documentation in a user-friendly way.

6. Documenting Case-Based Reasoning Development Experience

In this chapter, the basic concepts and, in particular, the terminology of software process modeling are introduced at a level of detail that is sufficient for potential users of the INRECA methodology. The basic terms that were first mentioned in the previous chapter are now explained more precisely.


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6. Documenting CBR Development Experience

6.1 Into the Details of Processes, Products, and Methods

6.1.1 Software Processes: The Basic Activities A process is a basic step — an activity — in a software development project. Each process has a particular goal that it must achieve. Typically, a process is named for this goal. Examples are: “Analysis of system requirements,” “Development of a system design,” and “Implementation of the software components.” These are examples of high-level technical processes that usually occur in every software project. A process usually has a set of defined input, output, and modified products. For example, one input of a software implementation process is some kind of design document and the output is usually the produced code and the documentation of that code. A process always transforms the given input into a desired output. A modified product is basically a product that is both an input and output product at the same time. In the following, we use the term product for everything that occurs as the input or output of a process, or gets modified by a process. A software design document is an example of a product. Another example is a piece of software that is produced. The final system being delivered is usually the main output product of the technical software development processes. To enact a process in a systematic way, different alternative methods are applicable. A given method specifies one particular way of carrying out the process, i.e., one means of creating the output products of the process. When planning a project, you must select the method that is most appropriate for implementing the process in the particular context of the current project. For example, a method for implementing a graphical user interface may be to code it solely in C using only the X-Windows primitives. Another method is to use a GUIbuilder to design the interface and to link events to graphical elements. A third method is to reuse and adapt the code of an existing, very similar user interface. The enactment of a process involves several resources. These can be human resources, called agents, who are actively enacting the processes and who are responsible for delivering the output product(s) of the process. Software tools are resources that are used by an agent during enactment of the process. For example, a programmer is an agent who is actively involved in the process of implementing a software module. S/he uses a GUI-builder to produce the GUI software and a word processor to write the GUI documentation. Both, the GUI-builder and the word processor are resources in the form of tools.

6.1 Into the Details of Processes, Products, and Methods

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6.1.2 Technical, Organizational, and Managerial Processes We distinguish three types of processes that are involved in a software development project: technical processes, organizational processes, ─ managerial processes. ─

─

Technical Processes. First, there are the technical processes, which describe the development of the system and the required documentation itself. Some of the technical processes that are part of most software development projects are, for instance, requirements analysis, system design, implementation, and testing. Organizational Processes. The second kind of processes that are part of most standard software development processes are the organizational processes. They address those parts of the user organization’s business process in which the software system will be embedded. New processes have to be introduced into an existing business process, such as training end-users or the technical maintenance of the system. Existing processes may need to be changed or re-organized to make the best use of the new software system. For example, the introduction of a helpdesk system for hot-line support, requires new processes for training the hot-line personnel, for archiving request-records, and for updating and maintaining the help-desk system. Managerial Processes. The third kind of processes are managerial processes. The primary goal of managerial processes is to provide an environment and services for the development of software that meet the product requirements and project goals, i.e., services for enacting the technical and the organizational processes. Examples of managerial processes are project planning, monitoring, and quality assurance. In a software development project, several processes of these types typically interact. The next section will examine this assertion. 6.1.3 How Processes Interact Typically, a process produces an output product that is used as an input to another process. For example, the process “Perform Feasibility Study” produces as its main output an “Evaluation Document” product. Then, this evaluation document is used as input for the process “Perform First Project Phase.” Figure 6.1 shows this kind of interaction between these two processes. This figure also shows the graphical notation the INRECA methodology uses for processes and products. For processes, we use rectangles with rounded edges, while products are displayed as rectangles with a double line on the left and right side. Processes and products are connected by different kinds of arrows. An arrow from a process to a product denotes that the

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product is contained in the output-products description of the process, i.e., it is produced by the process. An arrow from a product to a process denotes that the product is contained in the input-products description of the process, i.e., it is required by the process. A double-headed arrow between a product and a process denotes that the product is contained in the modified-products description of the process, i.e., it is modified by the process (no double-headed arrows occur in Fig. 6.1). Perform Feasibility Study

Project Idea

Evaluation Document

Perfrom First Project Phase

First Application

Fig. 6.1. Interaction among processes

For each process, there can be several alternative methods that describe alternatives to enacting the process. Simple methods are graphically denoted by a trapezoid, while complex methods are printed as a triangle. Figure 6.2 shows the “Feasibility Study” process together with two alternative complex methods. Perform Feasibility Study

Project Idea

Theoretical Analysis

Evaluation Document

Prototype Building

Fig. 6.2. Two alternative complex methods

Complex methods are called complex because they consist of several subprocesses connected in a particular way. Together, these subprocesses describe the details of how to enact the respective complex method. For example the method “Prototype Building” can be described in further detail by the subprocesses and the product flow shown in Fig 6.3. All these subprocesses have to be enacted in order to implement the complex method “Prototype Building.” Please note that as a result of the subprocesses of a method, all required output products of the higher-level process must be produced. In the example shown here, the “Evaluation Document” is created by the product flow shown in Fig. 6.3 and is further used in Fig 6.1 as input to the process “Perform First Project Phase.” By this approach, the whole software development activity can be divided into several isolated parts that are easy to understand. Also, these parts can be reused individually for new projects or modules.

6.2 Generic and Specific Descriptions

Project Idea

Define Scope & Goals for Feasibility

Scope & Goal Definition

Develop Prototype

Evaluation Document

Evaluate Prototype

Prototype Software

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Fig. 6.3. Product flow for the complex method “Prototype Building”

6.1.4 Combining Processes to Process Models One of the main ideas behind software process modeling is to make explicit all the processes, products, methods, resources, and interactions of which a software project consists. These are kinds of information that contain very valuable experience about the software development process. They should be captured, stored, and reused for setting up and implementing new projects. The processes, products, and methods must be identified and documented in a clear and understandable way. The diagrams alone are not sufficient for this purpose. Each of the elements that occur in a diagram must be described in sufficient detail. Sections 6.2 and 6.3 show in detail how processes, products, and methods are documented in the INRECA methodology. Such a description of processes, products, and methods is called a process model. Obviously, making these elements explicit provides a solid basis for project planning. For example, the effort required for the project can be calculated based on the processes involved in the project. Explicitly documented processes and the management of the products that arise also support the execution and monitoring of a project. Documenting processes is also essential for controlling and improving the quality of the product (software) being produced. Documentation is also essential to ensure compliance with current quality standards.

6.2 Generic and Specific Descriptions The INRECA methodology distinguishes between generic and specific descriptions for processes, products, and methods. The differences will be made clear in the following sections.

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6.2.1 Generic Descriptions The generic descriptions describe processes, products, and methods in a way that is independent of a specific development project. They contain generalized information that is likely to be useful in several specific situations. Again consider, for instance, the process of building a GUI for a CBR application. The process “GUI Development” has one input product, namely the document describing the specification of the GUI, and two output products, one for the software implementing the GUI and one for the documentation describing how to use the GUI. The generic description of the input product (GUI specification document) declares what information in general should be contained in a GUI specification document. It does not say anything about a particular GUI to be developed within a project. Moreover, the generic description of the GUI building process should also specify that one agent is required who has programming capabilities and who is familiar with a GUI-builder, and that a tool is required, namely a GUI-builder, that is flexible enough to produce the kind of GUI that is required. All this is generic information, not restricted to a single project and, therefore widely applicable. The generic description does not contain any information on exactly what the GUI looks like, which person is actually allocated to the task of building the GUI, or which particular GUI-builder is used. The generic information only describes requirements of agents or tools, and general guidelines on how to execute a process. Generic descriptions are the most important part of the experience base. They are used to document the common generic level and the cookbook level of our experience base (see section 5.3). 6.2.2 Specific Descriptions Specific descriptions elaborate processes, products, and methods for a particular development project. Usually, they are created after the project is finished. Specific descriptions contain information such as the specific agents who were involved in enacting the process and specific tools that were used. For the purpose of documentation, they can also contain information about the effort that was spent for a process, and so forth. Consider, for instance, a specific GUI development process as it occurs in a project, let’s say, in the Analog Devices Application described in section 3.2. The specific description for this process contains information about the input and output products, e.g., the Analog Devices GUI specification, Analog Devices GUI software, and Analog Devices GUI documentation. It contains references to the specific Analog Devices GUI specification document from the project. Moreover, it also contains information about the particular programmer who built the GUI (e.g., James Simon) and about the particular GUI-builder tool that was used (e.g., Visual Basic).

6.3 Detailed Description of Process Models

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6.3 Detailed Description of Process Models Generic and specific processes, products, and methods are documented (and stored in the experience base) using different types of sheets. One such sheet is like a structured page containing all relevant information about the respective item in a predefined format. The sheets help to standardize the documentation of the experience. They are created as Web pages, which can be viewed using a standard Internet browser. Hence, accessing the experience base becomes a very easy task that does not require any specialized knowledge. Altogether, the following types of sheets are available: ─ ─ ─ ─ ─ ─ ─ ─

generic process description sheet, generic product description sheet, generic simple method description sheet, generic complex method description sheet, specific process description sheet, specific product description sheet, specific simple method description sheet, specific complex method description sheet.

Each sheet is a form that contains several predefined slots that should be filled in to document the process, product, or method. Before introducing these sheets in detail, we will give a brief overview of the different kinds of sheets and how they are related (see Fig. 6.4). A single sheet is used to describe processes. This sheet will contain references to the respective input, output, and modified products of the process. Every product is documented by using a separate description sheet. The process description also contains references to the applicable methods. A method can be either a simple method (which is elementary and does not contain any

Generic/Specific Process applicable methods input, output, modified product

Generic/Specific Simple Method

sub-process

Generic/Specific Complex Method by-product

Generic/Specific Product Fig. 6.4. Road map of description sheets

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references to other description sheets) or a complex method. A complex method connects several subprocesses (each of which is again documented as a separate generic process description), which may exchange some byproducts (documented as separate generic product descriptions). The sheets for the generic and specific items look very similar. They have the same fields for entering information. The difference is that in the specific sheets, the fields are filled with project-specific information, while in the generic sheets the fields contain project-independent, generalized information. 6.3.1 Process Description Sheets Generic and specific process description sheets contain several fields to hold the information that is necessary for understanding what the process is supposed to do. Figure 6.5 shows an example of a generic process description sheet. Recipe/Project Name. Every process belongs to a particular level in the experience base and within this level to a particular recipe or project. For generic description sheets, the name of the recipe is noted, for specific description sheets the name of the project is noted. Process Name. The process name assigns each process description a unique identifier, i.e., a text string. The name should be as short as possible but should clearly identify the process. Names of generic process descriptions are subsequently used as references to the generic process description itself. Process Goal. The goal describes what the process is supposed to do, specified by a textual description. The process goal does not state how the process is performed. This information is recorded in the methods. Obviously, the goal of a process is partially contained in the process name. Since the process name should be as short as possible, a more detailed goal specification is often required. Input Product, Output Product, Modified Product. Input products are products that are required during the enactment of the process to achieve the desired result. Output products are products that are produced during the enactment of the process. Usually, the production of these output products is one of the goals of the process. Modified products are products that are changed or extended during the enactment of the process. Usually, the modification of these products is also one of the goals of the process. All products are referenced through the product name (see section 6.3.2). The respective product sheets can be reached by following the included HTML links. Applicable Method. Applicable methods specify a set of alternative methods to implement the process. Typically, a process can be implemented by different methods from which one is selected to perform the process. A method describes how the goal of the process can be achieved. Here, we do not specify the contents


Process Name

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Recipe or Level

Administrative Information

Fig. 6.5. Example process description sheet

of the methods, but only the names of the methods. Each method should be described in a separate sheet that includes all required details (see sections 6.3.3 and 6.3.4). The respective method sheets can be reached by following the included HTML links. Agents. Agents fields specify the type of agents (personnel) who are involved in the process, together with their qualifications and the kinds of the organizations to which they belong. The qualifications that the agent must have in order to execute the respective process is a very important issue. For instance, “experience in GUI development“ is a proper qualification for a programmer involved in the GUI development process. One of the agents involved in the process has to be declared “responsible” for the process.

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Required Tools. The required tool specification lists all kinds of tools that are required to enact the process. For each tool, the most important requirements should be noted as well. For instance, “can import Word documents and export HTML” might be proper requirements for a word processor. Administrative Information. Additionally, some administrative information about a sheet is required. This is important in order to maintain the sheets and to distinguish among different versions. This information is the name of the sheet author, the version of the sheet, and the date of the last change. 6.3.2 Product Description Sheets Products may be software, text documents, graphics, sound, etc. Generic and specific product description sheets contain several fields to hold the information that is necessary for understanding what the product consists of or should consist of. Figure 6.6 shows an example of a generic product description sheet. Module/Project Name. This field mentions the recipe (for generic sheets) or the project (for specific sheets) to which the product belongs. This is the same as for the process description sheets. Product Name. The name should be as short as possible but should clearly identify the product. Product names are used in the process description to specify the input, output, or modified product. Examples of appropriate product names are “Requirements Document” or “CBR Prototype Software.” Product Description. This is a detailed textual description of the product or a reference to a detailed “external” specification. From this description, it should become clear what product must be produced. If necessary, quality standards for the product also can be noted. Administrative Information. The same administrative information is required as for the process description sheets. 6.3.3 Simple Method Description Sheets Typically, there are several ways to enact a process. A method describes one such way. While the process declares WHAT should be done, a method explains HOW to do it. Simple methods describe the “How-To” using a short section of text. If a method needs to be described in more detail, complex method descriptions should be used (see section 6.3.4). Figure 6.7 shows an example of a generic simplemethod description sheet. It consists of the following fields:


Product Name

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Recipe or Level


Fig. 6.6. Example product description sheet

Module/Project Name. This field mentions the recipe (for generic sheets) or the project (for specific sheets) to which the product belongs. This is the same as for the process description sheets. Method Name. The method name should be as short as possible but should clearly identify the method. Names of simple method descriptions are used in the applicable methods section of process descriptions (see section 6.3.1).

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Method Description. The method description contains a “how-to” description about the enactment of the actual process. Such a description should contain all information necessary for the assigned agents to produce the specified products. Experiences from earlier projects can be captured in these descriptions by giving descriptions of “good” examples or warnings of steps that were not successful in the past. In general, this description will be informal, i.e., narrative text. However, if formalisms or schemata exist for structuring such descriptions, they should be used. For example, a simple method that describes the design of the similarity measure could give guidelines on how a local similarity measure should be chosen, depending on the kind of attributes.

Method Name

Recipe or Level


Fig. 6.7. Example simple method description sheet


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Administrative Information. The same administrative information is required as for the process description sheets. 6.3.4 Complex Method Description Sheets Complex methods are necessary when a method needs to be described in more detail. In that case, a method itself can be decomposed into several subprocesses, which interact in a certain way and exchange products. Hence, a complex method consists of several subprocesses and the product flow among them. Figure 6.8 shows an example of a generic complex method description sheet. It consists of the following fields:

Method Name Recipe or Level


Fig. 6.8. Example complex method description sheet

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Module/Project Name. This field mentions the recipe (for generic sheets) or the project (for specific sheets) to which the product belongs. This is the same as for the process description sheets. Method Name. Each complex method has a unique name. The method name should be as short as possible but should clearly identify the method. Names of complex method descriptions are used in the applicable methods section of process descriptions (see section 6.3.1). Method Description. The method description contains a verbal description about the enactment of the actual process, similar to those of the simple methods. It should explain how the subprocesses shown in the “details section” (see below) should be enacted. Details. The details section links to a product flow description for the complex method, like the one shown in Fig. 6.3. This contains the relevant subprocesses, and the created byproducts. Each of the subprocesses and byproducts has its own description on a separate sheet. Thereby, details can be added at several levels of abstraction, as appropriate, to describe the experience. Administrative Information. The same administrative information is required as for the process description sheets.

7. Developing Case-Based Help-Desk Support Systems for Complex Technical Equipment

Help-desks support end-users of complex technical equipment by providing guidance on the use of products and by performing maintenance operations when needed. Since the complexity and diversity of technical equipment increases continuously, help-desk operators have difficulty covering the entire range of relevant products and need a knowledge repository to be able to find the correct solution to a problem as soon as possible. This chapter describes the processes that must be performed to develop a casebased system to support help-desk operators in diagnosing complex technical equipment. It is based on the help-desk recipe from the cookbook level in the INRECA methodology. The recipe was created during the development of the HOMER system described in section 3.1, and has been applied to the development of several other help-desk systems. Using the methodology, we were able to speed up the development process of a case-based help-desk support system by a factor of 12 (see section 7.5). Apart from the increase in productivity, the methodology also resulted in considerable benefits regarding product and process quality, provided a means of communication, and served as a basis for management decisions.


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7. Developing Case-Based Help-Desk Support Systems

7.1 Characteristics of Case-Based Help-Desk Support Systems The ever-increasing complexity of technical equipment makes it difficult for the users of these systems to operate and maintain them without support. While the probability that technical systems will fail grows exponentially with their complexity, the expertise needed to be able to control every feature of such complex systems usually exceeds the resources available to end-users. Help-desks support end-users of complex technical equipment. When end-users have problems, they count on help-desks to provide emergency services. Help-desk operators provide guidance on how to use the system and keep the system operational by performing necessary maintenance tasks. They are expected to be able to solve problems on very short notice, in a very short time, and to be knowledgeable in all areas that are related to the technical system at hand. Help-desk operators use their own experiences to solve most of the problems that are relayed to them. However, as systems become more complex, the areas help-desk operators are experts in tend to diverge, i.e., problem solving experience is distributed among experts and the areas of expertise do not necessarily overlap. Nevertheless, when an end-user has a problem, he or she wants it solved as soon as possible. If that expert is not available, the user has to wait, which is annoying and not acceptable in a commercial environment. The problem-solving experience must be available to every help-desk operator at all times. Current trouble-ticket tools that are being used at help-desks do not provide a means of capturing and reusing problem-solving experience in a convenient and efficient manner. Since problem-solving experience is a corporate asset, it has to be collected, preserved, and used as efficiently and effectively as possible. The best way to achieve this is to use CBR to develop help-desk support systems. The goal of developing a case-based help-desk support system is to create a knowledge repository that contains problem-solving experiences for a complex technical domain that changes over time. This knowledge repository will be used in an organization, by a group of people with varying levels of expertise, in a timecritical operation. It is obvious that the development and use of such a system does not only involve technical processes, but also raises managerial and organizational issues. In the following sections, we describe the tasks that must be performed to develop a case-based help-desk support system and the processes that have to be put into place to make such a system operational.

7.2 Development and Use of Case-Based Help-Desk Support Systems

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Table 7.1. Processes during case-based help-desk support system development and use Managerial Processes

-

Organizational Processes

-

General ITSystem Related Technical Processes

-

Knowledge Repository Related

-

System Development Goal Definition Awareness Creation CBR-Tool Selection Project Team Selection Initial Domain Selection Project Team Training Knowledge Acquisition Process Development Acquisition, Utilization and Maintenance Process Development System Specification System Implementation System Integration System Verification Initial Knowledge Acquisition Core Knowledge Acquisition

System Use - Progress Verification and Controlling - End-User Training - Continuous Knowledge

Acquisition - Utilization Process - Maintenance Process

- Continuous System

Maintenance

- Continuous Knowledge

Acquisition and Maintenance

7.2 Development and Use of Case-Based Help-Desk Support Systems for Complex Technical Equipment Table 7.1 lists the processes that must be considered and performed during the development and use of a case-based help-desk support system. As described in section 6.1.2, we distinguish among managerial, organizational, and technical processes.

7.2.1 Managerial Processes during System Development Goal Definition. For a case-based help-desk support system project to be successful, precise goals must be determined at the outset. This enables management to fix the direction in which the project should develop and to measure the success of the project upon completion. Hard (quantitative) and soft (qualitative) success criteria should be identified (cf. Stolpmann & Wess 1999). Hard criteria are measurable quantities and cover aspects like:

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– problem solution quality (first-call resolution rate, solution correctness, and consistency, average cost of proposed solution, etc.), – process quality (average time needed to solve a problem, average number of escalations needed, quality of dynamic priority assignment, etc.), – organizational quality (speedup in help-desk operator training, flexibility of staffing, cost per interaction, etc.). Soft criteria, on the other hand, measure the subjective quality of the help-desk and cover aspects like: – end-user satisfaction (availability of the help-desk, perceived competence, friendliness, etc.), – help-desk operator satisfaction (workload, work atmosphere, repetitiveness of tasks, intellectual stimulation, etc.), – corporate aspects (preservation of knowledge, publicity, etc,). The goals must be communicated to the project team, and the team has to be motivated to achieve them (see also chapter 4). When project goals are selected, it is important that these goals be realistic both in terms of their time frame and whether they can be achieved with an acceptable amount of resources. The case-based help-desk support project must be seen as part of the long-term knowledge management strategy for the company. Since knowledge increases and evolves, the experience in a CBR system must be maintained continuously. System development is only the initial phase in any CBR project. Awareness Creation and Motivation. The case-based help-desk support system project targets the most precious asset of the employees: their experience. The project’s goal is to collect the problem-solving experience of each relevant employee and make it available to anybody who needs it in the organization. Obviously the help-desk operators will have a motivational barrier to giving away their experience. Every employee knows that “knowledge is power.” In helpdesk environments or domains where experience is being used to solve problems having experience translates into being superior and indispensable, whereas giving away the knowledge can be perceived as becoming obsolete. However, as soon as help-desk operators become part of a project team and understand that sharing knowledge means that they will get back much more than they invest, most barriers disappear. It has to be made clear that the user and beneficiary of the developed system is not going to be an anonymous “company,” but they themselves. They will be able to access the experience of their colleagues and solve problems they could not solve before, as well as end situations in which colleagues constantly pester them for advice. The resulting help-desk system will enable them to work with increased efficiency and effectiveness. Apart from the help-desk operators, management has to be motivated as well. CBR is perceived to be rather academic by most managers. While to them


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investing resources into a database project seems to be no problem, investing into CBR is investing into a venture with an uncertain outcome. It has to be clarified that CBR is an established technology and by no means only an academic playground. It also has to be clarified that the initial installation of the case-based help-desk support system is only the beginning of a process that will enable the company to capture and reuse experience. Management must be prepared to invest resources on a continuous basis while the system is operational. A CBR system is only useful if it contains knowledge and is being maintained on a continuous basis. Without continuous management support and employees who are willing to fill and use the system, any CBR project is bound to fail. CBR Tool Selection. Based on the project, domain, and user-group specifications, a suitable tool to develop the case-based help-desk support system must be selected. Criteria to be taken into account include: – the operating environment in which the system is going to be used (hardware and software, network architecture, database type, etc.), – the complexity of the technical domain (home appliances or networked workstations), – the level of experience of both the end-users and the help-desk operators, – the organization of the help-desk (number of levels, physical locations, etc.), – the project goals that have been defined. Since the case-based help-desk support system is going to serve as a (long-term) knowledge repository for the organization, this selection should be based not only on technical criteria, but also should take into account economic and organizational considerations, as well as strategic decisions of the company. 7.2.2 Organizational Processes during System Development Project Team and Initial Domain Selection. The creation of a project team to serve as the “knowledge engineers” and the selection of a group to act as initial test users of the system are the first organizational steps that must be taken. Apart from the person implementing the case-based help-desk support system (CBR consultant), the project team should contain help-desk personnel who are very experienced in the relevant subdomain to be modeled and well respected by the help-desk operators outside the project group. Personnel that are interested in “technology” rather than implementing useful systems are very common in helpdesk environments, but definitely not helpful in creating a case-based system and should be avoided. Once selected, the members of the group should be kept constant, i.e., fluctuations should be avoided. The group of initial users should comprise two types of help-desk personnel: One that is on a comparable level of expertise with the project team with respect to the selected subdomain (i.e., expert users) and help-desk personnel who are less

