Intelligent Knowledge-Based Systems: Business and Technology in the New Millennium

INTELLIGENT KNOWLEDGE-BASED SYSTEMS

BUSINESS AND TECHNOLOGY IN THE NEW MILLENNIUM

VOLUME 1 KNOWLEDGE-BASED SYSTEMS

INTELLIGENT KNOWLEDGE-BASED SYSTEMS

BUSINESS AND TECHNOLOGY IN THE NEW MILLENNIUM

VOLUME 1 KNOWLEDGE-BASED SYSTEMS

Edited by CORNELIUS T. LEONDES

University of California, Los Angeles, USA

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Library of Congress Cataloging-in-Publication Data Intelligent knowledge-based systems: business and technology in the new millennium. / edited by Cornelius T. Leondes. Includes bibliographical references and index. Contents: v. 1. Knowledge-based systems-v. 2. Information technologyv. 3. Expert and agent systems-v. 4. Intelligent systemsv. 5. Neural networks, fuzzy theory and genetic algorithms. ISBN 1-40207-746-7 (set)-ISBN 1-40207-824-2 (v.1)-ISBN 1-40207-825-0 (v.2)ISBN 1-40207-826-9 (v.3)-ISBN 1-40207-827-7 (vA)-ISBN 1-40207-828-5 (v.5) ISBN 1-40207-829-3 (electronic book set) (LOC information to follow.)

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CONTENTS

Foreword Preface

VII

IX

List of contributors

Xlll

Volume 1. Knowledge-Based Systems

1. Platform-Based Product Design and Development: Knowledge Support Strategy and Implementation 3 XUAN F. ZHA AND RAM D. SRIRAM

2. Knowledge Management Systems in Continuous Product Innovation

36

MARIANO CORSO, ANTONELLA MARTINI, LUISA PELLEGRINI, AND EMILIO PAOLUCCI

3. Knowledge-Based Measurement of Enterprise Agility NIKOS

c.

67

TSOURVELOUDIS

4. Knowledge-Based Systems Technology in the Make or Buy Decision in Manufacturing Strategy 83 P. HUMPHREYS AND R. MCIVOR

5. Intelligent Internet Information Systems in Knowledge Acquisition: Techniques and Applications 110 SHIAN-HUA LIN

6. Aggregator: A Knowledge Based Comparison Chart Builder for eShopping

140

F. KOKKORAS, N. BASSILIADES, AND I. VLAHAVAS

v

vi

Contents

7. Impact of the Intelligent Agent Paradigm on Knowledge Management

164

JANIS GRUNDSPENKIS AND MARITE KIRIKOVA

8. Methods of Building Knowledge-Based Systems Applied in Software Project Management 207 CEZARY ORLOWSKI

9. Security Technologies to Guarantee Safe Business Processes in Smart Organizations ISTVAN MEZGAR

10. Business Process Modelling and Its Applications in the Business Environment

288

BRANE KALPIC, PETER BERNUS, AND RALF MUHLBERGER

11. Knowledge Based Systems Technology and Applications in Image Retrieval EUGENE DI SCIASCIO, FRANCESCO M. DONINI, AND MARINA MONGIELLO

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FOREWORD

Almost unknown to the academic world, and to the general publi c, the application of intelligent knowledge-b ased systems is rapidly and effectively changing the future of the human species. Today, hum an well-being is, as it has been for all of history, fundamentally limited by the size of the world economic produ ct. Thus , if human economic well-being (which I personally define as the bottom centile annual per capita income) is ever soon to reach an acceptable level (e.g., the equivalent of $20,000 per capita per annum in 2004), then intelligent knowledge-b ased systems must be employed in vast quantiti es. This is pr imarily becau se of the reality that few human s live in efficient societies (such as the United States, Canada, Japan, the UK, France, and Germany, for example) and that inefficient societies, many of which are already large, and growing larger, may require many decades to become efficient. In the meantime, billions of people will continue to suffer economic impoverishm ent-an impoverishment that inefficient hum an labor cannot remedy. To create the extra economi c output so urgently needed, we have only one choice : to employ inte lligent knowledge-based systems in great numbers, which will produ ce eco nomic output prodigiously, but will consum e hardly at all. This multi-volume major reference work , architect ed by its editor, Cornelius T. Leond es, provides a wealth of'case studies' illustrating the state of the art in intelligent knowledge-ba sed systems. In contrast to ordinary academic pedagogy, wh ere 'ivory tower' abstraction and elegance are the guiding principles, practical applications require detailed relevant examples that can be used by practitioners to successfully inno vate new operational capabilities. Th e economic progre ss of the species depends upon the vii