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familiar with the specific problem area (i.e., novice users). While the expert testusers can communicate to the project group in their language, the novice users will represent the target group for which the system is being implemented. Feedback from both types of users is required for a successful project. After a first “rapid prototype” has been implemented, the expert users can give hints regarding problems with the knowledge modeled in the system. The members of the novice user group, on the other hand, will serve as models of the help-desk operator who will use the system. The vocabulary in which the cases are being represented and the knowledge contained within them has to be adjusted to the novice user group Which domain one selects for the initial knowledge acquisition is of utmost importance. The domain should be representative of the problems that are being handled at the help-desk, both in terms of complexity and frequency. It should also be a problem area that accounts for a considerable amount of the workload and about which the help-desk operators are interested in sharing (obtaining) knowledge. Training the Project Team. Training the project team is another organizational process that has a major impact on the success of the help-desk project. At the beginning of the project, the project team is (most of the time) inexperienced with respect to CBR and knowledge acquisition. Since the project group will be responsible for system maintenance and continuous case acquisition after the development has finished, it is very important that they are trained in CBR, as well as in knowledge acquisition and modeling, during the initial knowledge acquisition. While the project team should also get advanced training to be able to model, fill, and maintain the knowledge in the system, the test users only need to be trained in using the resulting case-based help-desk support system. Development of the Knowledge Acquisition, Utilization, and Maintenance Processes. The introduction and use of a case-based help-desk support system usually causes a re-evaluation and modification of the existing knowledge and information management processes in a help-desk environment. When the system is used, it must be integrated into the operating environment of the help-desk operators and become part of the standard business process. Existing processes must be altered to facilitate the flow of information to and from the CBR system. When organizational processes are defined, the tasks to be performed, the personnel or roles to perform these tasks, and the communication among the groups/roles have to be fixed. After the development of the case-based help-desk support system is complete, it will serve as the central source of information for the help-desk operators. To ensure a smooth flow of information, the knowledge sources and formats, as well as the qualification of the personnel that requires the knowledge, have to be analyzed, and processes that allow efficient and effective acquisition, use, and maintenance of knowledge have to be developed. One should keep in mind that


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while the group enacting the initial knowledge acquisition process is the project team and rather experienced, the users who use the system in the end (both in terms of knowledge retrieval and continuous acquisition) may be less qualified. During the development of HOMER (see section 3.1), we found it very useful to define three roles for the organizational processes during the use of the help-desk system: – the help-desk operator, – the CBR author, – the CBR administrator. Help-desk operators are the users from the target group. Their duty is to use the implemented help-desk system in their daily work. If they cannot find an appropriate solution with the system, they will have to solve the problem on their own and generate a new case. Depending on the domain and on managerial decisions, this new case may or may not be made immediately available as an “unconfirmed” case to the other help-desk operators. For maintenance purposes, the operators are also encouraged to comment on the quality and applicability of the cases in the case base. The unconfirmed, new cases have to be verified in terms of their correctness and suitability for the case base by the CBR author(s). The CBR author is a person with experience both in the domain and in using the CBR system. While the CBR author can decide on the quality and inclusion of a case in the case base, he or she is not allowed to perform modifications on the vocabulary, the similarity, and the adaptation knowledge. These can only be performed by the CBR administrator. The long term maintenance of the knowledge in the system is performed in cooperation by the CBR authors and the administrator (see section 7.3.2.) The personnel enacting the roles of the CBR author(s) and the CBR administrator should be included in the project group from the start of the project. It should be noted that both these roles require a considerable amount of resources and should be performed by dedicated personnel. If the organization or the size of the help-desk does not permit dedicating more than one person to these tasks, the duties of the CBR author and CBR administrator should be performed by one person. 7.2.3 Technical Processes during System Development General IT-System Development Related Processes. The development of a casebased help-desk support system is similar to any other IT project in most aspects. As usual, the system has to be specified, implemented, integrated, and verified. The definition of the requirements, the implementation, and the testing and revision of both the prototype and the actual case-based help-desk support system are steps that have to be performed in accordance with standard software

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engineering techniques. However, the user-interface and the connection to supporting programs (integration) are two features that require additional attention. The essential task in developing a user interface is to present the relevant data, at the right moment, in the right representation, and on a level of abstraction that is suitable for the current users of the system. If the available data is presented to the users in a way that they do not understand, in a representation they are not familiar with, or at a moment when it is irrelevant, it will only cause confusion and be of no use. While the exact specification of the firmware installed on a printer may be necessary information for a second-level help-desk operator, it will be rather useless for a first-level help-desk operator who is just trying to figure out whether the printer is connected to the computer. The user interface of the case-based helpdesk support system has to be developed in accordance with the user group (i.e., second level, first level, or even end-user), the specific domain, and company policies (who is allowed to see what kind of data). It has to present the right data, at the right moment, on the right level of abstraction, and in accordance with company policies. A case-based help-desk support system cannot operate in isolation. While the CBR system will store experience, it will not contain data regarding device configurations, maintenance contracts, and users. Maintenance information and device configurations are stored in an inventory system most of the time. Data regarding the end-users is usually stored in another, separate database. Since this information is needed during problem solving, the system has to have interfaces to these databases. Most help-desks use trouble-ticket tools in their daily operations; they record, manage, trace, escalate, and analyze the calls they receive. While these troubleticket tools are very useful in handling calls, they do not provide means to capture and reuse problem-solving experience. Depending on the environment, the casebased help-desk support system should also either be integrated into the user interface of the trouble-ticket tool or vice-versa. Data from the trouble-ticket system has to be transferred to the CBR system to initialize the attributes that relate to the data that has already been acquired. Except for very complex second-level applications, it is not feasible to have two points of entry to the problem-solving process. Initial Knowledge Acquisition for the Case-Based Help-Desk Support System. A CBR system is useless without cases. When the case-based help-desk support system is handed over to the help-desk operators, it has to contain enough cases to cover at least part of the relevant problems at the help-desk. Otherwise the system will be considered useless and the project will fail. Initial knowledge acquisition serves three major goals: – training the project team in knowledge acquisition, – initializing the knowledge in the system, – collecting enough help-desk cases to bootstrap the system.


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During initial knowledge acquisition, the knowledge in the system can be distributed among the domain model (vocabulary), similarity measure, adaptation knowledge, and the case base. These knowledge containers (Richter 1998) have to be created and filled. While the similarity measures, the adaptation knowledge, and the vocabulary are compiled knowledge, the case base is interpreted at runtime. In principle, each container could be used to represent most of the knowledge. However, this is obviously not very feasible, and the CBR consultant should carefully decide on the distribution of knowledge into the containers. After the initial knowledge acquisition is completed, this distribution is more or less fixed and should only be changed with caution. The processes for the acquisition of knowledge for each container run in parallel and cannot be easily separated during the initial knowledge acquisition. Since the vocabulary lays ground for entering the cases and describing the similarity measures and adaptation knowledge, it has to be available first. However, to be able to create a domain model (i.e., the vocabulary), one has to understand how the domain is structured, and this can only be done by looking at the cases, the similarities, and the adaptation rules. In our experience, the best way to approach this problem is to create and use standardized forms to acquire an initial amount of cases from the project team. The form should be developed in co-operation with the project team. A sample form that was developed for the initial case acquisition for the HOMER (cf. section 3.1) application is shown in Tab. 7.2. The first thing that must be done is to ask the project team to fill out as many case acquisition forms as they can. By looking at the elements of the forms, the vocabulary (i.e., the phrases that have to be used and the domain structure) can be derived and a vocabulary that is capable of describing the cases that have been on the forms can be modeled. By asking the project team what the range of possible values for each attribute on the forms is and inquiring what would have happened if one of the values on a form were different, a broad range of cases can be created and the vocabulary expanded in a short time. Discussions among the project team members raise the level of understanding of both the approach and the problems, and should be encouraged in this early phase. One should also keep in mind that the goal is not to model the domain in every detail but on a level that helps the system’s user solve a problem (i.e., the system does not have to solve the problem autonomously). Especially during initial knowledge acquisition, it is advisable to have more cases on an “everyday” level rather than having a few extremely specific ones. While the initial vocabulary is being created and value ranges fixed, questions regarding adaptation rules and similarities should be posed and the results entered into the system.

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Table 7.2. Sample form for initial case acquisition

Problem Nr : 0816 Author: S. Itani

Homer Case Acquisition Date: 26.04.99 Verified by: J. Fleisch

Problem Description (Failure) Reason (Fault) Solution

Printer does print pages full of gibberish File is Postscript, Printer does not understand PS Send File to Postscript Printer, delete file from queue

What did you check to find out what the problem was ? Printer Model File Type

HP LJ 6L Postscript

Other Notes:

The reverse of this problem did also happen, somebody sent a PCL file to a pure PS printer

One of the major challenges one must face when creating a system to capture and represent the experience of domain experts, is determining the level of abstraction with which the domain and the knowledge will be modeled. If the model used is too simplistic, it will cause problems while the experience is being captured and will miss important details. If, however, the domain model is too specific, the user will get lost quickly in useless details, and knowledge acquisition will be very tedious and time consuming. Maintenance is very difficult for both a too-simplistic and a too-complex model. The system’s intended users also influence the decision to use a structured domain model approach as opposed to a textual query-answer-based approach. For inexperienced help-desk operators, a tool with which simple problems can be solved by answering a limited number of questions is of great value (Thomas et. al. 1997). However, for experienced help-desk operators who would not bother to use a system for (subjectively) trivial problems, a structured domain model approach yields better results. The system will be able to present the not-so-obviously similar solutions that the help-desk operators could not find. Since knowledge contained in the domain model is used in similarity calculation, the retrieved solutions will be similar in a semantic and structural manner. The domain model allows the solutions in the case base to be applicable to a broader range of problems. The cases in the help-desk domain should be modeled in accordance with the approach the help-desk operators use in solving problems (Fig. 7.1).

7.3 Using the System

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The Problem Description is the first information the help-desk operator gets from the end-user. This description is what the end-user subjectively perceives as the problem. It may or may not have to do with the actual cause of the failure. The System Description contains information regarding the system itself as well as the operating environment. It consists of the questions the help-desk operator must ask or the information he or she must obtain from various sources to arrive at a diagnosis. The system description contains the necessary and sufficient amount of information needed to diagnose the problem. The Solution contains the fault, i.e., what caused the problem, and the remedy, i.e., how to solve the problem. Depending on how the system is implemented and what statistical information is needed for further evaluation, some additional, administrative data may also be added to the case description. Each complete path from problem description to solution makes up one case. Once the cases from the initial forms have been entered into the help-desk system, the system should be shown to the project group to verify the results it delivers. Afterwards the initial knowledge acquisition can continue as more cases are entered from additional forms and the knowledge containers are incrementally updated. Initial knowledge acquisition takes place in two steps. During the first, preliminary knowledge acquisition, the cases for the prototype of the case-based help-desk support system are collected. While the collected cases will help to initialize the knowledge containers and train the project team, the collection of the “core” cases for the system should be done in a second step, the core knowledge acquisition. Nevertheless, the approach that is used in both processes is similar.


7.3.1 Managerial Processes during System Use Project progress with respect to the qualitative and quantitative criteria selected as project goals must be monitored constantly during system development and use (cf. Stolpmann & Wess 1999). Regular project reviews should take place. Standard project planning and controlling techniques can and should be applied to casebased help-desk support projects.

Problem

Diagnosis Path

Solution

Fig. 7.1. Basic structure of a help-desk case

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Measuring the impact of the help-desk system on the efficiency and effectiveness of the target group (increase in first-call problem resolution, decrease in problem solution time, etc.) and making the results available to both the project and the target groups will motivate the help-desk operators to use the system and help uncover deficiencies. 7.3.2 Organizational Processes during System Use Knowledge Acquisition, Utilization, and Maintenance Processes. The knowledge acquisition, utilization and maintenance processes that have been defined during system development have to be enacted during system use. The use of the case-based help-desk support system contains the Application Cycle in which the system is used by the help-desk operator and the Maintenance Cycle in which the system is maintained by the CBR author and the CBR administrator (see section 7.3.3, Fig. 7.2). During the application cycle, the cases that are stored in the case-based helpdesk support system are being used to solve problems. Even if no new cases are being acquired during this cycle, statistical data regarding the quality and usage of the cases (last retrieval time, last application date, success rate etc.) can be collected. This data can be used to determine the quality of the cases and for maintenance purposes. Whenever a help-desk operator decides that the proposed solution is not appropriate, a new case has to be entered into the case base. However, since the quality of these cases varies according to the user entering them, they cannot be transferred to the case base without being verified by the CBR author. This is done in the maintenance cycle. The extension and maintenance of the case base is the duty of the CBR author and the CBR administrator. Training the Help-Desk Operators. Just as the test-users were trained during the project team training, the help-desk operators have to be introduced to the basics of CBR technology and the developed case-based help-desk support system. Since the operators are going to participate in the continuous acquisition of knowledge, standards on how to store cases have to be introduced and taught. Feedbackchannels also should be created and introduced during this training. 7.3.3 Technical Processes during System Use Continuous Knowledge Acquisition and Maintenance. The knowledge contained in a case-based help-desk support system is an incomplete model of the domain in the real world. Whenever the real world changes, the model in the system has to be updated. The necessity for changes in the model may either arise from real changes in the world or from the learning effect associated with using the


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Reuse

Retrieve

Application Cycle

Revise

ReCycle

Retain

Maintenance Cycle

Refine

Fig. 7.2. Use of the case-based help-desk support system

case-based help-desk support system. By learning, the system improves the model’s coverage of the real world. Since the model is incomplete by definition (and no such thing as a closed world exists in the real world), with growing knowledge, updates in the knowledge containers will be necessary. While nobody would consider purchasing a database system with the assumption that it would continue to work without any maintenance at all, there seems to exist a misconception about knowledge-based systems in this respect. All concepts used for maintaining database systems are also applicable to knowledgebased systems. However, because of the semantics associated with the information in knowledge-based systems, additional maintenance operations are necessary. Learning and changes in the real world can make maintenance necessary for each knowledge container. A case-based help-desk support system comprises two linked process cycles: the Application Cycle and the Maintenance Cycle (see Fig. 7.2). The Application Cycle takes place each time a user solves a problem with the case-based help-desk support system. During the application of the CBR system, the standard tasks Retrieve, Reuse, and Revise must be performed (see section 2.6). During Retrieval the most similar case or cases in the case base are determined based on the new problem description. During Reuse the information and knowledge in the retrieved case(s) is used to solve the new problem. The new problem description is combined with the information contained in the old case to form a solved case. During Revision the applicability of the proposed solution (solved case) is evaluated. If necessary and possible the proposed case is repaired. If the case solution generated during the reuse phase is not correct and cannot be repaired, a new solution has to be generated by the help-desk operator. The solution that has been retrieved by the system or created by the help-desk operator

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is put to use during the Recycle task. The Application Cycle is performed by the end-user of the system (help-desk operator). Whenever a new solution is generated during system use, this case is stored in the case buffer, made available to all help-desk operators as an “unconfirmed case,” and sent to the Maintenance Cycle. These operations as well as the maintenance cycle are not visible to the standard help-desk operator. The Maintenance Cycle consists of the Retain and Refine tasks. While the Application Cycle is executed every time a help-desk operator uses the CBR system, the Maintenance Cycle can be executed less frequently, i.e., only when there is a need for maintaining the system or at regular intervals. During the Retain task, the CBR author checks the quality of the new cases that were generated by the helpdesk operators and stored in the case buffer. The CBR author verifies and approves the representation and content of each case. In terms of representation, the cases should: – contain the information that is necessary and sufficient to solve the problem, – be described on an abstraction level that is appropriate for the system’s end-user. The content is verified by checking whether the case is: – correct, – (still) relevant, – applicable. During the Refine phase, maintenance steps for the knowledge containers are performed by the CBR administrator. The case base, vocabulary, similarities, and adaptation knowledge have to be refined, and quality-decreasing effects of external changes in the domain, as well as the inclusion of new cases in the case base, have to be counteracted. The goal of the Refine task with respect to the case base is to keep the case base correct, to have maximal coverage of the problem space, and to have no redundant cases. After each case has been validated in the retain task, their suitability for inclusion in the case base has to be determined. Before a new case is taken into the case base, it must be checked to see: – whether it is a viable alternative that does not yet exist in the case base, – whether it subsumes or can be subsumed by an existing case, – whether it can be combined with another case to form a new one, – whether the new case would cause an inconsistency, – whether there is a newer case already available in the case base. The operations that have to be performed during case base maintenance vary depending on the application domain and the vocabulary that is used to represent the cases. Recent research on maintenance issues in CBR has addressed this issue in much detail (Leake & Wilson 1998, Smyth & McKenna 1998, Surma &

7.4 Process Model for a Help-Desk Project

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Tyburcy 1998, and Racine & Yang 1997, Nick et al. 2001, Reinartz et al. 2001, Wilson & Leake 2001). Both the inclusion of new cases and changes in the domain may have an effect on the validity and quality of the compiled knowledge containers (vocabulary, similarity, adaptation knowledge) as well. The maintenance of these containers is also performed in the Refine step. Since changes in the vocabulary can cause information in the cases to be no longer available or missing (e.g., attributes can be added and deleted, classes can be moved) maintenance of the vocabulary should be performed with utmost caution (Heister & Wilke 1998). It should be noted that the refinement of the knowledge containers does not necessarily have to be triggered by external events but may also be performed through introspection. By analyzing the content of the knowledge containers, more efficient ways to structure the domain, adaptation rules, and similarities, as well as new cases, can be discovered or derived. While maintenance operations for the case base can be performed by the CBR author, maintenance of the vocabulary, the similarity, and adaptation knowledge should only be performed by the CBR administrator. General IT-System-Related Processes. Once the case-based help-desk support system has been put into operation, it has to be debugged, monitored, and updated continuously. The necessity for updates does not necessarily have to come from the help-desk system itself, but may also be initiated by changes in the (IT) environment. Since these processes are not CBR-specific but apply to IT systems in general, we refrain from going into their details here.

7.4 Process Model for a Help-Desk Project The overall process for the development and introduction of a case-based helpdesk support system can be divided into six main phases: – project planning and initialization, – implementation of a rapid prototype, – evaluation and revision of the prototype, – implementation of the integrated case-based help-desk support system, – evaluation and revision of the case-based help-desk support system, – utilization of the case-based help-desk support system.

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During Project Planning and Initialization, the preliminary requirements for the execution of the project are fulfilled. The project goals are defined, the project information is disseminated, the project team is created and trained, and the CBR tool is acquired (Fig. 7.3). The Implementation of the Rapid Prototype enables the project team to test the validity of the goals and the requirements set forth in the project-planning phase. It also serves to train the project team in knowledge acquisition techniques and in using the CBR tool. The processes that will be used during knowledge acquisition can be defined and refined. The “Rapid Prototype Implementation,” “Preliminary Knowledge Acquisition,” and “Knowledge Acquisition Process Development” processes are closely interconnected and influence each other. While changes in the prototype will effect the way knowledge is acquired, changes in the domain model will effect changes in the acquisition process as well as the user interface of the system, etc. (Fig 7.4). The interaction between processes is shown with dotted lines in the figures.

Project Vision

Goal Definition

Awareness Creation and Motivation

Project Goal Description

Project Information

Initial Domain Selection

Project Team Selection

Requirements Definition

Domain for Initial Implementation

Project Team

Requirements Definition Document

Project Team Training

CBR-Tool Selection

Trained Project Team

CBR-Tool

Fig. 7.3. Project planning and initialization

7.4 Process Model for a Help-Desk Project



CBR-Tool

Rapid Prototype implementation

Preliminary Knowledge Acquisition

Preliminary Case-Base

Knowledge Acquisition Process Development

Rapid Prototype

Knowledge Acquisition Process Model

Fig. 7.4. Implementation of a rapid prototype



Rapid Prototype

Test of Rapid Prototype

Rapid Prototype Test Report

Requirements Revision

Revised Requirements Document

Fig. 7.5. Evaluation and revision of the prototype

Initial Test Users

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During the Evaluation and Revision of the Prototype (Fig. 7.5), the initial users (see section 7.2.3) evaluate the developed a rapid prototype as well as the structure and content of the knowledge containers. The results are collected in a revisedrequirements definition document, which serves as a basis for the development of the actual system. The Implementation of the Integrated Case-Based Help-Desk Support System contains the development of the actual system to be used at the help desk. The implementation is based on the preliminary case base, the revised requirements, the rapid prototype, input from the project team, and the process model for knowledge acquisition. While the system is implemented, the project team acquires the corecase base to be deployed with the system after implementation is complete. The processes to be used when the system is used are also developed in close connection with the implementation and the knowledge acquisition in this phase (Fig. 7.6.). During the Evaluation and Revision of the Case-Based Help-Desk Support System the system is evaluated by the initial users and revised according to the results of this evaluation (Fig. 7.7.). System phase. The impact of the system is continuously monitored and feedback is given to developers and users (Fig. 7.8). Knowledge Acquisition Process Model




Rapid Prototype

Implementation of Integrated Case-Based Help Desk Support System

Core Knowledge Acquisition

Core Case-Base

Utilization Process Development

Integrated Case-Based Help-Desk Support System

Utilization Process Model

Fig. 7.6. Implementation of the integrated case-based help-desk support system

7.5 Impact of the Methodology

Core Case-Base


Case-Based Help-Desk Support System

Test of Help-Desk System

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Initial Test Users

Help-Desk System Test Report

Revision of Help-Desk System

Revised Integrated HelpDesk System

Fig. 7.7. Evaluation and revision of the case-based help-desk support system

7.5 Impact of the Methodology in Developing and Using HelpDesk Support Systems The process models shown above were created during the development of the HOMER application (see section 3.1). One of the goals of the implementation of HOMER was to observe and record the processes that have to be enacted during system development and use. The results of these observations have been documented as a recipe in the cookbook level of the INRECA methodology. The process models shown above have been extracted from this recipe. In the course of developing the methodology, we documented the tasks that were performed, their duration, the resources needed, and the outcome of the tasks. While monitoring our own actions, we discovered redundancies as well as dependencies among processes that were not obvious. This enabled us to structure, optimize, and understand some of the processes better. Even the development of the methodology had an impact on the way the project progressed.

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Knowledge Acquisition Process



Core Case-Base

Integrated Case-Based Help-Desk Support System

Utilization of Case-Based Help-Desk Support System

Continuous Knowledge Acquisition

Case-Base

Continuous System Maintenance

Optimized Held-Desk

Operational System

Progress Verification

Feedback to Development and Help-Desk

Fig. 7.8. Utilization of the Case-Based Help-Desk Support System

After the process models were complete, the methodology and the developed tools were used during the project definition, application development, and system utilization phases of new projects. As described in chapter 5, the methodology has an impact on productivity, quality, communication, and management decision making. We could observe the advantages of using the methodology in each of these areas and in all three project phases, both to the customer (management and user) and to the developer.


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7.5.1 Impact of the Methodology during Project Definition During the definition of a project, the processes to be executed have to be detailed. For each process, this involves defining the methods to be used, the project’s duration, the resources needed, the results to be produced, the interaction with other processes within the project, and the sequence in which the processes must be executed. By means of the methodology we were able to create project definitions with structured process models, task definitions, roles and responsibilities, duration, and resource allocations. The methodology enabled us to make the case-based helpdesk support system development process traceable both to the customers and to ourselves. While the creation of the project definition for HOMER from scratch took us about three months, we were able to reuse the help-desk recipe to define three new projects in less than a week each. The overall duration of these projects varied between six months and three years. Since we reused a recipe that had been executed before, we could be sure that all aspects that needed be taken into account were covered. Since the basic “recipe” was available, we could concentrate on the peculiarities of each domain. The quality and level of detail we were able to achieve in these project descriptions were far beyond the level that can be achieved when a description is created from scratch. Apart from these advantages in increased productivity and quality, the methodology was also very useful for communicating with the customer and conveying the message that the development of a CBR system is not an art, but, rather, solid technology. As we mentioned above, Case-Based Reasoning is still considered an academic approach by some managers. Being able to describe each process in the development of a case based system in detail enabled us to convince managers of the validity of the approach, clarified the need for continuous maintenance and resource allocation, and let us raise realistic expectations. We were able to give customers figures on how much effort had to be spent by whom and when. Project progress became measurable. This gave them a basis for making decisions, enabled them to plan their resource allocation in advance, and prevented the loss of critical resources in the course of the project. 7.5.2 Impact of the Methodology during System Development The detailed project plan that was created during project definition served as a guideline during the development of the system. The ability to trace a structured path and the use of the software tools that was developed to support the methodology allowed us to speed up the development process by a factor of 12. While the development and testing of the first prototype in HOMER and comparable applications took up to six months from the initiation of the projects,

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we were able to create a prototype and test it at another site of DaimlerChrysler within two weeks. Since the tasks that have to be performed and the results that have to be achieved during the implementation of the system were described in detail in the “recipe” and the project definition, some tasks could be transferred from one developer to the other without additional effort, and the progress that was achieved during the project could be measured. A clear-cut definition of the tasks to be performed and the availability of tools for creating and maintaining a domain model allowed us to use less-qualified personnel during the development of the system without compromising the quality of the resulting product. This allowed both the developer and the customer to optimize the allocation of personnel resources. 7.5.3 Impact of the Methodology during System Use During the development and initial use of HOMER, we had to define and modify the tasks and qualifications of the personnel needed to operate the system several times. Since the processes for the acquisition, use, and maintenance of the knowledge in the case-based help-desk support system are defined in the methodology, we were able to introduce new help-desk systems in a much more efficient manner. While the domain modeling and maintenance task could only be performed by a highly qualified help-desk operator in the HOMER project, the domain modeling and model-maintenance tools, as well as the similarity editor that was developed to support the INRECA methodology, enabled us to use much-lessqualified personnel in the ensuing projects. The detailed definition of the duties that have to be performed and the qualification that is needed for the project group also enabled the customers to allocate the necessary resources in advance and monitor the status of the project according to the goals that had been set when the project was initiated. The methodology was also used to train users in the administration of a casebased help-desk support system. Using process charts, we explained the overall structure of the project to the operator who would be in charge of the project after it was completed. This allowed the operator to maintain and use the system without getting lost in details and neglecting important aspects, like maintaining the case base.

7.6 Conclusion To develop case-based help-desk support systems that will be used in dynamic, corporate environments by a large group of users, one must take into account the managerial, organizational, and technical processes. It has to be kept in mind that

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once a CBR system is in place, continuous knowledge acquisition and maintenance is necessary. Processes for knowledge acquisition and maintenance have to be developed and put in place, and personnel have to be dedicated to performing these tasks. The processes necessary to develop a case-based help-desk system have been described in a recipe of the cookbook level in the INRECA methodology. The methodology structures the development and use of case-based help-desk systems, makes it transparent, traceable, and its success measurable. Using the INRECA methodology in the project definition, system development and system utilization phases of new projects resulted in considerable increases in productivity and quality. The methodology also provided a means of communication and served as a basis for management decision making.

8. Developing Intelligent Catalog Search Applications

This chapter describes the recipe for building catalog search applications that incorporate case-based reasoning. The concept of finding a solution to a problem based on the similarities between the new problem and recorded cases in a case base is central to the CBR approach. When developing an intelligent catalog search application we simply treat the product list as the case base and the user’s requirements for a product as the new problem. The recipe for building catalog search applications, therefore, shares many of the characteristics of help-desk and classification applications. We will describe the detail of the recipe and how the methodology has impacted on the practical issues associated with building a new application.


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8.1 Characteristics of an Intelligent Catalog Search Project In recent years, the traditional paper-based publishing of product or service catalogs has given way to the electronic delivery of catalog information. CD-ROM allows an organization to deliver a more functional catalog for a much lower production and delivery cost than could be derived from a weighty paper-based catalog. The Internet extends this to support the delivery of up-to-the-minute product information to the ever-increasing Internet population and, in turn, can capture valuable market information from the online use of the catalog. The problem faced by companies who use electronic catalogs has changed from “How do we efficiently get our information to users?” to “How do they use it when they get it?” The main problems users experience can be summarized by the following statements, familiar to all catalog users: “Results 1-10 of 426, Please refine your search,” and finally “Sorry, no match found!” The technologies whose applications generate these messages are equally well known, i.e., databases. Commercial relational databases support Boolean (yes or no) queries. In other words, if the database doesn’t have exactly what you want, then it has nothing that you want. Similarly, if it has many items that meet your request, then it presents them as a list without ranking. The last one in the list could be the ideal product to meet your needs. The approaches to catalog searching most commonly used are: keyword search, stepwise search, and parametric search. Keyword search. This is the most basic and generally the least effective. It relies on matching words entered by the user with words contained in the product specification or description. For example, if a user is searching a database of properties and types Washington in the keyword search, he or she may get properties in Washington State or Washington, D.C, properties near an airport with regular flights to Washington, properties for whom the agent is John Washington Realty, and many more than we can imagine. Stepwise Search. Stepwise Search presents a hierarchy of product categories to the user. The user gradually refines the search by going deeper into the product tree. Yahoo1 is an example of such a search on the Internet, and Krakatoa ™ (Neal 1997) from Cadis Inc. provides a tool for developing such systems for CD-ROM and Internet catalog searches. Parametric Search. This kind of search is common and is often combined with keyword or stepwise searches to improve functionality. In this approach, the user enters one or more values for attributes of the product. Attributes of parameters can generally be numeric or symbolic. A parametric search acts like a filter and is, in effect, the same as a database query; it either meets all search criteria exactly or it 1

See www.yahoo.com

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fails. Some parametric search systems provide ranges and sets of values to overcome the limitations of the database approach, but still “No match found!” is a common occurrence. Some implementations of this approach use the product code and manufacturer’s code as the parameters, which means that the user must know the product he/she wants before starting the search. The fourth approach, and the subject of this chapter, is the Nearest Neighbor method of searching, also called Intelligent Parametric Search. This approach is a key output from CBR research and is the basis for the INRECA Catalog Search applications. The characteristics of the problem domain that make “nearest neighbor” so effective are as follows: – Users need to search the product space in its entirety. – Products in the product space will be similar is terms of their characteristics and their structure. – The user generally does not need an exact match; the optimum match will suffice. – The user needs the ability to negotiate with the search system. – No match found is an unacceptable outcome from a search (i.e., “sellers want to sell”). Human assessment of similarity or closeness is a normal thinking function and in many cases an automatic one. A sales person interacting with a customer in a real scenario will not take the customer’s requirements as absolute unless told so by the customer. The sales person will treat these requirements as a goal or ambition for the customer and will try to establish the customer’s priorities before making a recommendation. This allows the customer to give a precise statement of requirements but not have it interpreted as such. The intelligent parametric search does exactly this and uses weights to represent user priorities.

8.2 Vertical Platform for Catalog Search Through experience in building a variety of catalog search applications that make use of CBR technology, we can now define a vertical platform for this type of application. The platform describes the tools and methods used to build an intelligent catalog search application, as well as the characteristics of the eventual end-user system. 8.2.1 Case Structure and Domain Model In general, the case structure for a given application is defined by the product specifications in the existing catalog or in the available product literature.

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Therefore, the case structure is well known in advance, since it relies on an existing set of products whose characteristics are known (contrary, for instance, to a diagnosis application where one must make an intensive effort to “discover” the domain model). In many cases we have found that a flat structure is sufficient to meet the search needs of most end-users. By flat we mean that all attributes of the product of interest are used to describe every product in the catalog. The alternative to a flat structure is an object structure. As an example, an object structure for a car might see its attributes as [body, engine, wheels, electronics] where body would contain [color, style, doors, etc.] and engine would contain [cc, fuel, hp, etc.], while a flat structure would contain [color, style, doors, cc, fuel, hp, etc.], all at the same level. The object model allows us to describe a structure for a more complex product representation, and this is supported by the CBR tools. However, an object model structure means a more complex interface for the user, and this, unless necessary, should be avoided. A flat representation can simplify the user’s view of the catalog search and in most cases give the same level of functionality as the object representation. A flat model also can be stored easily in a standard database system, of which many are available. The process of defining the domain model involves specifying and applying data types for each of the catalog attributes. Basic data types are: – – – – – –

Boolean, Date, Integer, Real, String, Symbol.

These attribute types may need to be specialized to cater to the needs of a specific project. As an example, in the Analog Devices search application, the Temperature Specification for each device can be listed, in a simplified way, as one of [Commercial, Industrial, Military, Space]. The order of these values is important, as it demonstrates an increasing scale of Temperature Specification. Therefore, in this project we created a type “Temp_spec_type,” which is an ordered symbol with the set of possible values defined as shown in Fig. 8.1. 8.2.2 Defining Similarity The assessment of the similarity between one product and another, and indeed between one value for a parameter and another, is always likely to be subjective. The expert’s view of similarity will differ from the customer’s, and, as a result, the effectiveness of similarity calculations is best seen in the appropriateness of the results. In each of the intelligent catalog search applications developed to date, it

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Fig. 8.1. Creating a specialized type

was necessary to go through a process of experimentation with the similarity functions. In other words, standard (simple) similarities did not work, and a set of tailored similarity functions had to be defined. See section 3.2 for an overview of the Analog Devices application. 8.2.3 Customer Dialogs The dialog with the customer is the key to a successful implementation of an intelligent catalog search application. Some questions need to be answered before a developer can prepare the dialogs to support the customer in using the search system. Does the customer need to weight the parameters for which he or she specifies values? What validation is required for inputs to the search? What explanation of the search parameters is needed? What explanation of the results is needed by the user? What extra features will the user need to make use of the query or results featured in the system? Each of these questions is important, and the answers can only be obtained by consulting users directly or by consulting people who deal with user queries on a daily basis. As a consequence, the catalog application should be open to validation based on feedback from its use in the “field.” This step is important to ensure the catalog’s long-term acceptance and success. 8.2.4 Integration with Product Database The product database, if it exists, is a starting point for the creation of a catalog search system. The CBR system can use the database directly in two ways:

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1. to generate the index for the nearest neighbor search. This index uses only the parameters from the database that are relevant for the search. The creation of the index involves combining the model, data, similarity measures, and pre-defined weights into a fast, searchable structure. When the database changes, this index must be regenerated to include the new products or items in the catalog. 2. to display the results of the search. On completing a search of the case base, the search returns a reference to a record in the database that contains the detailed product information and link to literature, if required. On the Analog Devices CD-ROM, this is used to launch Adobe Acrobat and load a detailed data sheet of the electronic device of interest. On the IRSA Expertise search it is used to load an Active Server Page with details of the scientist and links to his or her personal Web pages (see Fig. 8.2). 8.2.5 Web Interface The Web opens up enormous possibilities for the presentation of product information to an international customer base. At the moment, most Web sites provide basic database or keyword searches that in many cases reflect negatively on the Web site because of poor-quality search results. The capability of the nearest neighbor search to use all parameters and still return a result means that Web interfaces can now be more dynamic and supportive to the user.

Fig. 8.2. IRSA search results dialog on the Web

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Instead of discouraging the user from specifying all his or her needs, we can encourage the user to “tell all.” The impact of this is that, following a search using CBR, we have a log of the detailed requirements from each user. Over time, this log represents invaluable market information and a basis for reviewing the product catalog contents and presentation. Web interfaces to the intelligent search server can be created easily in HTML, Active Server Pages, XML, or Java. The INRECA project has produced tools to support the creation of these interfaces.

8.3 Process Model for Catalog Search The recipe for developing catalog search applications breaks down into five main processes linked as shown in Fig. 8.3. These processes are now described. 8.3.1 Requirements Acquisition The process of acquiring the user and business requirements for the catalog search application involves three key activities: analyzing technical and sales materials, eliciting user requirements, and, finally, eliciting business requirements (cf. Fig. 8.4). CATALOG SEARCH Author: Sean Breen Version: 1.0 Date: 18/03/99

Product Literature

Requirements Acquisition

User Requirements

Requirements Acquisition Mtd

GUI Development Method

GUI Development

Graphical User Interface

Integrate GUI and CBR Engine

Case base

Case Base Development Implement CBR Engine

code the GUI

Running CBR Engine

Similarity Measures Integrated System

Case Base Development Mtd Select and test CBR engine

Fig. 8.3. Process model for catalog search application

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Analyze Technical and Sales Materials. An analyst knowledgeable in data modeling and CBR should be assigned to gather and review all available materials used internally or externally to describe the products or services of interest. Material may be available in several forms: product database or catalog, sales literature, technical data-sheets, design specifications, and Web documents. It is possible that the detailed information in these forms will conflict. We have noticed that sales literature often presents a simplified view of the product specifications, making it unsuitable for an experienced or knowledgeable customer. Elicit User Requirements. The intended application user ,or stake-holder, should be interviewed and asked to describe the following: – – – –

current work practices in the application area, perceived problems or areas that need improvement, his or her vision of what the best possible situation would be, his or her relationships with other staff members in and out of the area.

From a practical point of view, the team of people involved in this process should include an analyst knowledgeable in data modeling and, ideally, in CBR, a sales person from the company who provides the products or services listed in the catalog, and a technical person who understands the makeup and specifications of the products or services under discussion. The sales person is expected to have an understanding of the customer’s needs, the ways in which they currently use company information when selecting a product, and the best way to meet their needs using a CBR catalog search application. The technical person is expected to Requirements Acquisition Process

Requirements Acquisition Method

Analyse technical and sales materials

Elicit user requirements

Elicit business requirements

read and highlight features

interview sales expert

interview business manager

Fig. 8.4. Process decomposition for requirements acquisition


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be in a position to describe how individual products or groupings relate to each other functionally and how values for individual parameters or attributes can be compared to each other. The analyst will interview each of these professionals and use the information to establish the data and interface requirements for the catalog search application. Elicit Business Requirements. Sales support is a key business function, and the input of an experienced business manager to the creation of an electronic sales support application is essential at this stage in the process. Many possibilities exist to enhance the sales process when a decision is made to create a CBR Catalog Application. An experienced business manager will be in a position to highlight these opportunities and to make the business case for focussing on them during the course of development. 8.3.2 Case Base Development Development of the case base is a three-stage activity: descriptive model development, similarity development, and case acquisition (see Fig. 8.5). Descriptive Model Development. This will draw on the outputs of the requirements acquisition process to define a list of attributes and organize these into a class hierarchy. Different tools may provide for different types of data and will certainly have different ways of implementing the descriptive model. It is important to ensure that the tool you choose to implement your application supports the model you have describe. Similarity Development. This is not broken down in this diagram, since it is a well-developed, common generic process to almost all CBR projects. See also (Bergmann & Stahl 1998, Bergmann 2002). Case Acquisition. This is essentially either a manual input operation or a question of importing existing data. Importing electronic versions of the case base is certainly the most likely scenario in a product catalog application; however, there may be a procedure put in place that allows for manual additions to the case base once the initial import is done. CBR tools such as Kate and CBR-Works both support importing cases from database or ASCII files, and both support the CASUEL (Manago et al. 1994) case format defined by INRECA.

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Case Base Development

design of domain specific implementation

Descriptive Model Development

Similarity Development

Implement descriptive model in tool

Case Acquisition

Manual Case Acquisition

Find Attributes

Class Modelling

produce list of attributes and types

design of class hierarchy

case import

Fig. 8.5. Case base development process decomposition

8.3.3 GUI Development The development of a Graphical User Interface (GUI) for your catalog application is likely to consume a large part of the development work for your project (see Fig. 8.6). This is where the technology meets the technophobe. An application without a user interface designed for real users will almost certainly run into problems when it goes live. This is not wisdom gleaned from CBR projects but a broadly accepted IT maxim: “Ignore the user at your peril.” Where possible, involve users early in the design of the GUI. Get their feedback when creating the graphics for the application and show them screen-shots of the eventual system. These actions will prompt questions regarding the functionality of the system that will be much easier to deal with at the start of a process than at the end. Formal acceptance testing should be conducted at the end of the GUI development to sign off on the interface before integration of the CBR component begins.


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Gui Development Process

Code the Gui

Graphic and Design

Gui Programming

design the graphics for the interface

creation of screens

GUI Acceptance Testing

Fig. 8.6. Graphical user interface development process

8.3.4 Implement CBR Engine The engine that drives the catalog search application is obviously a key component (cf. Fig. 8.7). Specific questions should be answered by the engine’s supplier before a final decision is taken to adopt one nearest neighbor search facility over another. – Is the product scaleable: what limitations exist for the number of cases, and how is performance affected as the case base grows? – Does the engine support changes to the model once the application has been deployed? – Are there limitations to the number of users? – Are there restrictions on the number of case bases that can be used? – Are there clear separations among the model, data, and similarity measures? 8.3.5 Integrate CBR and GUI The CBR engine and GUI components are integrated to form a single application (see Fig. 8.8). This step is not trivial and often requires experimentation and tweaking to get the application to work. An experienced developer may be required to solve any issues relating to the differences between the GUI environment and

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Implement CBR engine

select and test the CBR engine

CBR Engine Selection

CBR engine test

evaluate available tools

operate the CBR engine

Fig. 8.7. Implement CBR search engine process

the CBR engine environment. The system should, of course, be fully tested in its component and integrated forms. Testing should approximate, as closely as possible, the ways in which actual users will interact with the system. Testing at this stage may raise issues that lead to changes in the model or similarity functions within the system. The final step in deploying the system is to implement the links to the existing product data or literature. This will be trivial if the links are facilitated through an existing catalog database, as was the case in the IRSA and Analog Devices applications (see annex A and section 3.2).

8.4 Benefits of the Methodology The partners involved in developing the INRECA methodology have developed, among them, over 15 catalog search applications using CBR. The first four of these were used to formulate the initial version of the “catalog search application” complex method, and subsequent applications were used to enhance and refine it. The impact of the methodology on the later applications and its expected impact on future applications can be described as follows: Development Time is Shortened. The impact here has, in at least one case, been very dramatic. The first Analog Devices search component took six months to produce, the second six weeks and the third only two days. Some of this is due to automation, but much is due to the systematic reuse of a proven method.

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System Integration

Integrate Engine and Gui

Integrate the GUI and CBR

link the component s

Test the integrated system

operate the integrate d system

link to existing product materials

implement the links

Fig. 8.8 Integrate CBR and GUI process

New People Can be Introduced Quickly. Documentation of the process allows us to quickly bring new people up to speed. We have an independent record of experience and clear documentation on how to apply it. Steps in the Process have been Automated. By formalizing a record of the method we use, we have been able to identify steps in the process that are suited to automation, and we have developed computerized versions of these processes. Customers Like the Comfort Factor. In any innovative domain, it is important that the customer be comfortable with the concept and the risks involved. A clear method removes the feeling that “black magic” is involved, and the business decision becomes easier for the customer. Clear Documentation is Maintained. Many software quality systems require a systematic documentation regime before approval is granted. The INRECA methodology, when fully applied, generates high-quality and highly usable documentation that meets this need.

9. Developing Maintenance Applications

“Data is a burden, knowledge is an asset.” Operators and manufacturers of complex equipment often collect data about the equipment they maintain: technical follow-up files, breakdown files, intervention reports, preventive maintenance reports, and records of requests from their clients or distributors. Unfortunately, this information is usually not well exploited. This is due both to the complexity of the equipment itself and to the type of technicians who carry out the maintenance processes. In this chapter, we explore a recipe for this category of problems, with a specific case structure and modeling approach, as well as typical problems for linking the developed maintenance system to both on-board equipment and technical information.


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9.1 Characteristics of a Maintenance Application for Complex Technical Equipment A typical piece of complex technical equipment shows a high degree of dependence between the different modules or parts that compose it and its different organs. Typically, this modern equipment contains electronic systems. Some examples of such complex equipment to be maintained are: – – – – –

traction and brakes on streetcars, on-board computer systems on high-speed trains, oil systems on aircraft engines, pistons on large marine diesel engines, robots.

For these kinds of equipment, a decision-support system typically provides: – an action to carry out, – a spare part to replace, – some technical documentation to consult. 9.1.1 Typical Input and Output Maintenance actions are usually done by experienced technicians. The data the maintenance support system uses is generally accessed by the technicians either directly (by querying the equipment database directly), or after they have performed an action suggested by the system. This information can be in the form of: – – – –

information extracted from the current state of the equipment itself, data extracted from the current session (by communicating with the equipment), data extracted from the history of the equipment, or information provided after the technicians have carried out some actions.

The input parameters have to be defined jointly with the equipment specialists. These can be: – the state of the equipment when the fault has been noticed, – the state of the equipment in the past, – the actions carried out by the technicians to identify the fault. The output parameters will cover: – the action to perform, – the part(s) to replace, – the technical documentation to consult.