viii

Foreword

flow of these innovations, which requires multi-volume major reference works with carefully selected, well-written, and well-edited 'case studies.' Professor Leonde s knows these realities well, and the five volum es in this work resoundingly reflect his success in achieving their requir ements. Volume 1 addresses Knowledge-Based Systems. Thes e eleven chapters consider the basic question ofhow accumulated data and staffexpertise from business operations can be abstracted into valuable knowledge, and how such knowledge can then be applied to ongoing operations. Wide and represent ative situations are considered, ranging from produ ct innovation and design, to intelligent database exploit ation , to business model analysis. Volume 2, Informati on Technology, addressesin ten chapters the important question of how data should be stored and used to maximize its overall value. Case studies consider a wide variety of application arenas: produ ct development, manufacturing, product management, and even product pricing. Volume 3 addresses Expert and Agent Systems in ten chapters. Application arenas considered include image databases, business process monitoring, e-commerce, and production planning and scheduling. Again, the coverage is designed to provide a wide range of perspectives and business-function con centrations to help stimulate inno vation by the reader. Volume 4, Intelligent Systems, provides nine chapters considering such topic s as mission-critical functions , businessforecasting, medical patient care, and produ ct design and development. Volume 5 addresses Neural Networks, Fuzzy Theory, and Genetic Algor ithm Techniques. Its ten chapters cover examples in areas including bioinformatics, product Iifecycle cost estimating, produ ct development, computer-aided design, produ ct assembly, and facility location . The examples assembled by Professor Leondes in this work provide a wealth of practical ideas designed to trigger the development of innovation. The contributors to this grand proje ct are to be congratulated for the major efforts they have expended in creating their chapters. Humans everywhere will soon ben efit from the case studies provided herein. Intelligent Knowledge-B ased Systems: Business and Technology in the New Millennium, is a reference work that belongs on the desk of every innovative technologist. It has taken many decades of experience and unflagging hard work for Professor Leondes to accumulate the wisdom and judgment reflected in his editorial stewardship of this reference work . Wisdom and judgment are rare-but indispensablecommodities that cann ot be obtained in any other way. The world of innovative technology, and the world at large, stand in his debt . Robert Hecht-Nielsen Computational N eurobiology Institute for Neural Computation Department of Electric al and Computer Engineering University of California, San Diego

PREFACE

At the start of the 20 th cent ury, national economies on the international scene were, to a large extent, agriculturally based. T his was, perh aps, the dominant reason for the protraction, on the internation al scene, of the Great Depression , which began with the Wall Street stock market crash of October, 1929. After World War II the trend away from agric ulturally based economies and toward industrially based econo mies continued and strengt hened . Indeed, today, in the United States, approximately only 1% of the population is involved in the agriculture requirements of the US and, in addition, provides significant agriculture exports. This, of course, is made possible by th e greatly improved techniqu es and technologies utilized in the agriculture industry. The trend toward indu strially based economies after World War II was, in turn, followed by a trend toward service- based economies. In th e U nited States today, roughly over 70% of the employment is involved with service indu stries-and this percentage continues to increase. Separately, the electronic computer indu stry began to take hold in the early 1960s, and thereafter always seemed to exceed expec tations. For example, the first large-scale sales of an electro nic computer were of the rEM 650. At that time, projec tions were that the total sales for the United States wou ld be twenty-five rEM 650 computers. Before the first one came off the proj ection line, rEM had initial orders for over 30,000. T hat was thought to be huge by the standards of that day, and today it is a very miniscule number, to say nothing of the fact that its computing power was also very miniscule by today's standards. Computer mainframes continued to grow in power and complexity. At th e same time, Gordon Moore, of "M oore's Law" fame, and his colleagues founded IN TE L. Then around 1980 M [CRO SO FT was ix