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9.1.2 Technical Characteristics The main characteristics of building a diagnostic application for the maintenance of complex technical equipment are: – Because of the complexity of the equipment, failures are rarely identical. Additionally, there are a high number of possible different failure patterns. A systematic approach for tracking all possible failures and the conditions under which they might appear is, therefore, not possible. Some foreseen failures will never happen, whereas failures that were not planned will be observed frequently. – The end-users of such a diagnostic system are qualified technicians. They have very specific knowledge. There is no knowledge base available. Indeed, they do not describe the problems in a deterministic way but with their “feelings,” based on their experience. Moreover, because the equipment is technical, it has specific characteristics. Technical equipment is becoming increasingly modular. The same parts of a system can be used in many configurations. For instance, an electronics card can be reused in different parts of a train, or even on several different trains. Hence, it is necessary to develop a specific diagnostic process for this card. However, since the diagnostic procedures depend on each other, it is necessary to plug the card into the general framework of the whole piece of equipment. In fact, a typical piece of technical equipment is often composed of smaller subsystems, which themselves are decomposed in order to reach the Line Replaceable Units (LRUs). An aircraft engine, for instance, is composed of major modules (low-pressure turbine, fan and booster, combustion chamber, etc.), themselves decomposed into minor modules (bearings, rings, etc.) and finally into LRUs (sensors, cables, etc.). Figure 9.1 represents such decomposition. This organization has major consequences for the diagnostic process: – The modeling process must take into account the reusability of the subsystems. – The cases must share information among different models. – The retrieval process has to help the user navigate among the different subsystems. Another typical characteristic of complex technical equipment is that they often interact with the computer system. For instance, on-board avionics can provide information about the state of each component, as well as automatically provide data requested by the user (maintenance personnel). As a consequence, the diagnostic system has to be ready to receive two categories of information: One provided automatically by the equipment (self-diagnostic, etc.), and one provided after investigation by the technician. Both types of information will significantly move the diagnostic process toward either a black-box style (fault trees if most of

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the information is automatic) or an open style (dynamic CBR consultation if most of the information has to be asked of the end-user). Finally, integrating the diagnostic process into a powerful information system is an absolute necessity. Indeed, technical drawings, IPCs (Illustrated Parts Nomenclatures), service bulletins, job cards, etc., help the technician either correctly apply the recommended procedure, or, simply, understand the behavior of the equipment.

9.2 Vertical Platform for Maintenance As we have seen in the previous paragraph, maintenance of complex technical equipment requires specific procedures for modeling the data, collecting the cases, defining the diagnostic process, and integrating the process into the end-user environment. For instance, trial applications by TGV maintenance technicians have demonstrated that the number of attributes increases quickly with the number of functions covered. When creating the cases and building the fault trees, if any, the technician has to take into account all of these attributes for each reference case. Most of these attributes are not relevant for the subsystem considered. However, the technician has no way of knowing this in advance. Thus, if the case base is broken into smaller chunks corresponding to subsystems with their own case bases, it is difficult to maintain the case base and the data model, and it is difficult to use the case base because the many attributes make the choice harder for the technician. This section describes the development processes related to building a technical maintenance application. It focuses on the description of the hierarchical data model (section 9.2.1) and on acquisition of the cases for each case base of this hierarchical data model (section 9.2.2). Main equipment LRU 1

LRU 1

LRU 3 LRU 4

Subsystem 1

Subsystem 4 Subsystem 8 LRU 5

Subsystem 2

Subsystem 5

Subsystem 3

Subsystem 6 Subsystem 1

LRU 6

Fig. 9.1. The decomposition of a complex piece of equipment

Subsystem 7

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9.2.1 Domain Model: The Hierarchical Approach The difficulty of maintaining complex technical equipment comes mainly from the difficulty of understanding the impact of a failure from a specific organ on the equipment’s functions. In general, a single organ has an influence on several different functions. When a technician troubleshoots a fault in the equipment, he or she perceives the functional consequences of an organ failure: One or several functions are affected. The general principle of building hierarchical models aims at splitting a complex model into n simple submodels. Each submodel may itself be decomposed into other submodels until it becomes a simple model, with fewer than 15 attributes, which are easy to maintain. Adding a new attribute is done in only one submodel and does not impact existing cases that are related to other subsystems. The submodels are reusable and can be called from different functions. The input used for defining the functional decomposition of the equipment is: – Functional specifications of the equipment (decomposition in major, then minor modules). These specifications describe the associated fault codes and the associated context. – The organs themselves enable the technician who builds the hierarchical model (let us call him the “expert”) to refine this functional decomposition when organic fault codes, like autotests, etc., exist. Figure 9.2 shows the proven process model for building hierarchical domain models. The main steps for building the model hierarchy and the content of each model are summarized below: – The first step is to define the data model hierarchy which is based on an analysis of the existing information on: • functional architecture, • data supplied by the equipment, • additional observations made by the technician. This is the starting point for describing more precisely each submodel within the hierarchy and the common attributes among the models. – For each model in the hierarchy, it is then necessary: • to create the data dictionary that contains the technical sentences used to describe a case, • to define the attributes and attribute values, • to test the data model. This can be done by integrity tests (“Do any cases have a value in a submodel for any attributes?” “Are there no conflicts between two models that have common attributes?” etc.), by study of completeness (for instance, “Does a submodel cover all the possibilities of failures for a specific LRU?”), etc.

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Analysis of the functional architecture

9. Developing Maintenance Applications Analysis of data observed by the technicians

Analysis of data supplied by the equipment

Description of the model hierarchy

Model hierarchy

Create data dictionary

Data dictionnary

Results of the test on the data model

Define attributes and attribute values

Non-validated data model

Test of the data model

Validated data model

Fig. 9.2. Process model for building the hierarchical models

9.2.2 Case Acquisition Process The processes related to case acquisition are strongly linked to the definition of the hierarchical data model. The information collected during the previous phase enables the experts to refine the initial decomposition. The “electronic” data (fault codes, contexts, etc.) will allow the expert to create high-level case bases for each main function. This initial case base will guide the technician towards one of the subfunctions that are related to the main function. Example. For a TGV, the main case base concerning “Opening access doors” contains sufficient information to guide the technician towards: – a problem with the whole train (problem on the on-board computer network), – a problem with a specific door. Because of the hierarchical modeling, each case base is simple enough to be described in a relational way, i.e., a matrix where each line corresponds to a case

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and each column corresponds to an attribute value (electronic information or question to the operator). Each case concludes either with a final diagnosis or with a submodel. There is generally no need to build a complex interface for creating and validating the cases. The different steps for building each case base are (see Fig. 9.3): – definition of the database structure related to a submodel. The result of this process is a physical database structure. – definition of the authoring environment (a simple one the technician who creates the cases can directly update). Once the authoring environment is ready, the technician uses it directly to author the cases. – creation of the current case base. Typically, the technician will use two sources of knowledge: The one that comes from the technical information about the equipment (analytical information) and the one that comes both from his experience and from the historical behavior of the equipment (practical information). As a consequence, the technician will author two categories of cases: • analytical cases, • real cases. Even when authoring real cases, it will be necessary to analyze them in order to verify that they are complete and coherent. For both analytical and real cases, it is necessary to describe the solution. It may be either a final diagnosis, or a link to another case base. These cases are then saved in the database of current cases, which contains cases not yet validated. – validation of the cases. This validation can either be done: • manually, by reviewing each case and comparing it to the other cases of the case base. This is possible since each submodel is small and the case base is dedicated to the diagnostics of a specific subsystem of the considered equipment, • automatically, by building a fault tree. The fault tree can help the technician discover holes in the case base or discover incoherence between cases, – transfer of validated cases. The last step is to transfer the validated cases from the current case base to the reference case base.

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Data model

Definition of the database structure

Structured database

Specification of the case authoring environment

Case authoring environment

Creating cases

Current cases

Transfer of current cases to reference cases

Database of reference cases

Validated cases

Validation

Fig. 9.3. Process model for case acquisition

When the case bases are being built, some rules also apply so case retrieval can be optimized: – about the case base as a whole: each case base must be modular. It should be possible to use it independently from the other case bases and to make it evolve without interacting with them. – about the cases themselves: • Each attribute has to have at least two possible values in the case base. • For each case, each attribute should have a value. • A case may have several conclusions, since it is sometimes difficult to define an attribute that discriminates among near solutions.

9.3 Impact of the Methodology in a Maintenance Application of Complex Technical Equipment Because of the complexity of the equipment, the technicians often rely on their own experiences to solve a failure, and they have difficulties transmitting this experience to their colleagues. The hierarchical domain model approach together


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with the INRECA methodology that describes how to build those hierarchical models allows this complexity for the description of problems and their solutions to be reduced. As a consequence, the introduction of the INRECA methodology has positive impacts on: – – – –

the productivity of the building process, the quality of the results obtained, the communication among the members of the development team, the monitoring of the development process.

9.3.1 Productivity for Building a Diagnostic Support Application By productivity, we mean the reduction of the effort necessary to reach the desired result. A high productivity is ensured by applying this recipe, which enables the developer to easily build a hierarchical domain model because: – The maintenance effort of the system is low. An updated specific data model or case base has no impact on the other models or case bases. – Authoring the case and maintaining the data model are simple enough because of equipment decomposition. Authoring and maintaining can be mastered by the technicians themselves. This reduces the delay in obtaining and maintaining the cases and models. A typical example of the productivity increase was demonstrated when the TGV application was built (see section 3.3). A first attempt at data modeling and case base authoring was made in three months for describing problems related to a “chain of traction” of the TGV (the set of equipment required for maneuvering the train). Four experienced technicians were first trained on data model and cases authoring tools over eight days. Then they started to describe the data model and the cases related to this chain of traction. After three weeks, they decided to stop because the number of cases began to grow exponentially as they started to analyze the subparts of the chain of traction. Consequently, the development process was modified. By applying the updated development process, one technician was then able to provide a first set of data models and the corresponding case bases (29 different models) in less than two weeks. 9.3.2 Quality of the Results Obtained Applying the INRECA methodology increases the quality of the results obtained because: – The decision support process is documented and reusable. – The best specialists can be hired to describe complex subsystems they know well, since the amount of work is lower.

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For instance, in the TGV application, it was necessary to match some internal ISOlike quality plan. The application of the INRECA methodology enabled the client to more easily validate the results. Each part of the application (each data model, each case base, etc.) could be validated independently and could demonstrate a better quality than when everything were mixed up. This was possible because the specialists needed much less time to achieve the same work and, as a consequence, paid much more attention to it. 9.3.3 Improved Communication Documenting the processes lets users not only improve and validate the results, but it also improves communication among the development team members, and between the development team and the end-users (By “development team,” we mean people who author the cases and data models). This improved communication has two benefits: – Understanding the content of the data models and the case bases is much better because of their granularity. Each case base and each data model is small. As a consequence, everybody has the same understanding of its content. – The documentation of the diagnostic process improves the understanding of the CBR technique itself. This is very important for the acceptance of the technique. Let us move back to the example presented in section 9.3.1. The technician who authored the case bases in fewer than two weeks told us that he had finally understood the need and the behavior of CBR after he had read the documentation about the diagnostic process. This example can be generalized. Many maintenance technicians are use to representing what they know or want to learn, through flow diagrams. Since the INRECA methodology proposes the same approach for describing the CBR development processes, it is easily understood and improves the communication and the understanding within the development team. 9.3.4 Managing Decision Making The INRECA methodology offers a solid basis for management decision making: Planning, resource allocation, and monitoring. It allows the managers to create project plans with structured process models, adapted task definitions, roles and responsibilities, duration, and resource allocations. By defining and documenting the process models, the methodology ensures that every aspect is taken into account when a new CBR application is developed. It makes the development process transparent both to the customers and to the developers. It is a key point for the managers when they decide whether to start a CBR project. By offering proper processes, by documenting each level of the processes, and by closely defining the


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input and output of each part of the processes, the INRECA methodology helped the CBR tool developer convince the customers of the validity of this approach. It clarified the need for data models and case base maintenance and allowed the managers to estimate precisely what resource allocations were needed based onto realistic expectations. For the TGV application, applying the INRECA methodology also helped convince managers that the service technicians could handle the development of many different case bases (several hundred). It was possible to quantify the resource allocation for creating and maintaining this information, and to precisely define roles and responsibilities for each task. Finally, the INRECA methodology helped AcknoSoft, as a CBR service provider and tool vendor, to more accurately estimate provisional efforts and delays, produce a reliable project plan and write a clear proposal which was then accepted by the client.

10. Developing Experience Factory Applications

With the acceleration of technological changes and product innovation, ability to share software engineering experience (all kinds of knowledge that can be of use for developing software) within a software-development organization is a major competitive advantage. Support for sharing experiences at the technical-system level together with the human-based enactment of processes are highly important in industry and public organizations. We call such a combination of computer and human agents a learning “system,” in the sense of a socio-technical system. To build such learning systems systematically and in a cost-effective way, it is necessary to have systematic development processes. Given the software engineering domain as our main focus, what is needed then, is an approach that is both general enough to cover the whole domain of softwareengineering-experience reuse and specific enough to give meaningful, detailed process descriptions. In this chapter, such an approach is described. We present parts of the experience factory application-development process that especially considers the interplay between human-based and computer-based processes. We describe the subprocesses for goal-oriented ontology development for softwareengineering experience, that is, subprocesses that are specialized towards the tasks of populating and utilizing an experience base for the domain of softwareengineering-experience reuse. These processes are widely supported by case-based reasoning technology and address non-technical issues, such as social and organizational issues.


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10.1 Introduction With the acceleration of technological changes and product innovation, sharing software engineering experience (all kinds of knowledge useful for developing software) within a software-development organization becomes a major competitive advantage (Basili et al. 1994). Sharing software engineering experience can be implemented by storing the software engineering experience in an organizationwide repository, referred to as the experience base. The experience factory — usually its own organizational unit — is responsible for the experience base (Basili et al. 1994; see also chapter 5 and section 3.4). We are concerned with developing a process for building and running a complete experience factory infrastructure for sharing/reusing all kinds of software engineering experience during software engineering projects. While in Althoff et al. (1999) the general approach has been described, this chapter details the systematic development of such a repository and, especially, the validation in a case study at Fraunhofer IESE (a still more detailed description can be found in Tautz 2000). The historical starting point of our approach was the insight that case-based reasoning (as, for example, described by Aamodt and Plaza 1994 and Althoff 2001 in the task-method decomposition model) can be combined with the qualityimprovement paradigm (QIP) for continuous learning and improvement (Althoff et al. 1998), which underlies the experience factory approach (Basili et al. 1994). Thus, experience factory application development can be viewed as a process for building CBR systems with a special focus on software engineering experience reuse. On this level it can be compared, for instance, with the work of Kitano and Shimazu (1996) on a life cycle model for CBR systems, or all the other applications built with the INRECA methodology described in this book. Compared with these, the experience factory application-development process is in some parts more detailed, especially regarding the identification of attributes and the definition of the recording processes, as presented in section 10.2. For the knowledge-based system, sharing/reusing knowledge is the primary objective of ontologies (Chandrasekaran et al. 1999). Ontologies are explicit representations of conceptualizations (Gruber 1995). As such, the term “ontology” is is a synonym for the conceptual information of an experience base. In fact, ontologies have been used for knowledge management systems (Benjamins et al. 1998). There have been some approaches to developing ontologies, but their descriptions are often limited to a set of guidelines (Gruber 1995, Uschold & Gruninger 1996) or to an enumeration of necessary development activities (Guarino 1997) without detailing the activities to a level that we found helpful for experience factory applications in practice. However, we reused the kind of tabular descriptions used by METHONTOLOGY (Blázquez et al. 1998, Gómez-Pérez 1998), a well-known project for developing ontologies. The tabular notation of METHONTOLOGY for documenting ontologies was the basis for a notation that was extensively tailored to the needs of the software engineering domain (especially with respect to defin-

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ing similarity). An overview of methodologies for building ontologies is given by López (1999). While ontologies focus on (sharing and) reusing conceptual knowledge, problem-solving methods codify reusable procedural knowledge. One of the bestknown attempts at building a generic library of problem-solving methods and providing information on how to use such a library is the CommonKADS approach (Schreiber et al. 1994). Generally, ontologies and problem-solving methods are seen as complementary parts for building knowledge-based systems (Studer 1999). A recent overview of ontologies and problem-solving methods emphasizes this (Gómez-Pérez & Benjamins 1999). This integration idea is also fundamental to our experience factory application-development process, although the tasks described in its task-method decomposition (see section 10.2) are in part carried out by humans, whereas problem-solving methods are typically associated with automatable algorithms. Our experience factory application-development process is concerned with the overall task of developing learning “systems,” where “system” does not necessarily mean a fully implemented software system but includes the option that some/many subprocesses are carried out by humans (i.e., a socio-technical system). This clearly overlaps approaches from the knowledge management community and, as with most of these, our development process also emphasizes the importance of non-technical issues (e.g., see section 10.2.1 and section 10.2.2), such as social and organizational aspects (Akkermans et al. 1999, Davenport & Prusak 1998, Bailin et al. 1996, Wiig 1995). New with this view is that this view allows systematic research on the learning organization topic, especially considering the interplay between human-based and computer-based methods. This is the benefit of integrating the technology-focusing CBR approach with the non-technical QIP: Though CBR is a technology that widely supports the experience factory applicationdevelopment process, the QIP is to some degree technology-independent and supports prioritizing certain methods over others from a cost-benefit point of view. Currently, the experience factory application-development process is evaluated using the case study presented in this chapter, as well as other projects, especially in the consulting business. In addition, the basic assumption underlying our experience factory application-development process as a process for developing learning “systems” has also been adopted by others, for example, for application development for CBR (see chapter 5) and data mining (Bartlmae 1999), as well as for supporting continuous improvement processes in hospitals (Althoff et al. 1999a). This shows that the process can be applied to a very broad range of application tasks. Here it differs from domain analysis approaches that are also concerned with building and structuring repositories, but have a much more restricted scope (e.g., they do not consider empirical data or process models to be stored) (Arango 1994, Simos et al. 1996).

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The next section describes the various aspects of the experience factory instantiation of the INRECA methodology. In section 10.3 some lessons learned are presented. Finally, section 10.4 summarizes and concludes this chapter.

10.2 Experience Factory Application-Development Process The technical infrastructure of an experience base is designed in several phases (section 10.2.1 through section 10.2.5) before it is actually implemented (section 10.2.6). The design subprocesses are (see Fig. 10.1): EF application EF application development development

EF application application EF development method development method

design experience design experience base base

infuse experience infuse experience base base

design design base experience experience base method method objectives set set objectives

areas set setsubject subject areas

scenarios identify identify scenarios

conceptualize conceptualise

“record” define define „record“

Fig. 10.1. Subprocesses of “design experience base”

1. 2. 3.

4. 5.

Set objectives: During this process, the objectives and associated success criteria for introducing the experience base are determined. Set subject areas: Once the objectives are set, the competence areas of the experience base are determined. Identify scenarios: During this process, narrative descriptions for using the technical infrastructure of the experience base are developed. The narrative descriptions in the form of scenarios can be interpreted as the user requirements for the experience base. Conceptualize. During this process, the actual ontology is defined based on the usage scenarios. Define “record”: Once the ontology has been defined, the experience base needs to be filled with experience. Often, this experience is not available in the form in which it will be needed later. Therefore, a precise

10.2 Experience Factory Application Development Process

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recording method needs to be defined that ensures the “conversion” of the available information into the needed information. 10.2.1 Setting the Objectives According to O’Leary, the choice of a conceptualization is a “political process that requires negotiation” among the stakeholders (O’Leary 1997). Furthermore, it is impossible to find a conceptualization that is preferred by all stakeholders. In this context, a “stakeholder with regard to a given issue, decision, or initiative is someone who has a definable interest in the outcome of that issue, decision, or initiative.” (Simos et al. 1996). The stakeholders do not only include supporters, but also strategic opponents or competitors (Simos et al. 1996). They can have multiple interests in the issue at hand, including interests that may conflict. The experience factory application-development process supports this political process by identifying the stakeholders, their interests and objectives; by selecting/prioritizing objectives; and by establishing success criteria to monitor the success of the reuse program (see Fig. 10.2).

set objectives

set objectives method

identify stakeholders

identify objectives

select objectives

establish success criteria

Fig. 10.2. Subprocesses of set objectives

In the IESE Experience Factory (IESE-EF), we identified the following stakeholders: the director of the institute, department heads, project managers, project members, library information and documentation service, and project control. The first four stakeholders can be viewed as both experience providers and the customers (users) of the experience base’s contents; whereas the latter two serve only as experience providers. To get an idea of the users’ interests, we decided to interview the director, all department heads, and representatives of the project managers and members (i.e., employees with extensive experience). After the first two interviews, it became clear that the interests of the users were not only diverse (although both interviewees agreed that project-relevant information must be shared), but also very de-

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manding. Since the diverse and demanding expectations could not be met within a short timeframe, we decided to abandon the interviews and to build the experience base incrementally. We chose to begin with managing project experience (i.e., experience on how to perform projects) for the reasons outlined in section 3.4. Later we would expand the set of objectives incrementally, based on the feedback of the experience base users. Other objectives included increasing the transparency of projects for the director of the institute and the department heads, as well as improving the sharing of contact information about Fraunhofer IESE’s customers. However, we did not start with these objectives, because sharing project experience had the largest number of potential users. To be able to judge whether the selected objectives have been accomplished, it is necessary to define success criteria. For example, it was expected that the explicit recording of project experience would improve the state of the practice at Fraunhofer IESE both in terms of time (i.e., useful experience items can be acquired faster by the project manager or member) and quantity (i.e., increased number of useful experience items). At the time the success criteria were defined, the current state of the practice for sharing project experience was to ask one or more colleagues. A project registry in the form of an HTML page in the intranet listed all of Fraunhofer IESE’s past, current, and upcoming projects including their project titles, customers, project managers, and members. Thus, the project registry could be used as a knowledge map to find interview partners based on project title and customer. However, the project registry was not always up to date; for example, changes in project staffing were often not recorded. 10.2.2 Setting the Subject Area Once stakeholders and their objectives are identified, the objectives are refined by defining the subject areas of the experience base (Fig. 10.3). In the case of IESEEF, three questions came up after we had decided to share explicitly stored project experience: set subject areas

set subject areas method

identify subject areas

select subject areas

Fig. 10.3. Subprocesses of “set subject areas”


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1.

What kind of experience should be shared (templates, reports, lessons learned)? 2. For which project phases should experience be shared (project acquisition, project planning, project execution)? 3. What experiences should be shared (internal processes of Fraunhofer IESE, technical experience regarding baselines, limitations, and tailorability of transferred software engineering technology)? To answer these questions, the evaluation criteria of the “select subject areas” subprocess (see questions of Table 1) were used (cf. Wartik & Davis 1999). In the following, the “killer criteria” are listed for the excluded subject areas. 1. Kind of experience: Templates were rejected, because (a) they are already accessible to project teams over the intranet and (b) the collection of templates is not expected to grow drastically in the future. Reports were rejected, because finding useful reports would require an elaborate ontology. Such an ontology should not only classify the reports by their title, author, etc., but also by the way they can be reused (e.g., an introduction to a software engineering technology may be reused in other reports1). Given the large number of reports available (about 250 at the time the decision was made) and the vague idea about what “similar reports” really means, the risk of expending a lot of recording effort for a possibly low success rate was assessed to be too high. 2. Project phases: Project acquisition was rejected, because (a) only a few persons were involved actively in acquisition, (b) most of the persons doing the acquisition were experienced, and (c) the persons doing the acquisition had little time to record their experiences. 3. Reference objects for the experience: Technical experience was rejected, because it was felt that (a) person-to-person communication worked quite well in this area and (b) technical experience was already documented, partly in IESE reports and other scientific publications. These rejections left only lessons learned for the internal processes “set-up project” and “execute project.” Table 10.1 shows the evaluation of this subject area. The evaluation shows that the selected subject area is almost a perfect candidate (“almost” because the experience was not yet available in a written form).

1

Note that such reuse opportunities cannot be deduced from the title. A report entitled “Experience gained during the improvement program at company X” may contain any number of such introductory texts (including none).

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Table 10.1. Evaluation of the selected subject area Category Potential

Question How many of the objectives can be reached by storing information for this subject area? How important is the explicit storage of this information for the objectives?

How many users are interested in the particular subject area? Are the tasks to be supported complex enough to justify recording information for this subject area?

How do existing and planned standards affect the reuse potential?

Existing artifacts

Are people available who are experienced in the subject area? Have they developed artifacts of high quality for this subject area? Are these artifacts available? Can the experts decide whether two artifacts are similar?

Stability and maturity

Is it possible to collect within a few months a set of artifacts that represents a good coverage of the subject area? Are needs or technology changing slowly enough to allow recovery of an investment in explicitly storing the information in the subject area?

Answer It addresses two objectives (process guidance and sharing of experience), because the experiences can be interpreted as hints for performing the processes “set-up project” and “execute project.” It is expected that the explicit recording of experience improves the sharing of the experience and supports the project guidance. In addition, capturing the experience explicitly counteracts fluctuation of personnel. Everybody who is on a project (which is the majority of IESE’s employees) Yes. E.g., the process for executing a project (which may last for over one year) is described in five pages. During a project, many situations may be encountered that are not covered by the process description. Therefore, additional experience is very helpful. Positively. The existing process descriptions for “set-up project” and “execute project” can be used as references. The process “wrap-up project” will ensure the capturing of the lessons learned. Yes. Many IESE employees have already executed several projects. No. However, tacit knowledge for the required artifacts is available. Project teams need to be interviewed to develop the needed artifacts. No. See previous question. Yes. There is already know-how available concerning lessons learned (Birk&Tautz 1998) Yes. Teams on current projects and projects that ended within the last 12 months can be interviewed. This will cover all types of projects. Yes. Projects will always be planned and executed at Fraunhofer IESE.


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10.2.3 Identification of Scenarios After the subject area has been selected, reuse and record scenarios are developed (see Fig. 10.4). Reuse scenarios describe how the experience stored in the experience base is retrieved and utilized. To enable automated support for the retrieval of experience via the IESE-EF, the retrieval part of the reuse scenarios has to be formalized further (in the form of an ontology). For this purpose, retrieval goals are defined. However, experience does not always become available later in the form it is needed during reuse. During recording, this gap is bridged. Record scenarios describe how this gap can be bridged. They are developed in two phases: During the first phase, potential knowledge sources are identified, whereas the actual knowledge acquisition scenarios are described during the second phase.

identify scenarios

scenario identification method

identify reuse scenarios

define retrieval goals

identify record scenarios

identify record scenarios method

identify knowledge sources

describe knowledge acquisition

Fig. 10.4. Subprocesses of “identify scenarios”

Reuse Scenarios. The reuse scenarios of IESE-EF also are based on the experiences gained through documenting lessons learned in other projects (Birk & Tautz 1998). Each scenario is described by the following aspects (Fig. 10.5 shows an example): – Trigger: The trigger specifies under which condition(s) the scenario is expected to be executed. – Actions: The actions describe how the scenario is executed. The descriptions explain what information is typically entered and what information is sought. Because the lessons learned captured will cover a large spectrum (different types of projects, customers, domains, etc.), it is difficult to describe explicitly

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Trigger: A Project Member has recognized a problem in the project. Actions: 1.

Based on the characterization of the project at hand, problems that occurred in similar

2.

The suggested problems are analyzed further to see whether they occurred in a similar

projects, are retrieved using COIN. situation. Assumption: Each problem description includes an informal description of the situation in which it occurred (e.g., the status of the project). 3.

The solutions associated with the problem are analyzed. Assumption: Each problem is associated with a number of solutions. A solution describes an attempt to explain how the problem was solved (pragmatically) and includes an evaluation. The evaluation states whether the solution successfully solved the problem. If it did not solve the problem successfully, it states the reason why it did not.

4.

The most promising solution is chosen and adapted to the needs of the project at hand.

Expected benefits: Reusing solutions that worked successfully in the past (a) reduces the effort needed to devise a new solution and (b) avoids applying a solution that was not satisfactory in the past (thus avoiding making the same mistake twice).

Fig. 10.5. Reuse scenario “find solution”

how to utilize the lessons learned in detail. Therefore, the action section mainl focuses on the retrieval part. – Expected benefits: The expected benefits justify why the scenario should be supported. This allows assessing the consequences if the decision is made to no longer support one of the scenarios. Retrieval Goals. As a preparation for the actual conceptualization (i.e., the definition of the experience base ontology), retrieval goals summarize the retrieval part of the scenarios (see Gresse von Wangenheim et al. 1999). Each retrieval goal is described by four facets using a template (Fig. 10.6 shows an example): – – – –

Type of experience item: The type of experience item to be retrieved. Purpose: The purpose for which the experience item is supposed to be retrieved. Viewpoint: The role of the user (project manager, software developer, etc.) Environment: The environment in which the retrieval attempts will be made. Retrieve problems for the purpose of finding a solution for an occurred problem from the viewpoint of a Project Manager or Project Member in the context of Fraunhofer IESE

Fig. 10.6. Retrieval goal of scenario “find solution”

[type of experience item] [purpose] [viewpoint] [environment]


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Record Scenarios. After the documentation of the reuse scenarios, scenarios for recording the experience are developed. At Fraunhofer IESE, a project analysis interview was conducted at the end of 1998 to capture project experience as a test. This helped considerably in assessing the feasibility of the scenario for recording lessons learned. Because the project experience was not explicitly documented anywhere, the project teams were identified as the only available knowledge sources. Using project teams as knowledge sources puts several constraints on knowledge acquisition: – The effort for “supplying” the lessons learned should be low in comparison to the “real” project work, because providing knowledge for other projects is typically not an objective of the project. – The number of disturbances should be kept at a minimum; that is, everything that can be asked in a single event should be asked in one event and not in a series of events. – The interviewer should use terminology well understood by the project teams. Therefore, it was decided to use a questionnaire similar to the one used during the project analysis interview at the end of 1998. For IESE-EF, there is only one record scenario, because only one collection event (the project analysis interview) takes place. The result of the project analysis interview is the project analysis report (PAR). All lessons learned and the project characterization (for describing the context for the lessons learned) are derived from the PAR. To ensure minimal effort on the side of the project teams, someone from the IESE-EF team organizes and conducts the project analysis interviews. In this way, the project team does not need to spend time writing the answers — oral answers suffice. However, project teams are asked to prepare for the project analysis interview; the PAR template is sent to them before the interview. 10.2.4 Development of the Experience Factory Ontology To support the maturing of knowledge technically, the informal scenarios need to be formalized. There are two formalization aspects: One aspect concerns the structure of the contents and the behavior of the retrieval system. The other aspect concerns the precise definition of processes, ensuring the availability of needed information and high reuse benefits. The latter is the subject of section 10.2.5, whereas the former is the subject of this section. The experience factory application-development process suggests a sequence for developing the ontology (see Fig. 10.7). The ontology is defined using the constructs of the REFSENO formalism (see Tautz 2000). Define concepts. All types of experience items (e.g., problems) stated in the retrieval goals are listed as concepts together with a reference to the retrieval goals

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(using the name of the corresponding scenario). For each concept, a short textual definition is given. If different users use the same concept, a consensus among the users on the definition must be reached to ensure that all users mean the same thing. If no consensus can be reached, separate concepts must be defined. conceptualize

conceptualize method define concepts

define nonterminal concept attributes

identify reuse information define global similarity

define dependencies

identify reuse information method determine reusability factors

determine minimum quality requirements

determine application boundaries

classify reusability factors

define terminal concept attributes

define application policies

acquire distinguishing characteristics define terminal concept attributes method

determine reusability factors method define meaning of similarity

identify reusability factors

define type

classify attributes

define type method define value range

define local similarity

Fig. 10.7. Subprocesses of “conceptualize”

In a second step, additional concepts for modeling purposes are defined where appropriate. Concepts for modeling purposes are “artificially” created concepts that do not have a counterpart in the real world. They are typically introduced to express commonalities among existing concepts. For example, context information of experiences (such as characterizations of projects or organizational units) is often expressed as a separate concept. This allows sharing a single context description (e.g., that of a project) among many experience items stored (e.g., all experiences gained in the particular project). Such a single context description can lower the maintenance effort. For example, if the project characterization needs to be adapted for some reason, only a single instance needs to be changed. Define nonterminal concept attributes. If one or more of the following questions is answered positively for any pair of concepts, a semantic relationship using nonterminal attributes should be modeled: Will the relationship be used potentially to navigate among the characterizations (e.g., it may be of interest to a user to navi-


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gate from a code module to its specification)? Will the relationship be used potentially to formulate a query (e.g., all artifacts should be related to the projects they were developed/used in)? Is the relationship needed to define assertions or value inferences? Identify reuse information. For each concept, attributes are defined that are necessary to utilize candidate experience. This is done as follows: 1. All factors that are necessary to identify and evaluate candidate artifacts are determined (“determine reusability factors”) using a two-step method: First, the meaning of similarity between two experience items of the same concept is defined informally (“define meaning of similarity”). Then, all factors needed to compute the similarity are identified (“identify reusability factors”). 2. The reusability factors thus identified are divided into two groups during the subprocess “classify reusability factors”: Those that can be determined at the time a new experience item is recorded (“record attributes”) and those that can be determined only after an experience item has been utilized (“feedback indicators,” e.g., the effort it takes to adapt the experience item to project-specific needs). 3. Minimal quality requirements for utilizing experience items of the respective concept are determined in terms of the “record attributes” (“determine minimal quality requirements”). 4. For each “record attribute,” application boundaries are determined (“determine application boundaries”). Application boundaries describe under what conditions (in terms of the “record attribute” values of the query and the characterizations in the experience base) a given experience item can be utilized. 5. During the subprocess “define application policies,” experience item preferences (e.g., applicable completed documents should be preferred over templates or standard procedures should be preferred over exceptional procedures) are identified and “encoded” as “preference attributes.” 6. Property classes that have not been identified in earlier steps, but are perceived as “typical” characteristics of a concept, are defined as attributes during the subprocess “acquire distinguishing characteristics.” They are needed to ensure that each experience item will have a unique characterization. 7. Finally, the identified attributes (“reusability factors,” “preference attributes,” and “distinguishing characteristics”) are formally documented (“define terminal attributes”). For this purpose, each attribute is classified by documenting the reuse scenarios for which it is needed. In addition, the attribute’s type is defined, which includes a definition of the value range and the local similarity.

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Define global similarity. Using the defined attributes, the informal description of the global similarity (output of “define meaning of similarity”) is expressed formally. This allows for similarity-based retrieval. Define dependencies. Conditions that must always be true are expressed as assertions using the defined attributes. Also during this process, automatically computable attributes are identified. Their computation is defined via value inferences. In the case of IESE-EF, the experience base ontology for the selected subject area was developed in October 1999 based on the identified reuse scenarios. The process “conceptualize” was performed essentially as outlined above. Therefore, a detailed reflection of how the process was performed is not necessary here. However, it is worthwhile to note the following: – Although the modeled subject area is only a small part of the envisioned experience base, the ontology is already quite extensive (approximately 20 pages). However, the tabular representation of the conceptual information and the graphical representation of the relationships among the concepts (similar to those representations used by METHONTOLOGY: Gómez-Pérez 1998) still allow a clear presentation of the ontology. This shows that the experience factory application-development process can cope with industrial-strength experience base ontologies. – No (objective) minimal quality requirements regarding lessons learned were known. Therefore, no minimal quality requirements were produced during “determine minimal quality requirements,” Meaningful minimal quality requirements will have to be acquired during actual operation. – All stored characterization information is rationalized by the documented scenarios. If, for some reason, it would be decided not to support the scenario “find solution,” the corresponding attributes (needed for supporting this scenario) can be identified. If these attributes are not needed by other (still supported) scenarios, their values would no longer have to be acquired. In addition, other definitions (such as the type definitions) would become obsolete. This eases the maintenance and thus the transfer of ontologies to other organizational contexts that require a different set of scenarios. – The reuse of (not rationalized) ontologies led to attributes that could not be rationalized using the initial set of scenarios. However, some of these attributes (e.g., the author of an experience item) seemed to be important enough to store. Analysis showed that the initial set of scenarios was incomplete. In this way, three (important) additional scenarios were identified and documented during the conceptualization phase. This emphasizes the iterative character of the experience factory application-development process. Thus, the reuse of ontologies can also lead to a completion of the set of reuse scenarios. – The division of the similarity computation into global (on the concept level) and local similarity (on the attribute level) eases the definition of an adequate similarity. In this way, standard similarities can be used as a first “intelligent guess,”


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which can be improved later using an organizational memory improvement method (e.g., OMI: Althoff et al. 1999b). record record method collect

store

qualify

publish

inform

store method split

copy

analyze part

check for existing parts

evolve

review

analyze part method

analyze quality

qualify method

characterize initially

complete characterization

review method analyze reuse potential

suggest improvements

Fig. 10.8. Subprocesses of “record”

10.2.5 Definition of Experience Recording Once the ontology has been defined, how the experience base will be populated must be described precisely. This is done by instantiating the subprocesses of the “record” process, which is part of the experience factory application-development process (see Fig. 10.8; a more detailed description is in Althoff et al. 1999): 1.

Collect. The first step is to collect the available experience (e.g., by executing a project analysis interview).

2.

Store. The collected experience is stored by copying it into the experience base, splitting the experience into reusable parts, and initially characterizing each reusable part.

3.

Qualify. Then, each part is qualified by analyzing its quality (e.g., according to the minimal quality requirements), checking whether a similar part is already stored in the experience (in which case the new part may be rejected, may be merged with the already stored part, or may replace the

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stored part), analyzing the part’s reuse potential, suggesting improvements for improving its quality or increasing its reuse potential, evolving the part according to the improvement suggestions, and completing/updating the part’s characterization based on the review results and the part’s changes. 4.

Publish. After the new experience has been qualified, it is made available for retrieval.

5.

Inform. Finally, everybody who may be interested in the new experience is informed.

define “record” define “record” method define “knowledge collection”

define “split”

define “analyze quality”

define “analyze reuse potential”

define “inform”

define “knowl. coll. method” survey knowledge sources

define “knowledge collection plan”

select knowledge sources identify overlapping content

plan adaptation

define “characterize initially”

Fig. 10.9. Subprocesses of “define record”

These subprocesses are reflected by the experience factory applicationdevelopment subprocesses for “define record” (see Fig. 10.9): – “Split,” “characterize initially,” “analyze quality,” “analyze reuse potential,” and “inform” are defined explicitly. – “Copy” makes a copy for the administrator of the experience base. This process is usually straight forward and does not need to be described explicitly. – “Check for existing parts” and “complete characterization” are guided by the ontology. In contrast, “characterize initially” is defined explicitly, because where the attribute values come from needs to be described. – “Suggest improvements” and “evolve” cannot be described beforehand, because their execution is highly creative. – “Publish” involves only the change of a status (from “to be qualified” to “published”).


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In addition, the process for collecting the experience in the first place is developed systematically (see Fig. 10.9 and Table 10.2). In IESE-EF, project managers and project members are the only knowledge sources (i.e., human experts). Thus, the process “define knowledge collection” is straight forward (see Table 10.3). But since the “reusable parts” are not collected in the way they will be used later on, splitting the project analysis report into its reusable parts is a bit complex. Table 10.4 defines the remaining subprocesses of the record process. Table 10.2. Subprocesses of “define knowledge collection” and their objectives Subprocess survey knowledge sources identify overlapping contents select knowledge sources plan adaptation define knowledge collection plan define “characterize initially”

Objective Document contents of available knowledge sources Identify contents that can be supplied by more than one knowledge source Select the knowledge sources to be used Tailor knowledge sources to the needs of knowledge acquisition Document when and how which artifact has to be collected and by whom Document how to characterize artifacts internally

The explicit definition of these subprocesses makes the record process not only repeatable, but also transferable among people. In IESE-EF this is a very crucial aspect, because IESE-EF team members are not assigned fulltime to IESE-EF. An experience engineer (someone not on the IESE-EF team) is assigned for a project analysis on an “as-needed” basis. Thus, project analyses can be executed even if one IESE-EF team member is on vacation, at a conference, or involved temporarily in some other project.

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Table 10.3. Results of performing the subprocess “define knowledge collection” Subprocess survey knowledge sources identify overlapping contents select knowledge sources

plan adaptation define knowledge collection plan

define “characterize initially”

Result Project managers and members provide project experience Unknown/cannot be decided a-priori Every project manager and member should be interviewed in a project team meeting. This will allow the team to reflect on the project execution (social aspect) in addition to providing project experience. The template for project analysis reports (PARs) is used for questioning Project analysis interviews take place at the end of a project. For long-running projects, intermediate project analysis interviews may be scheduled (e.g., every six months). If possible, additional interviews should be conducted at milestones of a project. A member of the IESE-EF team (experience engineer) arranges and moderates each interview. The template of PARs is used for questioning. After the interview, the experience engineer completes the report based on the answers and asks the project manager and members to review it. Note 1: It is useful to send the template to the project manager and project members before the interview so they have a chance to prepare for the meeting. Note 2: For later recording, it is highly important that each answer be marked with the name of the person who gave the answer. Alternatively, only the project members may be interviewed. After the completion of the project analysis report based on the interview results (and review results of the project members), the project manager is asked to complement the PAR with his or her views and opinions. All characterization information is extracted from the corresponding PAR.


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Table 10.4. “Record” subprocesses for lessons learned Subprocess split

analyze quality analyze reuse potential

inform

10.2.6

Method The project analysis report is split into the project characterization, observations, improvement suggestions, guidelines, problems, and the solutions to the problems. Note 1: Newly entered problems have the status “new.” The following attribute values are not entered at this point: “decision date,” “priority,” “contact person,” and “creativity team,” They are filled out during the problem-solving process. Note 2: Newly entered improvement suggestions have the status “new.” The following attribute values are not entered at this point: “closed,” and “comments.” Since no minimal quality requirements are known, this process is skipped. Project characterizations are always retained so experience contexts can be specified. Observations, suggestions for improvements, guidelines, problems, and solutions are only retained if they are nontrivial and concrete enough to be useful in future projects. For example, the guideline “do good planning” for a project with effort overrun would violate both criteria and thus be rejected. The review is performed by someone on the IESE-EF team who was not involved in writing the project analysis report. The authors, reporters, and originators are informed of their suggestions for improvements, problems, observations, and guidelines.

Implementation

After designing IESE-EF and the processes around it, the next step was to collect the project experience, that is, to conduct the project analysis interviews. However, at this point there was still the risk of recording “unsuccessfully” the project experience. Here, the term “successful” refers to the “conversion” of the contents of the project analysis report into lessons learned using the developed experience base ontology. To mitigate this risk, a prototype using INTERESTS (Intelligent Retrieval and Storage System — an experience factory application development environment based on CBR-Works; Althoff et al. 2000; see also section 3.4.4) was built. Using this prototype, the defined subprocess for the “split” process was given a trial with the report that was produced as the result of the project analysis interview at the end of 1998 (see section 10.2.3, “Record Scenarios”) before any further project analysis interviews were conducted. For the initial population of IESE-EF with project experience, a representative set of projects had to be selected. To do so, the following constraints were identified:

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1. Only projects completed in 1999 or still running should be considered, because people may not be able to recall all the experiences they have had in projects completed over a year ago. 2. For those projects still running, some significant effort should have been spent on the project (that is, at least two person months). Otherwise, there would not be much project experience to record. 3. Each researcher (that is, those persons acting as project managers or project members) should be interviewed at least once (to ensure that everybody contributed something to IESE-EF) and at most three times (to keep the effort for each individual in acceptable bounds). The interviews themselves were conducted from the end of November 1999 until January 2000 and took 45–90 minutes. This was longer than expected and can be explained as follows: Number of participants. The more people who participate in an interview, the longer it takes, because everybody is asked to voice his opinion. Data shows that an interview takes at least 15 minutes “per participant.” The test interview was conducted with two project members. Consequently, interviews with more project members took longer. Preparation of participants. In general, those participants who prepared themselves for the interviews could provide more structured answers, which could be recorded directly as part of the project analysis report. Size of project. The more effort that is spent on a project, the more likely are lively discussions during the project analysis interviews regarding potential causes for identified problems. This is especially true for projects that ran for more than 1.5 years. Kind of people participating. Some people are very open and willing to share all their experiences. Others are very careful about sharing their personal opinions. In the extreme case, one interview took 60 minutes, although only one person was interviewed. Survey. As part of the first interviews, participants were asked to give feedback on the project analysis activities. This survey took about 15 minutes. infuse experience base

infuse experience base method develop technical infrastructure

prepare training material

plan change

Fig. 10.10. Subprocesses of “infuse experience base”


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In addition to creating the initial population, other actions for IESE-EF were planned (see Fig. 10.10): Develop technical infrastructure. Instantiate the architecture of the technical infrastructure (see also section 3.4.4). Prepare training material. Based on the gained project experience, develop a realistic “reuse situation” to demonstrate the functionality (and usefulness) of IESE-EF. Comprehensive training material was not prepared, because: – It was felt that using IESE-EF is straight forward because of its WWW interface. Therefore, a good demonstration should suffice for motivating people to use IESE-EF and for showing how to use it. – It was decided that recording should be done only by the IESE-EF team. Personal tutoring was preferred over conducting formal training sessions with formal training material. Furthermore, each project analysis report could be used as training material. Plan change. The introduction of IESE-EF was planned as follows: 1. After the initial population of the experience base, an experiment should be conducted with as many researchers as possible participating. The experiment will compare the “traditional” method of “ask your colleague” with the repository-based reuse of project experience (Tautz 2000; see also section 3.4). The experiment will give the researchers an impression of what IESE-EF can do for them. 2. Feedback from the experiment should be used to improve the technical infrastructure. 3. IESE-EF should be announced “officially,” 4. Project analysis interviews should be continued on a regular basis. Usually, one should start with a small group of pilot users (Bartsch-Spörl 1999). However, in the case of IESE-EF, it seemed unrealistic to include only a small group of pilot users, because all researchers had contributed their project experience and liked using the experience of others.

10.3 Lessons Learned IESE-EF has taught many lessons about: – The development of a technical infrastructure of an experience base and the definition of processes to populate and utilize the experience base, – The population of an experience base, – User expectations regarding the explicit documentation of project experience. This section summarizes the most important points.

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10.3.1 A Systematic, Goal-Oriented Approach for Structuring Complex Experience Bases Is a Must One of the most important lessons we learned for IESE-EF is that it is hard to “just do” the project. Too many stakeholders and interests were involved, and the potential contents of IESE-EF were highly interrelated. Thus, it was hard knowing where to start and how to proceed in an orderly fashion. The process for the development of an experience base ontology and the corresponding record processes did not become available until the end of August 1999. At that time, the project picked up momentum. Although the slow progress of the project in the beginning was also due to other factors (e.g., few resources), it was easy to put IESE-EF aside, because it was not clear how to proceed. The progress made since September 1999 (see also Althoff et al. 2001) shows the viability of the approach presented in this chapter. 10.3.2 The Process Hierarchy Is a Framework for Organizing Method Libraries (Positive) deviations of IESE-EF with respect to the “default” methods of experience factory application development subprocesses led to new alternative methods for the processes in the hierarchy. Thus, they can be used to build up method libraries. Such a library grows with each method explicitly documented and classified with the task it achieves and the practical constraints for which it is applicable. Future projects structuring experience bases can utilize those methods of the library, which were applied in projects with similar practical constraints. Therefore, the case study validates the experience factory applicationdevelopment process for tailoring the technical infrastructure of an experience base to organization-specific needs. It emphasizes some of the outstanding characteristics: Experience factory application-development process. – Evaluation criteria are very helpful in selecting objectives and subject areas (see section 10.2.1 and section 10.2.2). – Scenarios are an important prerequisite for developing the experience base ontology. They document what is expected of the future experience base. They also show the gap between the needed information for performing the (reuse) scenarios and the available information (or information that can be made available). Informal descriptions of how to acquire the needed information illustrate how this gap can be closed. – The documentation of reuse scenarios using the three-part structure of section 10.2.3 was found to be useful by the IESE-EF team in deciding whether to support a particular scenario.