x

Preface

founded, but it was not unt il the early 1990s, not that long ago, that WINDOWS were created- incidentally, after the APPLE computer family started. The first browser was the NETSCAPE browser, which appeared in 1995, also not that lon g ago. Of course, computer networking equipment, most notably C ISCO 's, also appeared about that time. Toward the end of th e last century the "DOT CO M bubble" occurred and "burst" around 2000. Co ming to the new millennium, tor most of our history the wealth of a nation was limited by the size and stamina ofthe work force. Today, nation al wealth is measured in intellectual capital. N ation s possessing skillful peop le in such diverse areas as science, medi cine, business, and engineering produce inno vations that drive the nation to a higher quality oflife. To better utilize these valuable resources, intelligent, knowledgebased systems technology has evolved at a rapid and significantly expanding rate, and can be utilized by nations to improve their medical care, advance their engineering technology, and increase th eir manufacturing productivity, as well as playa significant role in a very wide variety of other areas of activity of substantive significance. T he breadth of the major application areas of intelligent, know ledge-based systems technology is very impressive. These include the following, among other areas. Agri culture Business C hemistry Co mmunications Co mputer Systems Education Management Law Manufacturin g Mathematics Medi cine Meteorology

Electroni cs Engineering Environm ent Geo logy Image Processing Information Military Mining Power Systems Science Space Technology Transportation

It is difficult now to imagine an area that will not be tou ched by intelligent, knowledge-based systems techn ology. Th e great breadth and expanding significance of such a broad field on the international scene requires a multi- volume, major reference work to provide an adequately substantive treatment of the subject, "Intelligent Knowledge-Based Systems: Business and Technology of The New Millennium." T his work con sists of the following distin ctly titled and well integrated volume s. Volume Volume Volume Volume Volume

I. II. III. IV V

Knowled ge-Based Systems Inform ation Technolo gy Expert and Agent Systems Intelligent Systems Ne ural Networks

This five-volume set on intelligent knowledge-based systems clearly manifests the great significance of these key technologies for the new economies of the new millennium. The authors are all to be highly commended for their splendid contributions, which together will provide a significant and uniquely comprehensive reference source for research workers, practitioners, computer scientists, students, and others on the international scene for years to come. Cornelius T. Leondes University of California, Los Angeles January 5, 2004

CONTRIBUTORS

VOLUME 1: KNOWLEDGE-BASED SYSTEMS N. Bassiliades Department of Informatics Aristotle University of Thessaloniki Thessaloniki GREECE Chapter 6. Aggregator: A Knowledge-Based Comparison Chart Builderfor eShopping

Peter Bernus Griffith University School of CIT Nathan Queensland AUSTRALIA Chapter 10. Business Process Modeling and Its Applications in the Business Environment Mariano Corso Department of Management Engineering Polytechnic University of Mailand Milano ITALY Chapter 2. Knowledge Management Systems in Continuous Product Innnovation xiii

xiv

Contributors

Eugenio di Sciascio Dipartimento Elettrotecnica ed Elettronica Politecnico di Bari Bari ITALY Chapter 11. Knowledge-Based Systems Technology and Applications in Image Retrieval Francesco M. Donini Universita della Tuscia Viterbo ITALY Chapter 11. Knowledge-Based Systems Technology and Applications in Image Retrieval Janis Grundspenkis Faculty of Computer Science and Information Technology Riga Technical University Riga LATVIA Chapter 7. Impact of the Intelligent Agent Paradigm on Knowledge Management P. Humphreys Faculty of Business and Management University of Ulster Northern Ireland UNITED KINGDOM Chapter 4. Knowledge-Based Systems Technology in the Make-or-Buy Decision in Manuftcturing Strategy Brane Kalpic ETI Elektroelement Jt. St. Compo Izlake SLOVENIA Chapter 10. Business Process Modeling and Its Applications in the Business Environment Marite Kirikova Faculty of Computer Science and Information Technology Riga Technical University Riga LATVIA Chapter 7. Impact of the Intelligent Agent Paradigm on Knowledge Management F. Kokkoras Department of Informatics Aristotle University of Thessaloniki

Thessalon iki GREECE

Chapter 6. Aggregator: A Knowledge-Based Comparison Chart Builderf or eShopping

Shian-Hua Lin Department of Computer Science and Information En gin eering National C hi Nan University Taiwan REPUBLI C OF CHINA Chapter 5. Intelligent Internet biformation Systems in Knowledge Acquisition: Techniques mid

Applications

Antonella Martini Faculty of Engineering University of Pisa Pisa ITALY Chapter 2. Knowledge Management Systems in Continuous Product Innovation R. Mcivor Faculty of Business and Managemen t Un iversity of Ul ster UNITED KINGDOM Chapter 4. Knowledge-Based Systems Technology in the Make-or-Buy Decision in Manufizcturing Strategy Istvan Mezgar CIM R esearch Laboratory Comp uter and Automation s Research Institute Hungarian Academy of Sciences Bud apest HUNGARY Chapter 9. Security Technologies to Guarantee Safe Business Processes in Smart Organiz ations Marina Mongiello Dipartiment o di Elettrote cn ica ed Elettronica PoIitecnico di Bari Bari ITALY Chapter 11. Knowledge-Based Systems Technology and Applications in Image Retrieval Ralf Muhlberger University of Queensland Information Technology & Electrical Engineering

xvi

Contributors

Queensland AUSTRALIA Chapter 10. Business Process Modeling and Its Applications in the Business Environment

Cezary Orlowski Gdansk University of Technology Gdansk POLAND Chapter 8. Methods ~ c:> '" C C

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Figure 1. Knowledge Management in Product Innovation-s-a Literature Review.

the knowledge-creating PI system, and ii) the emphasis in the Knowledge Management process. The first dimension summarises the degree to which contributions in literature progressively enlarge the boundaries of the PI process to take into account possible sources or uses of knowledge, both internal and external. On this dimension, management scholars in the cognitive approach progressively shifted attention from knowledge integration among PI phases within the same project, to knowledge integration among different PI projects over time and, finally, to knowledge integration with internal and external partners outside the traditional boundaries of product development. The second dimension-the emphasis in the KM process-is related to the level to which the different contributions consider the overall process of knowledge creation and management. A KM process is in general described as a sequence of three or more sub-processes or phases [46; 47; 48], not necessarily sequentially or hierarchically ordered: - Knowledge transferring and sharing (Knowledge Transfer); - Knowledge capitalisation and reuse (Knowledge Capitalisation) - Knowledge assimilation and generation (Knowledge Creation). Literature showed different levels of completeness in analysing the Knowledge Management process going from mere attention to information and knowledge sharing, to knowledge codification and storing for reuse and, finally, to the overall process of knowledge creation and management.

44

Corso et al.

The combination of these two dimensions produces a bi-dimensional space where evolutions over time ofthe main streams ofliterature can be mapped (Fig. 1). However readers should be warned about the fact that overlapping and fuzzy borders between different streams exist'. 3.1. Main streams in literature

Concurrent engineering Since the early '80s Concurrent Engineering (CE) has been considered the new paradigm for product development. When compared with more traditional approaches, CE is characterised by stronger emphasis on integration among different product development phases: phased program planning is replaced by the joint participation of different functional groups in the product development process [10]. This creates many advantages that have been highlighted in literature; for example, shorter time to market [49], better communication and less inter-functional conflict [1; 10; 50; 51; 52; 53], fewer reworks and loops and, consequently, higher quality and lower cost products [49]. As far as Knowledge Management is concerned, CE played a key role in the development of a cognitive perspective in Product Innovation. CE, in fact, stressed the importance of a richer and more continuous communication within the development process, shifting attention from the transfer of articulated and complete information to the sharing of knowledge often in tacit forms. The need for overlapping ongoing activities, as a matter of fact, implies working in cross-functional groups-often co-located-where stronger and richer communication is fundamental to making innovation and co-ordination possible [54]. As the main emphasis is on the integration and speed ofa specific innovation process, knowledge is shared and socialised in tacit and contextual forms while limited emphasis is placed on codifying knowledge or on abstracting and generalising from current experience to foster future innovation. Flexible design With CE management attention shifted from designing structures for innovation to designing the innovation process, thus inducing a more holistic perspective to product development. In KM terms, however, CE limited its focus to the implementation and sharing of existing knowledge, without taking into account the overall learning process. CE, moreover, maintained a rigid separation between the locus of knowledge generation, when the product concept is generated, and the locus of implementation, when the product is actually developed [52; 53; 54; 56]. Iansiti (1995) highlights how "concurrent engineering models normally do not imply the simultaneous execution ~f conceptualisation and implementation, but rather thejoint participation if different functional groups in the execution if these separate and sequential sets of activities"2 along which the product is 1 In the analysis of literature we consider articles published in major English-language North American and European journals. These studies have been selected on the basis of the citation degree by other researchers. 2 Iansiti. M. (1995). p. 41.