10.3 Lessons Learned

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– The explicit documentation of how to record experience makes this task not only repeatable, but also transferable to other people (see section 10.2.5). Concerning the system implementation. A prototype can be built fast using the INTERESTS experience factory development environment. In case of IESE-EF, it was a matter of two days once the ontology had been developed. The initial population of IESE-EF validated the “record” process. The subprocesses could be applied as documented in section 10.2.5, Fig. 10.8. The case study showed some outstanding characteristics of the method: – The collection of project experience follows a very specialized method, which is neither transferable to other organizations (as is) nor applicable for artifacts other than project analysis reports. It will be a challenge to find “patterns” in collection methods that would allow a more detailed decomposition. This also justifies the elaborate method for the process “define knowledge collection” (see Fig. 10.9). – The “split” process is the key for “converting” available information into needed information. Just as with the collection method, this method is highly specialized. – The task “check for existing parts” is necessary for finding similar or matching lessons learned. This allows merging the lessons learned and recording the actual validity of the lessons learned. For example, without this check, it would be possible to record the same observation twice, each referring to another context in which it had been gained. This would result in a validity of “1” (each observation was evaluated in one project), although the same phenomenon has actually been observed twice and should have a validity of “2.” 10.3.3 Further Lessons Learned Finally, we learned that the new improvement program has a positive effect on Fraunhofer IESE: Before IESE-EF, many improvement initiatives were started (e.g., putting up an improvement suggestion box, setting up informal meetings). All of them are based on the assumption that the IESE employee takes the initiative. However, it is typical human behavior to go through the hassle of taking the initiative only if (a) something is really superb or (b) something is really bad. IESE-EF takes a radically different approach. Through the project analysis interviews, experience is captured actively instead passively. During the project analysis interviews, everybody is asked for his experience/opinion. In this way, (a) detailed information is captured and (b) less “spectacular” feedback is articulated.

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10.4 Conclusion Learning “systems,” in a more general sense that includes also human-based enactment of methods (i.e., socio-technical systems), are of high importance in industry and public organizations. Consequently, methodologies are necessary to build such learning systems systematically and in a cost-effective way. Given the software engineering domain as IESE’s main focus, what is needed, then, is an approach that is both general enough to cover the whole domain of software engineering experience reuse and specific enough to be able to give meaningful, detailed descriptions of methods. In this chapter, such an approach has been described. We presented (parts of) the experience factory application-development process that especially considers the interplay between human-based and computer-based methods. We described an approach that is specialized towards the processes of populating and utilizing an experience base for the domain of software engineering experience reuse. The experience factory application-development process pays particular attention to social and organizational issues by including activities for identifying the stakeholders of the experience base, the stakeholders’ interests, scenarios explicating the needs of the users as well as the practical constraints on available knowledge, and methods for populating the experience base. Thus, the experience factory application-development process ensures both the goal-oriented definition of the experience base contents and the definition of processes that smoothly integrate with already-established practices at a given organization. As this approach is applied in practice, the methods will be complemented by specific checklists (e.g., for the processes “select objectives” and “select subject areas”) and predefined conceptualizations for specific kinds of artifacts (e.g., lessons learned, GQM plans, process models, etc.). In the long run, some of the methods may be further decomposed (e.g., the process “select objectives” by subprocesses describing how to perform certain analysis tasks, such as how to determine business feasibility).

A. Compilation of INRECA Methodology Applications

A.1 ANALOG DEVICES – Sales Support for Integrated Circuits For the past 10 years we have tried, like our competitors, to develop a satisfactory parametric search engine that would help our pre-sales people. With two-footthick catalogs describing thousands of ICs, finding the right product or the closest to the customer’s needs is not an easy job, even for a well-trained engineer. We tried CBR and in less than a year we had a unique and successful presales tool. David Kress, Director of Applications Engineering, Analog Devices With sales of $2.3 billion for fiscal year 2001, Analog Devices is a leading provider of precision, high-performance integrated circuits used in analog and digital signal processing applications. Analog Devices designs, manufactures, and markets a broad range of high-performance linear, mixed-signal, and digital integrated circuits. Headquartered in Norwood, Massachusetts, the company employs more than 8,500 people worldwide and has manufacturing facilities in the United States, Europe, and Asia. With thousands of products, Analog Devices was, until now, printing catalogues and data sheets up to two feet thick each year. The cost of printing and shipping catalogs to its 60,000 customers worldwide approached $4 million per year. Most of Analog Devices’s products within a product line differ from each other by only one element and can be described by as many as 50 parameters. Technical support presales engineers take the customer’s requirements over the phone and try to find a match in the Analog Devices product range, a complex process that involves weighting dozens of constraints while at the same time interacting with the customer, usually a design engineer, to try to understand his, or her, real priorities. This lengthy process can be successfully accomplished only by welltrained engineers. For the past ten years, Analog Devices, like its competitors, tried to solve this problem with half a dozen software projects using a standard database management system. But this approach was not very helpful, since SQL-style searches only return a value when all conditions are met exactly. When a customer provides a


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complete set of specifications, the most likely answer is “no match,” but when he or she relaxes some of the specifications, hundreds of matches are found. The solution was developed by Interactive Multimedia Systems (IMS) around “orenge – empolis knowledge manager.” Using “nearest neighbor matching,” IMS produced a working prototype in fewer than six months. Called parametric search, the system allows customers to specify interactively their product requirements, then finds either the right product or the product as close as possible to their needs. With this system, the customer enters the specifications directly onto the screen. Values can be numbers or information, such as “the best,” “sort of,” or “less than.” The parametric search system always provides an answer: a list of the top ten Analog Devices’ products that are closest to the specified requirements. If the user is not satisfied, another search can be started with new priorities until the right product is found. The parametric search is now available on Analog Devices’ operational amplifiers and data converters and will soon be available for a further six product lines. The system is available to customers on a CD-ROM catalog and on the company website. Analog Devices is expecting a savings of $2 million per year, since the cost of producing and shipping 120,000 CD-ROMs is far less expensive than producing the paper catalogs. But it is the quality of the service the parametric search system provides that makes the difference in this very competitive market. Now, when customers call sales support, they usually know exactly what they want and order exactly what they need. For the support engineer, this means having more time to concentrate on truly complex customer requirements. Because the system keeps track of the customers’ requests, Analog Devices can analyze this valuable marketing information and use it to design new products.

A.2 ANSALDO – Maintaining the Metro in Naples An advantage of CBR, particularly compared to rules, is that the knowledge base can be very easily updated. Thanks to CBR, we produced a tool that allows maintenance staff who do not have specialized skills in computer science to update almost the entire knowledge base. Antonio Ruggieri, Ansaldo Trasporti In Southern Italy, the Circumvesuviana Railways have operated the busy railway lines of Naples’ suburbs for over 100 years. With a usual life span of 40 years for a train, the cost of maintaining the 200 vehicles is critical. According to the Italian Railway Company, the maintenance costs for electric traction vehicles represents 60% of the life cycle cost of a locomotive, which is much more than the acquisition cost, even at $3 million a piece. Increasing the availability of railway

A.3 bol.com – Find Books Fast

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vehicles not only reduces maintenance costs, but also minimizes the number of vehicles needed. Using Kaidara Advisor, an innovative diagnostic system was developed by a team of engineers from the Circumvesuviana Railways and the manufacturer Ansaldo Trasporti, with help from the University of Salerno. CBR technology was preferred to rule-based methodology because it is easier to implement and because the knowledge base could be easily updated by the maintenance staff who are qualified technicians but not IT specialists. This 16-man/month project has produced a working system for use by first intervention teams and maintenance shop workers. Because diagnostic equipment might be used on a train, the system runs on a laptop computer with a standard multimedia hardware configuration. It is also available on an Intranet with a web interface. First, the user describes the warnings given by the instrument panels. If this information is not sufficient to identify the cause of the failure, the diagnostic system prompts the user for more information. Verification of the damaged system is done through a sophisticated hypermedia interface. The system can provide pictures, sounds (such as a breaker noise), and movies to describe possible failures. In the final phase, the user is given a diagnosis and the intervention process. Reducing maintenance costs was the main objective for developing a new diagnostic system, but there are other positive effects, such as improving the multidisciplinary expertise of operators.

A.3 bol.com – Find Books Fast Its performance, flexibility, scalability, and precision make the “shop: assistant/ enfinity cartridge” the best solution for our e-commerce sites! Rainer Düspohl, Chief Operating Officer at bol.com International AG Nowadays, Internet shops are involved in an ever-intensifying competitive struggle with one another to win the customer’s favor; customers expect a comprehensive choice of products and precise search results in the fastest possible time. Demands like this put pressure on the vendor to keep on top of Internet sales technology, and a diverse product range means that large amounts of data and exact search results can only be realized by means of powerful search technology working on a semantic level. That’s why bol.com International AG, changing from Oracle to Intershop’s enfinity for its German, Dutch, Swiss, and British ecommerce sites, was looking for a high-end search technology that could handle large amounts of data and deliver only relevant results. The solution for bol.com was the “shop: assistant cartridge for enfinity.” This system is created by integrating “orenge - empolis knowledge manager” with Intershop’s e-business platform “enfinity.” The “shop: assistant/enfinity cartridge” allows multilingual product searches and provides precise, high-speed search

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results. At the heart of the technology is a knowledge model created exclusively for bol.com: It stores all the information about the products available in the Internet shop as well as the pooled knowledge of the bol.com experts themselves. Mathematical case similarities between the products and this expanded knowledge base are defined in the knowledge model. These predefined links enable the customer to make both fuzzy and multilingual searches, independent of any special key terms. The system even tolerates wrong or incomplete entries. Thanks to its knowledge model, the “shop: assistant/enfinity” cartridge finds relevant products lightning fast. With the “shop: assistant/enfinity cartridge,” bol.com now has a search technology that, thanks to its precision, only tracks down suitable products. The “shop: assistant/enfinity cartridge” can also handle high-performance demands and data loads, thanks to its powerful management of large amounts of data — more than 20 GB of product data per country. In the Netherlands alone, where bol.com is the market leader for online sales of books, DVDs and videos, the virtual product range encompasses more than 140,000/750,000 Dutch/English books, around 350,000 CDs and 7,500 films. The integration of the solution in all four countries was carried out without any lost time, and market maturity was attained in just eight weeks. The bol.com project was started in September 2001, and the first version of “shop: assistant/enfinity cartridge” went live just two months later. At the core of the application is the intelligent middleware “orenge - empolis knowledge manager.” Based on XML and Java, it’s an open, modular system that has a high degree of flexibility; it can be expanded by add-on components, incorporated into new technologies, and built onto existing databases. The main feature of “orenge empolis knowledge manager” is that it finds products fast on a semantic level. And thanks to its knowledge model, “orenge - empolis knowledge manager” not only recognizes the syntax but also the content in a search query: The system abstracts from one particular term and creates a comprehensive, mathematical comparison of similar terms stored in a knowledge base. The knowledge model automatically takes several languages into account during this procedure. Yet another two factors contributing to the powerful performance of “orenge - empolis knowledge manager” are its scalability and high load capacity. It’s capable of managing extremely large quantities of data in the high-gigabyte range and making this data available for a product search in the fastest possible time.

A.4 DAIMLERCHRYSLER – The Intelligent Hotline Our hotline operations rely on the knowledge and the experience of experts. CBRWorks helps us to utilize this human capital efficiently and effectively. Thomas Pantleon, CAD/CAM Support, DaimlerChrysler AG

A.5 FM-Ultranet

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DaimlerChrysler sees itself as a first-class provider of vehicles in all automotive markets. The outstanding worldwide reputation of the Mercedes-Benz brand will continue based on new and innovative products. Within its passenger car development department, DaimlerChrysler uses more than 1,200 UNIX workstations for CAD/CAM, while running more than 20 different software applications. Hotline operations and customer service costs are high in this area. At the Sindelfingen, Germany, locations alone, 600 complex problems must be solved monthly. Outstanding customer support is a key to successful competition. Since products and services are growing more and more complex while at the same time changing rapidly, the cost of this service is becoming increasingly high. Therefore, efficient decision-support systems are needed for customer support. The advantage of the CBR approach is in the collection and intelligent provision of experience. CBR-Works takes customers’ problems and compares them to already-known problem cases. When there is no direct solution, the user is guided interactively to a new one. The availability of this asset for every hotline employee drastically reduces the time required to process a request. HOMER is part of the ESPRIT project INRECA II and has been implemented jointly with the DaimlerChrysler research department in Ulm, Germany, by empolis. The productive use of the system started at the end of 1997.

A.5 FM-Ultranet: Fetal Malformation Ultrasonography Training Networking Platform Compared to other medical imaging techniques, ultrasound imaging is a truly noninvasive and cost-effective diagnostic technique. In obstetrics, this technique (fetal ultrasonography) is used differently than it is used in other fields. Fetuses are scanned mainly to detect abnormalities at a very early stage (e.g., of the head, heart, abdominal wall, umbilical cord, urinary tract, skeleton, etc.). This difficult task requires an ultrasonographist (i.e., a radiologist, an obstetrician, a general practitioner) and takes on average half an hour. Fetal ultrasonography is operator dependent and subject to a strong personal interpretation. Reference cases are not easily accessible; sequences of high-definition images are more difficult to store and to browse than are traditional radiology books. This adds to the difficulties already inherent in the technology, such as having to use a probe and inadequate image resolution. Fetal ultrasonography is far less accurate than ultrasonography is in other fields (in Europe, 60% to 75% of fetal malformations are unnoticed before birth), and in North America the percentages are higher. Moreover, in most European countries, when the ultrasonographer believes that there is a risk of malformation, he is re-

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quired to consult an expert, often attached to a regional reference center. In 40% of the cases, the expert invalidates the presence of the malformation. Ultrasound scan interpretation and diagnosis can be greatly improved by comparing new scans to past, similar cases with the guidance of an expert. FMUltranet combines case-based reasoning and expert networking to improve ultrasonography through decision support. The system was implemented with the CBR tool CBR-Works from empolis. One hundred and twenty reference cases of the malformations of the urinary tract, including related image data, were collected. The knowledge representation fully covers the normal fetus. According to the representation of malformations, the urinary tract is fully described, and 25% of the heart and 10% of the abdomen are covered. The model consists of 40 classes with 140 attributes. Each class has a rule set to ensure that descriptions are complete and consistent, and to provide guidance for interpreting the image. All relevant attributes have appropriate medical similarity measures. The system is used at the ultrasonographer’s workplace. Data containing all relevant entries for the fetus is easily collected on a single page. In the case of an abnormal value, the system provides detailed descriptions. Thus, only in the rare case of a potential malformation is an additional effort required. In 95% of cases with a normal fetus, documentation requirements are reduced. Instead of creating a report manually, the examiner simply annotates picture information to the filled attributes where necessary and then generates the case documentation report with a single click. The resulting document can be further edited on any word processor, e.g., Microsoft Word. In addition to these advantages, examiners can use the system for training, thereby improving their ability to detect malformations accurately. The system was deployed and tested in a trial with medical practitioners in Nimes and Liège. The CBR system’s results (i.e., reference cases) were evaluated and met the expert’s expectations. These users appreciated the chance to improve their diagnostic skills by retrieving reference data based on similarities, as well as the opportunity to use the system to manage their personal experiences. All participants indicted that they wanted the system in their work environment. Integrating the system into their work environment would require both better mapping of the actual medical workflow and technical interfacing with the ultrasonography equipment.

A6 Fraunhofer IESE – eParticipation in Software Engineering Projects (indiGo) Our competence in software engineering relies on the knowledge and experience of domain and project experts. CBR technology from INRECA in combination with

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methods from process engineering and KDD helps us to utilize, manage, and improve this fourth factor. Andreas Jedlitschka, Business Area Manager for Software Competence Management, Fraunhofer IESE The Fraunhofer Institute for Experimental Software Engineering (IESE) sees itself as a leading research and technology transfer center in the area of software engineering. IESE advances the state-of-the-art/practice in software engineering by evaluating promising new technologies experimentally and by collecting data demonstrating their benefits in practice. The underlying vision is to build-up, support, and strengthen software competencies in modern high-tech and serviceoriented companies. Software engineering transfer at IESE is done by over 120 employees in five departments, which continue to add employees. Knowledge about IESE’s competencies and internal processes has to be elicited, distributed, maintained, and improved to keep a sufficient level of expertise, and to support the progress of research and the leave of experts. Employee acceptance and comprehension is a key factor for successful business and software process models. Since companies and their business areas are becoming more and more complex, the cost of introducing and evolving process models is becoming increasingly high. Therefore, efficient and participatory environments are needed for process management. To support its own organization as well as its customers, IESE initiated and developed the indiGo project. IndiGo (http://indigo.fhg.de) is an experimentally proven environment for process introduction, evolution, and learning. Its methodology helps companies instantiate and manage goal-oriented eDiscussions, process models, and lessons learned from previous projects. IndiGo’s technology enables companies to propagate process models over their intranet, and develop and guide goal-oriented eDiscussions, as well as support users and moderators with lessons learned from a CBR repository. The ontology was modeled with the CBRWorks successor orenge by various domain experts. The benefit of the CBR approach is the context-sensitive offer of similar experiences (lessons) to support process users and moderators. Inspections and discussions of these models help to find problems, ambiguities, or omissions to improve employee acceptance and decrease the cost of their introduction. IndiGo was funded by the German Federal Ministry of Education and Research (BMBF) and developed by two Fraunhofer Institutes (IESE and AIS) in a threeyear project. After its evaluation in 2002 at Fraunhofer IESE, it is still used to evolve existing business processes, as well as to introduce new ones. Besides the methodology for process learning, eModeration, etc. AIS and IESE developed methods for text mining discourses to extract lessons learned, thread summaries, and other information to support users and moderators.

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A.7 FREIGHTLINER LLC – Online Knowledge Repository for Truck Diagnostics At Freightliner, we tried for a long time to create and distribute better diagnostic information. We could never get enough breadth and depth into the diagnostic database to deal with anything beyond the most common issues. We needed a way of tapping into the huge wealth of diagnostic knowledge available across our entire service chain — from our component suppliers and own engineering, manufacturing, and service divisions right through to our extensive service network. Using Kaidara software, we now have a solid opportunity to do this. A large number of very knowledgeable people are involved in a reasonably simple process. Carlo Nardini, Director Technical Support, Freightliner LLC Freightliner LCC, a DaimlerChrysler company, is the leading heavy-duty truck manufacturer in North America. Putting high-quality trucks on the road is just one part of the company mission — keeping them rolling is just as crucial. Quick, accurate diagnostics are a necessity at Freightliner, because if customers’ vehicles aren’t on the road, then customers aren’t making money. Freightliner trucks are highly customizable. As such, the complexity involved in covering every aspect of the vehicle is formidable. Instead of designing a system to address every single theoretical problem, the company opted for a system that documents real-world and practical diagnostic solutions as they occur and become known. This approach emulates how real-world knowledge is acquired and reused. Indeed, learning new solutions is part of the natural process of solving problems. Freightliner envisioned a system working similarly, as part of a natural process. In Solutions Database, the new system developed by Freightliner, authoring a solution is as easy as answering an e-mail from someone asking for help. In its search for a tool combining a knowledge repository and powerful search capabilities, Freightliner singled out Kaidara. Before the Solutions Database was implemented, authoring diagnostic solutions required skills in vehicle technology, computer technology, and knowledge management. Finding a combination of these skills was difficult; and considerable training was necessary. Thanks to the Solutions Database, knowledge-management theory is no longer required to author solutions. This allows Freightliner to focus on its core competencies: vehicle diagnostics. As a result, productivity is bolstered. The Solutions Database has enabled Freightliner to improve its problem-solving procedures to a one-step process. Experience is shared across all three levels of support, and the first-call resolution rate is increasing. This reduces the overall time needed to get a vehicle back on the road. The consistency and high quality of solutions is maintained and customer satisfaction increased. By acquiring and reusing problem-solving experiences, Freightliner ensures that its most valuable asset — the knowledge of its employees — is shared and protected. Service technicians

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avoid spending time diagnosing problems that have already been solved elsewhere in the organization. Launched in July 2000, the Solutions Database project aimed to create an informal forum for acquiring and sharing experience. Entering new solutions and finding existing solutions had to be simple. What’s more, the system had to be intuitive, letting service technicians and vehicle owners describe problems and solutions in their day-to-day language. The Kaidara Advisor software package, which had been used in a similar calltracking project at DaimlerChrysler, met and exceeded all the company's requirements. Freightliner opted to use this framework and started to develop the Freightliner online repository for vehicle diagnostics. Developed in Java and running on a Windows NT server, the Solutions Database includes a Microsoft SQL 2000 database. Data from all vehicles manufactured by Freightliner over the past 16 years is now available. Thanks to Kaidara Advisor’s ability to link to existing databases, the system was initially seeded with cases that originated from the call center. This initial case base is supplemented with new cases directly entered by people in the company as they diagnose vehicle problems. Users access the system over the Freightliner intranet. Searching the Solutions Database is very similar to accessing the Internet through a portal. Users find solutions after selecting initial information, such as the symptom, the vehicle serial number, and the mileage. They describe symptoms by selecting one of the ten categories, including engine, drive-train, or drivability concerns. Once they have selected the main symptom, users choose a sub-category, such as cooling system, air intake system, or engine noise for the engine category. The sub-categories are based on systems of the truck. In most cases, less than four levels of symptoms are needed to obtain a good understanding of what needs attention. Kaidara Advisor allows users to retrieve historical diagnostic information that is similar to the current situation. Even when there is not an exact match, the power of Kaidara Advisor returns the closest solutions to their diagnostic problem. Entering the vehicle serial number allows users to refine the search further. The initial description is enhanced using additional criteria, such as the engine and transmission type, by obtaining configuration information from a central database. When the engine type is included as a symptom in the search, the system will favor solutions for similar engine types, even though the truck model and other major components may be totally different. Available today to its employees on the company’s intranet, Freightliner plans to deploy the Solutions Database on its extranet as well as on the Internet. Freightliner will thereby ensure that its customers get the fastest, most costeffective service with the highest quality. The company will also ensure that the solutions from across the company and dealer network are acquired, revised, and shared. This will increase the first-call resolution rate even further and save precious time for both the dealerships and the company itself.

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A.8 GICEP ELECTRONIQUE – Diagnostic Help for Circuit Boards Used in Halogen Stove-Tops Our objective was to reduce our maintenance costs and the number of difficult repairs required, and to save 20% off our repair time. Today, the user no longer needs to search the notebook for difficult cases; they are accessed directly in the system, which acts as a memory aid. Serge Bossé, I.T. Director, Gicep Electronique Located near Angers, France, Gicep Electronique is a small company specializing in repairing complex electronic circuit boards, including those used in halogen stove-top burners. Though only a hundred odd such boards are processed each week, their problems are among the most difficult to diagnose. For this repair activity to be profitable, workers must find the cause of the malfunction and fix it in fewer than 45 minutes. If they can’t, the boards must be labeled “difficult to repair” and set aside. Then, to find the information necessary to fix the unit, a worker must search for a similar malfunction in the company’s repair history notebook . To reduce the maintenance costs and repair time of this procedure, Gicep used Kaidara Advisor to develop a diagnostic tool in fewer than three months. Of the 4,000 types of malfunctions cataloged, the company initially chose 200 representative cases to introduce into the case base. Another 50 odd, which are being validated, will soon be added. Operational since September 1996, the system tells the worker which repair to undertake, based on a description of the malfunction’s symptoms. Particularly user-friendly, the system then displays a diagram of the board with the exact location of the defective component. Less than a year after the system’s installation, Gicep is highly satisfied; the system has provided a return on the investment in six months and the desired reduction in repair costs. For the repair team, being able to share the know-how of scarce experts has improved overall team performance, allowing the team to make difficult repairs even when the experts are absent.

A.9 IRSA – Expertise Knowledge-Base CBR has allowed us, for the first time, to provide a real search facility for scientific expertise. This is a real advance for the overseas promotion of Irish Science sector. John Donovan. Secretary, Irish Research Scientists Association (IRSA). The Irish Research Scientists Association maintains a database relating to the qualifications and expertise of its membership. Until recently, this was only searchable using a standard keyword procedure. The usefulness of this, however,

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was limited by the variable levels of detail in the entries, which were produced by the members themselves. The information about individual scientists relates to their discipline, experimental technology, research interests, and much more. Since individual scientists use different terms to refer to the same area, a keyword search was of little use. As a result, the aim of this application was to provide a consistent “fuzzy” parametric search facility that would be available to – college-based agents responsible for managing the interface between research and industry, – industry-based R&D managers seeking recruitable postgraduate expertise or expert consultants, – government agencies seeking to expedite the emergence of a healthy R&D community. The facility runs on the Internet and provides the following search parameters to the user: – – – – – – – – – –

discipline. experimental technology. methodology. material, level of abstraction (theoretical to practical), role of the scientist, economic sector, production technology, broad location, organization identity or type.

The results the system generates are ranked according to how similar they are to the user’s enquiry, and links are included to the scientist’s detailed record, which may further link to a personal or departmental web site. At the time this book was being written, the site had been in operation for a few months, and a strategy for growth and revenue generation for the facility was being planned.

A.10 LEGRAND – Rapid Cost Estimation for Plastic Parts Production We chose Kaidara Commerce because our target users preferred the case-basedreasoning approach, which is less bothersome to use than rule-based systems. Our development teams appreciated the speed and ease of the product’s installation, as well as the support from Kaidara. Jean-Nicolas Manas, Project Manager, Service Production Department, Legrand

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With over 19,000 employees worldwide, Groupe Legrand is the market leader for low-voltage electrical equipment. Whether destined for the housing or service industries, or specifically designed for industrial uses, Legrand products cover nearly every conceivable function of low-voltage electrical installations: switching and branching, protection and repartitioning, current distribution, communication, and security. As new product-line life cycles continue to shrink rapidly in response to an evolving market, Groupe Legrand has made reducing the time to market its main priority. A major part of low-voltage electrical equipment is its plastic components. At Legrand’s Limoges site alone, over 200 thermoplastic injection molds are produced every year, and its plastics production experts are in great demand. So the company’s Center for Studies and Research in Plastics Science in Limoges, France, in collaboration with Kaidara, developed the Rapid Cost Estimation for Plastic Parts Production (ERCP) system. The result of 10 manmonths of development, ERCP allows technicians to determine the cost of tooling investments and the wholesale price of the finished part at the outset of its development cycle. Capitalizing on the know-how of company specialists, the system’s case base contains the detailed characteristics of over 600 tool configurations. Each case comprises over 40 pieces of data, including photos and CAD drawings. Designed with the help of plastics production and pricing experts, the project facilitated communication and a real knowledge transfer between the two sectors. Over time, the system will enable pricing experts to estimate the cost of a part in three days compared to three weeks, reducing by 30% the total cost of work required by specialists. The application may be adapted later for estimating the costs of metal parts, and Kaidara Commerce may also be used to develop a system for disseminating manufacturing data to design engineers.

A.11 MET-ERICSSON – Analyzing Telecommunication Cards and Electronic System Test Data The rapid evolution of electronic card technology makes diagnosing faults ever more difficult and delicate. Introducing a knowledge-based system that uses induction and case-based-reasoning techniques has provided us with promising new perspectives. Jean-Luc Dieusaert, PBA’s Test Engineering & Maintenance, Quality Department, MET-Ericsson MET, a subsidiary of Swedish telecom giant Ericsson, faces a critical dilemma in its manufacture and installation of public telecom network equipment, notably central telephone switches, or PBXs. At its plant in Longuenesse, in the northern French region of Pas-de Calais, MET must reduce the cost of testing and

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diagnosing faults in equipment so complex that acquiring experience with it is difficult. Diagnosing why an electronics card does not work properly is particularly representative of the problems MET employees face. The complexity of a card increases in direct proportion to the greater number of functions it provides and the number of components mounted on its surface. Methods for testing the cards have evolved along with the card technology itself, but following the path of the signal and getting information on individual components during the diagnostic phase have remained extremely delicate tasks. Furthermore, the diagnostic time allotted to technicians at the plant has declined significantly, caused in part by shorter product life cycles. Because of time constraints, the process of diagnosing and repairing cards today can make or break a card manufacturer, since the components on a card can represent over 90% of the finished product’s resale price. MET’s quality division launched a study on how to improve its diagnostics, and it launched its first project using Kaidara Advisor a few months later. The system employs two different, yet complementary, concepts to aid its diagnostics. The first, induction, provides the user with a decision tree resulting from the specified case and predefined aim (for example, identifying the faulty card in a system or detecting the fault in a card in a test). The second concept, case-based reasoning, resolves thorny problems by using a set of similar cases as a guide in researching the fault. Developed in two months, MET’s application, which today comprises 300 different diagnostic cases, processes complex electronics cards for which the level of knowledge is low. The test equipment supplies information on the fault, which is displayed on the screen. Currently used on PCs, it will be transported soon to UNIX, the test equipment’s operating system. Basing the system on UNIX allows technicians to use a single machine for testing and diagnostics, optimizing equipment use and repair time, since some diagnostics can be executed in the background of the test phase. MET’s first application aims to achieve ambitious objectives: reducing overall diagnostic time by half while maintaining an accuracy rate of 70% and cutting rejects by 25%. The system allows the company to capitalize on the expertise and know-how of its technicians, and it allows them to multiply their talents and increase their knowledge. For industrial uses, Kaidara Advisor’s client-server architecture makes it an optimal solution for large-scale deployment. MET plans to extend the application to the detection of faulty electronics cards during final system tests.

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A.12 MÜRITZ ONLINE – Tourist Information on the Internet We notice more and more that customers utilize the Internet as their source of information. CBR-Works Online enables us to act as an information broker that satisfies this demand. Manfred Becker, Müritz Online Müritz Online is a tourist information system on the Internet. It provides information on culture, nature, and leisure, and it offers available board and lodging possibilities in the Müritz, Germany, region. It was developed by tec:inno (now empolis) together with Heinzel & Röhr GmbH & Co. KG using regio-tours online and CBR-Works. It aims at leveraging the local tourism industry. The boarding assistant of Müritz Online gives intelligent support during a search. Anyone can use it. Choose a hotel, bed and breakfast, or conference site in the region. Müritz Online demonstrates a surplus value that is a pre-requisite for the success of any system. The success of this application is based on the added value for the tourist as compared to other media types. Travel services on the Internet are booming but do not offer much more than the usual catalogs of tourist companies. Müritz Online’s boarding assistant demonstrates the real possibilities of an interactive-media Internet. Müritz Online’s surplus value to its users originates from the use of knowledge on geographical aspects and similarities among different types of boarding and leisure activities. It comprises improved search results, an increased comparability of different offers, and, in particular, a quick search success. For example, to find a comfortable hotel with a pool at a certain location, the user just describes his or her wishes. He may use one of three search modes: A quick search for basic information, an expert mode with all the details, or a sophisticated mode for conference sites. Based on its knowledge of boarding facilities and the region’s geography, the system selects those offers that best meet his wishes. If there is no hotel with a pool, for instance, at the requested location, then a similar hotel within 2 kilometers will be proposed. With this intelligent search, the message “No entry found!” belongs in the past.

A.13 NATIONAL SEMICONDUCTOR – Failure Analysis of Semiconductors CBR helps identify similar recurrent issues early in the analysis and shorten the total analysis time. The resulting increased efficiencies are expected to yield savings of one million dollars per year. Arthur Hamilton WQN, Knowledge Management Manager, National Semiconductor

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National Semiconductor ships hundreds of millions of semiconductor components to thousands of customers around the world. Out of all these shipments last year, customers returned approximately 4,000 individual parts in which verified failures were analyzed. Customer-reported defect rates have shown steady improvement and sit today at approximately 30 parts per million. Although these numbers reflect National Semiconductor’s commitment to continuous improvement in reliability, today’s marketplace depends on virtually perfect product reliability for most semiconductor components. Being able to depend on deliveries with “zero defects” permits manufacturers to achieve lower costs in handling and testing of parts and also helps facilitate “just in time” manufacturing schemes. The result is a higher overall value that provides a competitive advantage to National’s customers. Therefore, when the rare failure does occur, it is an immediate cause for concern for both National and its customer. National maintains a group within its Worldwide Quality Network that performs the Product Quality Analyses, or PQAs, that provide answers to anxious customers. This group is in turn supported by inhouse analytical laboratories at worldwide sites. As technology has evolved, National has designed a progression of softwaresupport applications to aid the engineers and technicians in their work. Because National adopted the 8-Discipline (8D) problem-solving methodology, originally defined by Ford Motor Company, the support software had been structured on that model. The key information collected by the system is textual data describing conclusions drawn during the analysis process. However, because the data is entered from locations around the world, there is usually little similarity in how the same conclusions are worded. While this text data can be manipulated to create the desired 8D reports, it does not contain sufficient information to describe the nature of the failure in any consistent manner. The system is built on a Sybase database and is accessed through an interface developed in Power Builder that requires a custom client to be installed on each user’s desktop PC. Alternately, the data can also be accessed through the custom client installed on National’s thin-client network. In late 1999, National began the task of upgrading the software-support application to address the shortcomings of the existing system. In particular, there were three main points to be addressed by the new application: better knowledge of the workflow involved in each PQA, more complete information captured at each step of the analysis, and availability on National’s Intranet. However, there was one more issue that demanded attention. Engineers spent hours with the existing system attempting to search for similar PQAs from the past, because it was the only tool available. Since they were attempting to retrieve a particular PQA, the search only focused on the range of PQAs with which they had been intimately involved. Engineers seldom searched for the work done by others. Naturally, the search of the database was also faced with the natural problem of searching a relational database with a standard search tool. The selection criterion used the

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logical “and” of individual logical matches with whatever parametric values the user could muster. As could have been expected, the set of information returned was often too large to be of use, or, when the parameters were tightly defined, no matching PQAs were found. It was here that the potential of CBR provided a perfect fit. It offered to find similar cases based on “having seen something like this before.” This was precisely the approach taken by the engineers. In evaluating commercially available CBR tools, it became clear that a structured CBR approach was the one that best met the company’s needs. Kaidara Advisor was the choice for integration into the PQA tracking application. Consulting services provided by Kaidara helped define the CBR data model that would be needed. Another important difference between a search of a CBR case base versus a traditional relational database search, is that CBR introduces the concept of the relative importance of each component of the search. By applying importance weighting to each attribute, the result of a query can be manipulated and “fine-tuned” to produce the most meaningful results. The improved data model that allows the new application to work with CBR will provide the opportunity to truly capture the corporatewide knowledge gained during the extensive analysis that goes on every day. By enjoying the benefits of having better access to that knowledge, the talents and skills of the engineers and technicians engaged in that process can be effectively multiplied and used in product development and qualification activities as well as failure analysis. Being new to the use of CBR, National also benefited greatly from the close working relationship that has been developed with Kaidara. Its ability to consult in the early stages of the project provided a major boost to efforts to distill the important features of the PQA process so that an excellent data model could be developed. Kaidara’s consulting help will also allow National to integrate it seamlessly into the overall application. National looks forward to increasing its knowledge of this exciting technology and learning in what other ways it can help us “continuously improve our processes.”

A.14 NATIONAL SEMICONDUCTOR – Online Sales Support of Electronic Devices With Kaidara Commerce included in our parametric search engine, we can always provide a solution, even if we do not have the exact device that matches our customer’s requirements. They find quickly the device that best fits their needs and never get a frustrating “no result” answer. Richard Levin, Senior Manager Engineering, CommunitiesWeb Business, National Semiconductor A pioneer in the semiconductor industry, National Semiconductor was established in 1959. Today, National is one of the world’s leading suppliers of

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high-technology integrated circuits. National chips are at the heart of such systems as cellular phones, TVs, DVDs, medical equipment, computers, and thousands of other electronic devices. With headquarters in Santa Clara, California, National had annual sales of $2.1 billion in fiscal 2000 and about 10,500 employees worldwide. Started in 1994, National’s internet site (www.national.com) has offered a wide range of information about the company’s products to design engineers and sales representatives alike. Over 3,000 basic parts are described. As each part includes many options, National’s online catalog describes over 15,000 products. To add to the complexity, up to 25 key parameters can be used to select a part, making it even more difficult for an engineer to quickly find the device that perfectly matches his needs. National tried to solve this problem using objectoriented databases and even more advanced technologies like “step search.” But none of these approaches was satisfactory, and too often, design engineers got a frustrating “no result” to their requests. They needed a tool that could find the closest match to their request if their request was not in the catalog. An advanced parametric search engine, based on Kaidara’s case-basedreasoning technology was selected. A first system for micro controllers was developed with Kaidara Commerce in less than six weeks. Now, when design engineers click on National’s Community page for microcontrollers, they can choose up to 40 specific parameters, such as memory, size, temperature range, and connections to peripherals, etc. Design engineers enter the specifications of the device through a user-friendly CBR search interface and can even give priorities to key parameters. From the search results, they find the parts that best match their requirements. Once the product is found, they can get additional technical information in the product folder, as well as commercial data such as price and availability. The parametric search is available for most families of products on National’s internet site.

A.15 Neckermann – Intelligent Product Assistant An internal customer opinion poll confirmed that most customers would buy technical equipment on the web if they could count on the same assistance they would get in a dealer’s store Dr. Markus Krechting, Head of the Division at Neckermann Versand AG. In the “good old days,” if you wanted to purchase a new washing machine, you went to your local appliance dealer and asked for advice on matters such as water consumption, program types, and price. Today, shoppers can buy virtually anything via the web in the comfort of their homes and without having to stand in line in busy stores. However, the customer is mostly alone: The familiar “May I help you?” is missing; no one is at hand to assist with the process of finding and

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choosing a product. Just as in a real store, qualified sales staff are needed when complex items are available, to give advice as to a product’s suitability and to offer possible alternatives. Online shoppers need instant help or they become frustrated and move on, often never to return. Neckermann, one of the largest mail-order companies in Europe, is the first in its field to offer assistance normally provided only by trained staff in brick-andmortar stores. A product assistant helps Neckermann site users select items that fit their particular requirements and includes product categories like washing machines, mattresses, camcorders, computers, mobile telephones, TVs, DVD players, and cameras. In a purposeful, interactive dialog, the product assistant asks the customer selective questions. If a customer were looking for a washing machine, for example, the assistant would ask, “How much washing per cycle is involved?” … Or, “Are low energy and water consumption important factors?” The customer would then enter his or her requirements and rank them in importance according to his personal priorities. The assistant would then compare these preferences with available Neckermann products, then take the customer through a multidimensional assessment of relevant, matching products. The customer would then get a tabled product hit list, which allows direct comparisons of individual machines according to their characteristics. The customer simply selects the desired item and proceeds to checkout — with the feeling of having just received the best service currently possible on the web. See http://www.neckermann.de/produktberater. This intelligent product assistant was developed on the basis of empolis’s knowledge retrieval platform “orenge - empolis knowledge manager” and is expected to further improve customer satisfaction and, subsequently, increase sales for Neckermann. By providing an interactive dialog function, the solution appeals even to inexperienced users. The results pages, including the introductory comments, are generated automatically from the orenge knowledge model. Rules and similarities are defined in the knowledge model to ensure highest flexibility. To add a new product assistant is particularly easy for Neckermann staff, as it does not require any html skills, thus saving time and money. The staff can also influence the results offered to the user by specifying, for example, that the price for washing machines not be relevant.

A.16 ODENSE STEEL SHIPYARD – Improving Performances of Ship-Welding Robots With the integration of Kaidara Advisor’s CBR technology and multimedia tools, we are able to reuse the knowledge of specialists and have our operators handle most maintenance tasks. Thomas Knudsen, Automation Development, Odense Steel Shipyard

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Odense Steel Shipyard Ltd. (OSS) in Denmark is one of the market leaders in the design and building of large commercial ships. For more than a decade, it has increased performance and productivity by using huge robots that can weld 14 meters per hour. With 25 robots, OSS can weld more than 2.5 kilometers per day. Consequently, reducing robot down-time is critical for the Danish shipbuilder. To that end, OSS launched a fault-diagnosis system development project in 1997 for its Multi-Robot Gantry System, the world’s largest robot station. The station comprises 12 robot gantries located 17 meters above the shop floor. The robots are programmed offline, and the station is scheduled by a shop floor control system. Four operators supervise the robots with the help of one maintenance technician. The resulting diagnostic system, called Freya, was developed by a team of OSS maintenance specialists and operators in collaboration with Kaidara Advisor’s editor, Multicosm, the editor of a multimedia authoring tool, and experts from the Parallel Application Center. With Freya interfaced to the shop floor control system, operators are immediately aware of any robot malfunction, whereupon Kaidara Advisor searches approximately 500 cases, which describe possible robot faults and breakdowns, to give a diagnosis. Freya’s multimedia capability lets operators find not only technical documentation for robot maintenance, but also mechanical and electronic drawings, pictures, and video clips. A sophisticated multilingual user interface allows operators in Odense to work in Danish, but the same system could be used by other OSS operators in Germany, France, Finland, or Norway, using their native language. Built as a generic system, Freya is expected to be used for other robot stations and other types of equipment, such as cutting machines and cranes.

A.17 otto.de – Intelligent Product Search for Internet Shoppers With orenge we have taken yet another step towards expanding the intelligent product search procedure for our customers, thereby improving our Internet sales service even more. Dr. Thomas Schnieders, Director Neue Medien at Otto There has never been such a variety and comprehensive range of products or more multifaceted forms of advertising than there are today. However, many customers still long for the return of the “corner shop,” purely because the sheer number of different products confuses them. They often feel as if they’re not getting enough attention and helpful advice. On the Internet especially, customers are more or less left to their own devices. The other side of the coin is the opportunity that the Internet provides — the chance to create a completely new, customer- oriented world of purchasing. That’s why Otto needed a special product

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search solution — one that would give its customers comprehensive assistance on a worldwide, 24-hour basis while providing them with exactly the product they were looking for. The solution for Otto is called the “shop assistant cartridge for enfinity.” It was created by integrating empolis GmbH’s case-based retrieval technology “orenge empolis knowledge manager” into the e-Business platform “enfinity” from Intershop. On Otto’s website at www.otto.de, customers have an intelligent electronic assistance system at their fingertips; they receive expert advice and are guided to the products they want. The system combines “personal contact” and competent guidance with high-speed, 24-hour availability. Customers can formulate their search queries in their normal, everyday language, as if they were communicating with a real professional sales person. In an interactive dialog the system ascertains the customer’s individual preferences. During the subsequent product search, the system focuses totally on these preferences, displaying only relevant products when the search and retrieval procedure has been completed. A knowledge model, created especially for Otto, defines mathematical case similarities in the range of products available on the website. The knowledge model also enables fuzzy searches; the customer does not need to know the exact key term to find the desired items. Since 1999, the intelligent knowledge technology “orenge - empolis knowledge manager” has been operating successfully behind the scenes at the website of shopping24, an Otto subsidiary. The first enfinity version was integrated live into the otto.de site in October 2001. The “orenge - empolis knowledge manager” system is also at the core of the application behind Otto’s solution, supporting popular technology standards like XML and Java and adopting an intelligent “middleware” role. It can also be linked to existing databases, enabling Otto’s current databases to be integrated smoothly into the solution. As an open, modular system, “orenge - empolis knowledge manager” is extremely flexible; it can be expanded by add-on components and also can be incorporated into new technologies. The system’s technology is also highly scalable: “orenge - empolis knowledge manager” can manage even very large amounts of data. The main feature of the search-and-assistance system is its case-based search on a semantic level: “orenge - empolis knowledge manager” recognizes the meaning behind the terms in the original search query (in other words, it “reads between the lines”) and creates an innovative and comprehensive comparison to similar terms in the product description. That’s why even imprecise, wrongly-written or incomplete search queries will lead to relevant results. With the help of the “shop: assistant/enfinity cartridge,” Otto customers can always find the product they want. Even first-time Internet buyers will have no problem making their purchases, since they can express their search queries in their own particular style of language without having to use special key terms. The semantic, case-based search performed by “shop: assistant/enfinity cartridge” also considerably increases the number of relevant search results. The innovative

A.18 PSA PEUGEOT CITROEN 209

search-and-assistance system recognizes the wishes and preferences of the customer and makes these its basis for its product research. If no product can be found that matches the customer’s wishes 100%, the system will offer a choice of alternatives. The “shop: assistant/enfinity cartridge,” with its professional assistance and customer-friendly search, greatly improves customer service on Internet sales sites. The system also reaches market maturity in only a few months.

A.18 PSA PEUGEOT CITROEN – Improving Vehicle Repair to Enhance Customer Loyalty CBR enabled us to integrate, at low cost, known cases of after-sales breakdown into our diagnostic tools. The experience of after-sales service technicians is capitalized and fed back to the project team in order to improve new vehicles. Reciprocally, cases that have been identified for a new vehicle, from design to production, can be accessed by dealers worldwide to solve problems more quickly and accurately. Emmanuel Pinot, Director After-Sales and Quality Division, Citroen The AutoCBR project, launched by PSA Peugeot Citroen’s IT and customer service departments, has two goals. First, it provides repair shops with a highly efficient diagnostic tool that reduces vehicle downtime to a minimum. Second, it sends the experience acquired by 5,000 repair shops to dealerships across the world, enabling the customer-support and project teams to benefit from this shared knowledge as soon as a new vehicle is launched. The AutoCBR project had numerous difficulties to overcome. The manufacturer already had information on failure cases, but these were stored in databases from different departments (design, assembly shop, quality, etc). Each database was built with a specific purpose in mind and could not be used by after-sales service technicians. Moreover, the search engine could only find a breakdown if the technician’s case description was identical to an existing case in the database. Unfortunately, the existing case could have been in any one of 23 languages, covering all the countries where Citroen automobiles were sold. Allowing designers to capitalize on the experience of after-sales service technicians is a huge advantage for a car manufacturer. All vehicles are now built from the same platform base, thus details of any malfunction discovered by the after-sales service in a particular model can be used as soon as a new program is launched. Up to now, this information was not available in the headquarters. The implementation of this project required in-depth preliminary work. Defining a standard technical vocabulary common to all the different entities of the group was an essential preliminary step to facilitate the management of multiple languages in the diagnostic tools.