Knowledge management systems in continuous product innovation

45

defined, designed, manufactured and launched in the market. But in extremely turbulent environments, unpredictable technological and market changes create deadlines that even the fastest development process cannot meet. In such environments, the ability to react to newly discovered information during project evolution becomes the key factor for the competitive advantage itself. In this context a new and more flexible model of product development is emerging [56; 57; 58), which, in deep contrast with the traditional one, implies the ability to move the concept freeze milestone as close to market introduction as possible. This implies the ability to overlap the two fundamental development phases: on the one side, the concept development (analysis of customer needs and technological possibilities together with their translation into a detailed concept) which aims at specifying product features, architecture and critical components, and, on the other, the implementation phase (translation of the product concept objectives onto a detailed design and, thus, onto a manufacturable product). In a Knowledge Management perspective this means taking into account and fostering rapid learning loops within the overall product development process. Multi-project management

Starting in the late '80s a new stream of literature emerged highlighting the potential limits of CE in a long-term horizon. One of the main criticism was that while emphasising integration among PI phases, CE potentially isolates each innovation process from the rest of the organisation. As Product Innovation is becoming more and more frequent and resource consuming, however, effectiveness in managing the single product is not enough. Success depends even more on exploiting synergies amongst projects by both fostering commonality and reuse of design solutions over time, thus shifting attention to project families. In particular, re-using design solutions [5; 6] and focusing on product families [8; 9] means concentrating attention on the architecture of the product, that is on the way components and skills are integrated and linked together into a coherent whole [30]. In this way it is possible to devote more attention to managing sets of related projects, thus avoiding inefficiencies connected with individual projects 'micromanagement' and obtaining better performances in terms of common parts ratio, carried-over parts ratio and design reuse [2]. Although Multi-Project Management was nothing new in management literature, the problem of portfolio management in Product Innovation could hardly be linked to the traditional applications in engineering projects. The latter, in fact, focuses on contexts where the main problem is managing interdependencies among simultaneous projects deriving from the sharing of a common resource pool [59]. In PI, on the contrary, most interdependencies derive from transfer of knowledge and solutions between projects over time [1; 60; 61]. Analysing interdependencies, some authors focus on the actual object of the interaction [34; 62] distinguishing between interactions related to the exchange of tangible technological solutions (e.g., parts, components), of codified knowledge (patents, processes and formulas) and of non codified know how, generally person-embodied. Others focus on the scope of the interaction [39), distinguishing between component level and architectural level. A third, and last, group of contributions focuses on the

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Corso et al.