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The choice of CBR technology has enabled Citroën to solve at least two problems: the search for faults by finding similar, but not identical cases, and the ability to manage in a simple way multilingual user interfaces. Kaidara Advisor has been integrated into the diagnostic stations provided by Actia and Sagem, Citroen's partners in the development and advancement of diagnostic tools. Each case in the base which describes the symptoms of malfunction and the appropriate solution can be accessed in SGML format and translated into 23 languages. The diagnosis of multiplexed vehicles, like the Xsara Picasso, is becoming less and less intuitive. Computers show an error code when an abnormal situation is detected. A breakdown, however, may cause a cascade of errors and the cause of the original malfunction can be difficult to identify. Thanks to the case base, which includes theoretical models provided by Citroen, the system can analyze the fault and find a solution quickly. In practical terms, the shop mechanic connects the diagnostic station to the vehicle and reads the error codes, which are then automatically analyzed. The system then proposes the best known solution. New cases are reported by each after-sales service and then validated by Citroen. The case base is updated every three months by means of CD-ROM; however, workshops will soon be able to download directly from a central server. The system is currently available at more than 5,000 Citroen dealers. The second phase of the project, concerning the transfer of knowledge within the group, is still in the development stage. Despite problems with mainframe migration to PC and standards, the first common incident base is already in operation in the Rennes (France) factory.

A.19 SAG Knowledge Center – A Comfortable Way of Customer Support Using orenge, we have been able to provide our customers with our support knowhow online within a very short time. Dr. Ansgar Niehof, Manager Partner Engineering, Indirect Sales Europe, Software AG Customer support has become the main focus of eBusiness. The customer takes professional, personal support for technical problems for granted. Service hotlines are supposed to maintain existing customer relations. It is, however, increasingly difficult for staff members in first-level support to control the growing number of daily routine requests. Software AG, one of the leading suppliers of system software worldwide, solves this problem with the support knowledge management system (SKMS) “Knowledge Center.” The Knowledge Center is a complete solution for intelligent eBusiness applications in the area of eSupport. The support system allows efficient access to technical case descriptions (TCDs) via Internet and intranet, as well as via an

A.20 SIEMENS – SIMATIC Knowledge Manager 211

extranet connection. “Customers and partners of the Software AG are enabled to solve technical problems on their own,” according to Klaus Neitzert, Vice President, Customer Support Center of Software AG. Thus, the team at the Customer Support Center is relieved of frequently asked questions and can concentrate on more complex cases. With a user-friendly graphical user interface, users can enter their search query for a full text retrieval. Defined parameters make it possible for users, in particular those who are not familiar with the products of Software AG, to precisely define their search request: The system, for example, asks questions and the user can select the respective answers for limiting the result. Extended search functionality facilitates giving or verifying several discrete values, like product family or DocType. The matching documents for particular search terms are given in a result list, ordered according to their relevance. It is possible to access them directly with a link. The Knowledge Center is based on the integration of the search and assistance technology “orenge - empolis knowledge manager” into existing software applications, which are derived from the Software AG products Tamino and Bolero. Orenge was developed by empolis GmbH. Orenge is based on a knowledge model that determines similarities among defined search terms from the TCD document collection. Search terms and similarity relations between them, as well as relevant information on the TCD document, are combined to become a case. These cases are connected through an intelligent index structure, the case-retrieval network (CRN). CRN allows fast access to relevant documents. The advantage for the user is that there is no need to know the exact search term for a TCD; because of the knowledge model, the user will find what he is looking for.

A.20 SIEMENS – SIMATIC Knowledge Manager It will become essential for learning organizations to have good knowledge management. By means of CBR we are able to make a large amount of information available and reusable, for customers and staff alike. Karl- Heinz Busch, Project Manager Online Service, Siemens Automation & Drives Available on the Internet, intranet, and on CD-ROM since the Hannover exhibition in 1998, SIMATIC Knowledge Manager (SKM) is a comprehensive self-service offered by Siemens Automation & Drives. The current challenges are the increasing incoming hotline call volume, rising support costs because of a complex product portfolio, dependency on specialists and the resulting distribution of know-how as well as the rapidly growing amount of support documentation. SIMATIC Knowledge Manager enables a constant virtual availability of support specialists — anywhere and at any time. Customers and staff alike have access to

212


the knowledge base and therefore can solve problems on their own. SKM offers comfortable and intelligent access to more than 10,000 frequently asked questions and other support documents containing SIMATIC knowledge. While searching for relevant documents, SKM uses existing knowledge and the experience of the SIMATIC service technicians. Thus, it is possible to find documents and information that are not directly related to a certain product group, allowing customers and technicians to find relevant matches even when the request is vague. By using information technologies and intelligent self-service solutions, customer service and customer satisfaction were improved while, simultaneously, service costs were lowered. Because hotline calls were reduced, there was a return on investment in less than a year.

A.21 SNECMA SERVICES – Troubleshooting Boeing 737 Engines One of the main reason for selecting Kaidara’s technology was the fact that Kaidara Advisor adopts the same reasoning process as would a maintenance specialist who discovers solutions from prior experience. Richard Heider, Product Support, CFM International - Snecma Services Snecma Services is a subsidiary of the engine manufacturer Snecma. Jointly with General Electric Aircraft Engines, Snecma has developed the CFM56-3 engine that is used on Boeing 737 commercial aircraft. To reduce maintenance costs, an innovative project called Cassiopee was launched. Because troubleshooting represents about 50% of airplane downtime, and the main goal was to halve the time required for diagnosis, which means an overall reduction of 25% in downtime. In 10 years, with over 80 million flight hours accumulated, 23,000 troubleshooting reports had been sent to the manufacturer by the airlines. Stored on an IBM mainframe, this reliability data was used as the starting point for building a PC-based diagnostic system using Kaidara Advisor. A first version of the system was developed in eight months. The cases extracted from the raw data were incomplete, and as a consequence the diagnostics were fuzzy. The data was sampled and the cases were reviewed by a maintenance engineer at the rate of 15 cases per hour. The system now contains about 1,500 “clean” cases. It uses a combination of CBR and fault trees, either the ones from the Boeing maintenance manual or decision trees generated by Kaidara Advisor. Case-based reasoning is then used to retrieve similar cases so that solutions to problems can be found faster than they would be by going through the steps of the manual, were some tests might be skipped. The system is linked to the illustrated spare parts catalog and to relevant electronics technical documentation. It enables airlines to share troubleshooting experience worldwide. As a

A.22 THE COMPENDIUM

213

manufacturer, Snecma can validate the technical content of the reported cases and update regularly the case base by means of CD-ROM. Today Cassiopee is being tested by several large and small airlines throughout the world.

A.22 THE COMPENDIUM “PRECISION FROM RHINELAND-PALATINATE” – Product and Catalog on the Internet By CBR-Works the user obtains a compass that easily guides him to the assets of the compendium. Prof. Dr. W. Ehrfeld, CEO of the Institute of Microtechnology Mainz (IMM) The IMM is a center of excellence for microtechnology. Microtechnology plays a key role in the development of high-tech products and ordinary objects. It accelerates the advances in minimum invasive surgery, as well as in telecommunications. Other areas of application are new components and techniques for the automotive and aerospace industries. Microtechnology is a key technology for the next century. Additionally, the IMM hosts the European Contact Office of Rhineland-Palatinate and Hesse in Germany. It aims to strengthen collaboration and involvement of small- and medium-sized enterprises in the R&D programs of the European Union. The compendium “Precision from Rhineland-Palatinate” is a sort of Yellow Pages for high-tech purposes. If you are seeking specific manufacturing processes for certain materials, you can find them in this compendium on the Internet (http://www.imm-mainz.de). The compendium presents enterprises that offer services in the general area of high-precision tooling, including precision tooling, micro-tooling, precision optics, and electronics. If a specific service is unavailable in a region, e.g., flame-cutting, then CBR-Works offers a suitable nearby alternative, like plasma-cutting. The search for such suitable alternatives is based on the high precision know-how of the IMM. This way the compendium provides access to the high-precision tooling competence of the IMM’s experts for every interested party.

A.23 VITAC – The Virtual Technical Assistant Center for SIEMENS Employees With VITAC it is possible to take an individual’s knowledge, conserve it and quickly make it accessible to our employees — anywhere, worldwide and around the clock at any time. Eckardt Bertermann, Technical Assistant Center (TAC3) Knowledge Management, SIEMENS ICM, Munich

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Customer service has never been as important to economic success as it is today. In an age in which products and technology are developed faster and faster, it becomes more and more difficult for service staff to cope with the technical problems of their customers onsite. An intelligent knowledge management selfservice tool is required that provides employees with solutions that suit their technical requests around the clock, independent of existing mechanisms for supporting projects. The Virtual Tac Assistant (VITAC), an online support system for SIEMENS ICM employees, offers such a solution. Provided by the central product service (Technical Assistant Center = TAC3) in Munich, Germany, VITAC is available for the employees of the regional SIEMENS service centers (TAC2) for all kinds of technical requests and especially for the installation and support of SIEMENS products at the customer’s site. Employees enter their queries in English and receive suggested solutions documents, like trouble shooting cases (short descriptions of known problems and their solution), service documents and descriptions, feature documents, or test specifications. The documents are stored and administered in a special knowledge database. Orenge, empolis’s innovative search technology for knowledge management and document management, provides for fast and intelligent retrieval. “Orenge - empolis knowledge manager,” VITAC’s technological basis, permits the entry of technological requests into the retrieval system in natural language. This is possible because of a specific knowledge model in which key terms and similarities between the key terms (which are specific for the knowledge domain) are defined. VITAC “extracts relevant key terms from the text of the request and compares them to words or word combinations set down in a search index. Because similarity values are defined within the knowledge model, even non- specific requests can deliver relevant results. Documents closely related to the entered request are found quickly and easily. Since the system is available online, employees of the regional branches can use VITAC worldwide and around the clock. Furthermore, VITAC is integrated into the existing information management system at SIEMENS. Thus, SIEMENS employees can easily access the solution documents with already-existing mechanisms.

A.24 WARTSILÄ NSD – Extending Marine Engine Life Cycles and Easing Their Maintenance Some of our low-speed diesel engines are equipped with additional monitoring systems. These tools indicate the fitness of the engine, and permanent information of the engine operator is essential. Thanks to Kaidara Advisor, a failure in such a

A.25 Worldwide Content Network

215

tool can be localized and repaired within a very short time, avoiding heavy and costly engine breakdown with less downtime. Karl Svimbersky, Head of the Engine Diagnosis Division, Wartsilä NSD On a tanker, an engine breakdown can be catastrophic. The first signs of such a breakdown call for rapid action, but adequately qualified technicians are not always available on the high seas. Today, the main engine for a tanker costs between $6–$7 million, about 10% of the tanker’s cost, and has a lifetime of 15 to 20 years. To guarantee an optimum life span, Wartsilä NSD has set out to provide onboard engine operators with as much assistance as possible. Sensor-based operating systems continuously monitor the standard alarming system, mainly the piston’s running behavior and wear. The systems warn the operator before problems occur. But those surveillance systems themselves can break down without warning. In 1996, Karl Svimbersky conducted a feasibility study that chose Kaidara’s case-based-reasoning technology and charged Kaidara with developing a diagnostic system that could be used at sea. The pilot project focused on two of the most-sold monitoring tools of the Mapex product family. Kaidara created a case base from 120 carefully documented cases. Now, when an engine operator at sea encounters a problem, he phones a technician in Winterthur, Switzerland. To get information on how to fix the breakdown, the technician simply checks off a series of boxes on the screen. The diagnostic system then searches the case base for the previously solved problem that most closely matches the current case, and the system gives the technician precise repair instructions. Today, the diagnostic tool is available on the workstations of Wartsilä NSD’s maintenance staff. It will be deployed progressively onto their portables, with which they will be able access an engine’s entire history, as well as data on previous repairs from aboard a ship. Data captured on-board ship will be used to update the central case base. Highly satisfied with the results of this project, Wartsilä NSD is studying the possibility of applying the technology to the entire maintenance process.

A.25 Worldwide Content Network for Bertelsmann Created with Retrieval Technology Content is the key currency of the media business. Future competition among the large media companies will be decided by the quality of the content. The company that provides the best information and entertainment value will set the trends in market standards. Empolis has helped us to make this valuable content accessible, quickly, simply, and conveniently. Anke Felbor, Sen. Project Manager, Bertelsmann Content Network

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Bertelsmann, one of the biggest media companies in the world, has 82,162 employees in 56 countries and companies with countless product groups, like TV and radio, book publishing, newspapers and magazines, record/music labels, specialist information, printing and media services, book and music clubs, and eCommerce. The content offer is overwhelming, the potential for cooperation infinite but also long since unmanageable, with book titles, authors, musicians, CDs, newspapers, magazines, TV programs, clubs, services, and web pages numbering in the hundreds of thousands. This makes the search for responsible partners almost impossible and often stifles synergies within the concern itself. In an exemplary initiative, the Bertelsmann Content Network (BCN), headed by Rolf Schmidt-Holtz, appealed for cooperation among all company sectors. Knowledge about this content is more vital than ever in normal day-to-day routines; Bertelsmann now finds itself on a unique, forward-looking road to becoming an integrated media company. One significant aspect of this development is the internal cooperation among various company sectors, which in the future will be simplified by beQuest. For the first time ever, Bertelsmann employees can now obtain in seconds an overview of all content and services relative to any one specific topic. In addition, beQuest supplies a pertinent contact for every hit, with eMail address and telephone number. It’s this distinction that makes beQuest an invaluable tool in the development of future internal cooperative partnerships. Over 500,000 data records were provided by individual company sectors for the launching of the platform. Empolis GmbH was responsible for consolidating, making available, and locating these units of information, swiftly and comfortably. “Orenge - empolis knowledge manager,” the CBR retrieval technology of the knowledge management experts, has inbuilt functions that go far beyond those of a normal search engine: orenge doesn’t only look for exact hits, it also offers similar content as an alternative. These alternatives are in turn recognizable by their specific display in the hit list. A contact person with eMail address and telephone number is provided per hit. The result is an application that provides the basis for the development of successful synergies within the many internal operations of this media giant.

Index

acquisition 120-126, 130-137, 145, 147, 156, 160 Active Server Pages 145 adaptation 78, 121 ff administrative information 108, 111 f Adobe Acrobat 53, 144 agents 25, 75, 91 f, 100, 104, 107, 110, 165 Analog Devices 4, 28, 35, 47-50, 55, 104, 140-142, 152, 189 Ansaldo 190 f attribute 21, 27-35, 39-44, 56-58, 122f, 129, 140, 142, 147, 156160, 176, 180-182 automotive 14, 193, 213 awareness creation 117 f background knowledge 28, 31 banking 14 benefits 15, 49, 68, 75-79, 82, 86, 115, 162, 174 Bertelsmann 216 biology 14 business requirements 145-146 bol.com 191f call center 25, 79, 81, 83 case acquisition 21, 40-44, 94, 120126, 130, 132, 136 f, 145, 147, 156 case approval 44 case base editor 44

case base 11, 17-24, 29-48, 56-61, 66, 76-84, 93, 121-132, 135138, 144-149 case buffer 41, 44, 134 case maintenance 44 case query language (CQL) 44 case structure 31, 33, 42, 141, 153 case 1, 2 10-48, 54-62, 76-88, 9399, 120-161 Case-Based Reasoning 9-33 CASUEL 44, 147 catalog search 4, 18, 28, 35, 47-48, 74 f, 139-150 CBR consultant 121, 125 CBR cycle 78 CBR engine 28, 34, 149 CBR experience base 93 CFM International 212 common generic level 94-100, 104 complex equipment 4, 80, 153 f complex method 90, 92, 102-106, 108, 111, 150 complex method description sheet 105, 110 conversational CBR 4, 19, 21-24, 25, 28, 34 cookbook level 94-96, 104, 115, 133, 137 corporate knowledge 9, 11, 15, 38, 74, 78, 84 f customer relationship management, 9-10 customer service 10, 14, 62

234

Index

DaimlerChrysler 2, 36-46, 133, 192 data mining 2, 9, 11, 14, 20-21 decision tree 10, 20, 25, 32 deduction 14 development process 18, 49 diagnosis 119, 161, 198, domain model 17-45, 77-82, 92-93, 120-124, 130, 136, 141 f, 146, 153-161 dynamic induction 20, 57, 59 electronics 14, 47, 62, 142, 155 embedded electronics 56, 59 end-user 60-61, 116, 118, 122, 125, 128, 141, 156 end-user training 117 evaluation 101 f, 125, 129, 132, 172-174, 186 experience base 88-100, 104-106, 168 f experience factory 61-70, 88-93 experience management 70 expert system 4, 16-17, 25, 39, 56 expert users 120 fat client 43 fault 11-12, 18, 24, 34, 40, 57-59, 124-125, 154-159 fault isolation manual 12 fault tree 59, 156 features 20-21, 27, 40, 45, 122, 145 first-level support 25, 36 Freightliner 196 frequently-asked questions 22, 23, 76 Freya 207 front office 9-10, 15 functional decomposition 157 generalization 14, 98 Gicep Electronique 198 goal definition 90, 117

graphical user interface 106, 107 150 f

92, 100,

health care 14 help-desk 4, 15-19, 35-47, 85, 93, 101, 115-129, 135-139 hierarchical data model 156-161 HOMER 4, 35-47, 115, 121-123, 133-136, 192 f hotline 22, 39, 47, 67, 192 f hotline operator 75 HTML 59, 61, 106-107, 113 f, 145 induction 11, 14, 20, 57, 59 information gain 42, 45 information management system 60 initial domain 30, 34, 117, 119 input product 91, 104, 106, 113 INRECA 1-4 INRECA methodology 1-4, 11, 19, 63-188 integrated circuits 47 integration 60, 122, 143, 148, 167 interfaces 61, 87, 82, 122, 145 Internet 4, 15-16, 31, 48, 60, 77, 81-82, 105, 140 IESE 61-70, 165-188 ISO 9000 3, 85, 95 Java

41, 145

knowledge acquisition 39, 45-47, 120-132, 175, 181 knowledge base 56, 74, 78, 76, 155 knowledge-based system 167, knowledge container 123-132 knowledge management 79, 82 f, 118, 166 knowledge repository 37-38, 46, 115-116, 119 knowledge utilization 126

Index

Legrand 11, 199 line replaceable units

155

maintenance 12, 16, 21-46, 56, 59, 60-62, 77 f, 84, 87, 95, 98, 101, 115-137, 176 managerial processes 101, 119, 125 manufacturing 14, 16 marketing automation 10 measures of success 74 MET-Ericsson 200f method 95 f, 100, 102, 105-112 method description 108-112 method name 109, 112 mobile office 15 model hierarchy 57, 61, 157 model-based reasoning 56 modified product 106 Multicosm 207 National Semiconductor 202-205 nearest neighbor retrieval 19 Neckermann 205 novice users 120 object structure 27, 142 Odense Steel Shipyard 206 f on-board computers 59 organizational process 101, 117121, 126 organizational quality 118 ontology 166 f, 173, 175 otto.de 207 f output product 91, 100-106 parametric search 17, 48-50, 55, 140 f, 189 f precision 22, 35, 50 problem description 37-41, 46, 7980, 124-127, 155, 157, 160 problem-solving 21, 36, 67, 116, 120, 122, 167, 182 process 88-92, 99-114

235

process description sheet 105 f, 106-102 process goal 106 process model 36, 85, 88-103, 129, 132-135, 145, 157, 167, 188 process name 106 process quality 115, 118 product 88-92, 99-114 product description sheet 105, 108 product name 106, 108 project goal 101, 118-119, 125, 129 project management 87 project name 106, 108-109, project plan 74, 87-103, 126, 129, 135, 162 project planning 88 f, 101, 103, 126, 129 project team 74, 117-132 PSA Peugeot Citroen 209 quality control 18, 29, 33, 35, 87 quality improvement paradigm 8891, 95, 99, 166 f quality standards 100 query 9, 17, 22, 28-31, 42-44, 5053, 59, 140, 143 rapid prototype 120, 129, 132 recall 13, 22, 35 recipe 95-99, 106-112, 115, 133139, 145, 153, 161 recycle 128 refine 48, 128-129, 140, 150, 157 f reliability analysis 15 remedy 40, 125 repository 64, 167 requirements 47-50, 53, 75-77, 100 f, 104, 108, 121, 129-132, 139, 141, 145-146, 177 ff resources 38, 91 f, 100, 103, 116121, 133-136 retain 36-37, 128

236

Index

retrieve 11, 17, 22, 28, 35, 36, 45, 127, 173 f reuse 3, 34-37, 47, 88-95, 98, 100, 119, 122, 127-128, 135, 150, 165-188 revise 36-37, 127 sales

2, 4, 10-11, 15-16, 28, 47-50, 75, 81-83, 141, 145, 146-147 sales automation 10 sales materials 145 sales support 15, 28 savings 49, 55 search engine 49-50 second level support 74 self-diagnostic 156 self-service 74, 78, 81 f Siemens 211 f, 214 SIMATIC Knowledge Manager 23, 211 similaritiy 28, 34-35, 54, 77, 94, 110, 125 f, 128 f, 139, 143, 149, 167, 177 ff simple method 92, 102, 105-113 simple method description sheet 115 smarter business decisions 2, 9-10 Snecma Services 212 Software AG 210 f software engineering 61ff 165ff, software process modeling 88, 91, 94, 99, 103 specific project level 94 f, 97, 99 SPICE 3, 87, 95 stakeholders 169, 188 statistics 4, 16 structural CBR 4, 21, 26-36, subprocesses 92, 102, 106, 111f system administrator 37, 44, 78 system development 117-121, 135 system guided dynamic induction 20 system implementation 117 system integration 117

system specification 117 system use 59, 117, 125-127, 134137 system verification 117 technical equipment 115 f, 154-157 technical process 100 f, 116-121, 126, 136 technical support 9, 11-12, 15, 18, 48 telecommunications 14, 213 testing 12, 49, 79, 101, 121, 135, 148, 150 textual CBR 4, 21-23, 28, 34 TGV 4, 35, 56-62, 156-165 tool selection 121 transportation 14, 56, 62 transportation market 56 troubleshooting 74, 212 tourist information 202, 213 ultrasonography 193 unconfirmed case 128 user feedback 79f user guided dynamic induction utilization process 117, 120

20

vertical platform 18-19, 141, 156 vocabulary 32, 120-129 Wartsilä NSD 215 Web interface 144 f Web server 10, 41, 61 worldwide 24/7 support 81 XML

145

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