approach in the tranifer process, that can either be reactive-when solutions and knowledge from past projects are ex post retrieved and reused-or proactive, when solutions are deliberately developed to be used in the future for projects that have not yet been planned [61; 7]. Many authors showed how traditional reactive policies based on carry over of parts and subsystems are intrinsically limited and may also be detrimental to innovation [1; 63]. Excellent companies instead use proactive policies where ex-ante efforts are made to predict characteristics and features of new parts and subsystems to suit future applications. Depending on the architectural or component knowledge embodied in the solutions, these proactive polices are named "product platforms" or "shelf innovation" [5; 6; 7; 8; 9; 64]. The urgency to manage interdependencies among projects over time induced many companies to conceive new organizational and managerial approaches. In many cases this entailed the introduction of new roles and intermediate decision levels, such as Product Manager, Platform and Program Manager [6; 65]. Cusumano and Nobeoka (1992-n. 2) explicitly introduce commonality and reuse of design solutions over time in their strategy-structure-performance framework, systematising the management literature on the PI process in the auto industry. Other authors stressed the importance of developing product plans at companies or product family levels [5; 6; 61]. In particular Wheelwright and Sasser (1989-n. 5) emphasise the necessity of a 'New Product Development Map' which allows managers to understand technological and market forces driving past and present evolution ofproduct lines from one generation to another, thus providing "a context for relating concurrent projects to one another'", Linking the intensity of project changes to manufacturing process innovation, Wheelwright and Clark (1992-n. 6) allege that many NPD failures are caused by the lack of an aggregate plan for coordinating existing projects. Meyer and Utterback (1993-n. 9) and Sanderson and Uzumeri (1995-n. 8) emphasise not only the necessity to shift attention from single projects to product families, in order to enable the development and sharing of key components and assets, but also the opportunity to go beyond individual product families, in order to consider relationships between product families, as they enable higher commonality in technologies and marketing. More in particular, Meyer and Utterback (1993-n. 9), connecting product families to the management of a firm's core capabilities, develop a normative model to map product families and evaluate the dynamics of the embedded core capabilities. The resulting product family map developed into four hierarchical levels-the family itself, the platforms, the product extensions and, then, the single products-and constitutes the basis to assess the evolution of a firm's core capabilities, analysed into their four key dimensions-product technology, customer needs comprehension, distribution and production. Sanderson and Uzumeri (1995-n. 8), instead, trace back Sony's decade-long dominance in Walkman production to its skill at managing the evolution of its product families and, more exactly, to four specific tactics of product planning: the variety-intensive product strategy, the multilateral management of product design, the judicious use of industrial design and the commitment to minimizing design cost. "Wheelwright and Sasser (1989-n. 5), p. 125.

Knowledge management systems in continuous product innovation

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In all casesproduct solutions are considered the most powerful vehicles to accumulate and transfer knowledge from one product to another. Organisational learning

A rich stream of literature from different research fields emerged in the last decade dealing with organisational learning in Product Innovation [42]. Compared with the previously described streams these contributions place much more emphasis on the dynamics of knowledge creation and transfer over time. As in Multi-Project Management, the focus is on the relations among projects over time rather than on the single development process. While Multi-project literature mostly focuses on knowledge embodied in design solutions, organisational learning literature emphasises the importance of transferring knowledge also in tacit form or embedding it into processes and organisational routines [34; 44; 66]. While Multi-Project literature, moreover, considers the reapplication of knowledge as a rather automatic process, Organisational Learning literature emphasises how the issue is too articulated to be dealt with normatively [66; 67] and how learning and reuse of knowledge may face barriers at both the organisational and the individual levels, calling therefore for an aware support by management [29]. In particular many potential difficulties entangle the process oflearning across different projects [11; 12; 68]. Von Hippel and Tyre (1995-n. 68) focus their attention on problems connected with knowledge reuse when dealing with innovative projects. Imai, Nonaka and Takeuchi (1995-n. 44) devote their attention to the urgency to unlearn past lessons in order to eliminate dangers in terms of NPD toughening. Arora and Gambardella (1994n. 26) emphasise how knowledge has to be abstracted from each specific project and generalized in order to extend past experience to future PI projects. Abstraction and generalization entail, respectively, the selection of some relevant information and elements, as well as the definition of those criteria which allow knowledge to be applied. Only abstract and general knowledge allow the creation of both a long-term competitive advantage in different product/market segments and new businesses: firms competitiveness comes from the ability to build at the best cost and time conditions with respect to competitors, the key competencies to develop new products [16]. Other authors stressthe importance ofthe role ofmanagement in designing adequate enablers for learning to take place in Product Innovation [34; 44]. Bartezzaghi et al. (1997-n. 12) suggests that designing adequate vehicles to support knowledge storing and dissemination over time is a fundamental lever to foster innovation. These vehicles should be designed coherently with the organisation's corporate and national culture [69]. Nonaka (1991-n. 34) and Hedlund (1994-n. 43) classify the different processes ofknowledge conversion and introduce the concept ofknowledge creating spiral: new knowledge is generated through cycles of knowledge socialisation, externalisation, combination and internalisation. Nonaka and Konno (1998-n. 70) reaffirm the above model, describing a 'space' (the concept ofba') that is conducive to knowledge creation. Other authors focus on the concept of' communities of practice' which is a special type of informal network that emerges in an organization and to which access is

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dependent on social acceptance [71; 72; 73; 74]. As these communities playa role in the creation of collective knowledge, managers should respect the 'situated activity' in order to develop them. Most contributions, however, share the underneath assumptions that Product Innovation is the outcome of NPD projects over time. Downstream phases are considered important only as far as they can provide information for feeding next generation product development, or even constraints that should be anticipated and considered during development [1]. Some contributions, however, diverge from such perspective, indicating the necessity to extend innovative efforts to the overall product life-cycle [13; 62; 75; 76; 77]. Itami (1987-n. 62) suggests how excellent company experimentation and technological strategies are often aimed at generating knowledge through trial and error learning processes that leave the lab and extend to production activities and market. In this way it is possible to start preventive or experimental commercialisation, allowing an important facing with consumers in a phase in which it is still possible to introduce modifications and technological improvements. Bartezzaghi et al. (1999-n. 78) and Corso (2000-n. 13) summarise by stressing the importance of shifting attention from product development to Continuous Product Innovation (CPI), a cross-functional knowledge-based process leading to life-long Product Innovation that implies Product Innovation along all its life cycle. Product development should be considered only as the first, yet important, phase in Product Innovation, which is also extended to down-stream phases such as manufacturing, and after-sale services. While in traditional models feed-back is stored for feeding next generation product development, in Continuous Product Innovation all stages in the product life cycle are potential opportunities for innovation. Inter-organisational design

Starting from the CE concept of inter-functional teams, two partially overlapped streams recently emerged in product development literature further expanding the scope of the PI process to take into account the importance of assimilating and integrating knowledge from outside the traditional boundaries of R&D. Some authors stressed the importance ofdesigning new roles within R&D, such as gatekeepers [50; 78; 79], in order to bridge to the external environment. Others stressed the importance of direct and early involvement of customers and suppliers in inter-organisational groups [1; 11; 80; 81]. More and more contributions stress how for the single firm external complexity hinders the possibility to manage the knowledge system supporting the whole Product Innovation process: not only researchers but also companies themselves become specialised nodes within complex and dynamic knowledge creating networks. Reid et al. (2001-n. 81) highlights alliance form as the optimal collaborative structure for the knowledge-based enterprise, proposing a research model based on an alliance life cycle. Analysing how inter-organisational groups develop knowledge in the PI process, some authors focus on the network, comparing different industries, and highlighting interface aspects facilitating inter-organisational collaboration. Studies are based on

Knowledge management systems in continuous product innovation

49

evidence from different industries, such as automotive [1; 82), ICT (57), automation technology [83], packaging equipment [84), biotechnology [85], pharmaceutical [86; 87], and aeronautical [88]. A second group of contributions investigates the specific relationships the firm builds with actors belonging to the supply chain (vertical agreements), with competitors (horizontal agreements) and with complementary firms and external institutions (cross agreements). In the past, contributions regarding vertical agreements with customers showed how early customer involvement can significantly enhance the success probability in innovation activities and how such involvement should take place in different situations [89; 90; 91; 92]. More recently contributions started focusing on supplier involvement, with emphasis placed on the critical role played by suppliers in the achievement of high performances in Product Innovation [84; 93]. A relevant group of contributions analyses the Japanese approach emphasising how creation of tight relationships with suppliers is based on strong interactivity, continual information exchange and the deep reciprocal reliance [1; 82; 94; 95]. Others explicitly focus on Knowledge Management, with emphasis on the advantages of managing suppliers as sources of knowledge rather than vendors of parts and equipment [10; 11; 96; 97; 98]. A final group of contributions enlarge the scope of the knowledge creating system outside the boundaries of the supply network. Clark and Fujimoto (1991-n. 1) highlight the increasing role played by horizontal agreements with competitors, emphasising how their objectives are shifting from pure market control and influence on standards and regulations to the joint development of technologies and components. Other authors stress the importance of cross agreements with complementary firms and external institutions in order to develop technological breakthroughs, or simply to scan technological opportunities and assimilate knowledge [93; 99; 100; 101]. 3.2. The literature evolutive trend: towards KM configurations

Following a Knowledge Management perspective, literature can be analysed in terms of the scope of the knowledge creating system underpinning the Product Innovation process and of the emphasis placed in analysing the different phases of the knowledge creation and management process. This analysis shows how literature, starting from Concurrent Engineering, progressively enlarged the scope of the PI process shifting from the need to remove cross-functional barriers within the same project, to the need to remove time separation which isolates by different PI projects and, finally, to the opportunity to build inter-organisational relationships. Similarly, it shows how emphasis in the KM process progressively went from mere information and knowledge exchange to knowledge embodiment and transfer for reuse and, finally to the overall process of knowledge creation, diffusion and refinement over time. Each of the above-mentioned developments represents a gradual evolution rather than an abrupt leap; this evolution has progressively added and refilled the previous results, rather than contrasting and substituting them. The strong emphasis CE placed on the need to overcome the functional barriers isolating knowledge sources involved in a project has constituted the starting point from which literature has indicated the

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opportunity to search synergies both internally, with other projects, and externally, with knowledge sources outside the organizational borders. Similarly, the CE emphasis on knowledge exchange constitutes the foundation for knowledge reuse and creation. In this sense, each single stream in the evolution of the two considered variables-scope of the Knowledge creating system-and emphasis in the KM process-presupposes and, hence, comprehends the former contributions. In a Knowledge Management perspective, this means that Product Innovation literature shows a unique trend starting from CE and moving towards a more systemic and comprehensive approach to Knowledge Management in PI. The diffusion of new organizational models based on distributed teams and crosscompany collaboration, and the availability of tools based on new ICT, challenged the traditional approaches to the creation and sharing ofknowledge, requiring management practitioners and scholars for more aware and innovative KM approaches. But while there is a growing need to manage Knowledge in PI, traditional literature was lacking of empirically tested supportive models to help managers understand 1) the processes through which knowledge is managed across wide and dynamic networks, 2) the ICT tools and the organizational/managerial mechanisms supporting such processes and 3) their impact on performance. In the last few years, different contributions tried to fill this gap. Most articles highlight the existence of different approaches characterized by a different emphasis on the use of technologies and organizational and managerial tools for managing the flow of knowledge in codified or articulated forms. In particular, in Hansen et al. (1999-n. 102) such Configurations are named Codification Strategy (knowledge is codified and stored in databases where it can be accessed and used easily by anyone in the company) and Personalization Strategy (knowledge is closely tied to the person who developed it and shared mainly through direct person-to-person contacts: computers chief purpose is to help people communicate knowledge, not to store it). Corso et al. (200l-n. 103) have identified three different ICT Approaches SMEs follow in the adoption of ICT in Product Innovation by drawing evidence from analysis of a multiple-case study on 47 SMEs in Northern and Central Italy. On the basis of a contingency framework, such approaches can be related to product and system complexity. More exactly, the empirical research has clearly shown how SMEs are influenced in their choice by product complexity, acting as a deterrent to ICT tool adoption in the PI process, and by system complexity, determining the need for technological co-ordination between SMEs and their customers. While confirming a general gap in the adoption of ICT tools by SMEs, Corso et al. (2001-n. 103) shows how the latter cannot be ascribed to generic considerations concerning cultural lags. The pattern of ICT adoption should rather be analysed in the frame of the wider Knowledge Management System which also include organizational mechanism and management practice. If compared with larger enterprises, in particular, SMEs tend to place more emphasis on the management of knowledge in tacit forms, and communication channels are inter-firm rather than intra-firm. Corso et al. (2003-n. 104) goes further, linking the above ICT patterns with KM internal processes. Three different KM configurations emerge: "Traditional",

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51

"Codification" and "Network-based"4. The 'Traditional approach' was followed by firms leveraging on traditional mechanisms to transfer and consolidate knowledge both internally and externally, relegating ICT tools to a marginal role; hence, emphasis is on teams, paper documents, interpersonal relationships, gatekeepers and interaction with customers and suppliers. The 'Codification approach' is typical ofthose firms giving great importance to ICTs (particularly CAE, CAM, 2D CAD, DB) containing design solutions and lntraNets, for consolidating and transferring knowledge, and making it codified and peopleindependent. The 'Network-based approach', lastly, is internally characterized by the same behavior showed by firms belonging to the 'Traditional approach': knowledge transfer and consolidation mainly rely on traditional tools (teams, paper documents and interpersonal relationships). At the inter-firms level the use of organizational Levers, with particular reference to gatekeepers and interactions with customers and suppliers, is supported by 'border' ICTs, which is those tools allowing the exchange of data across interfaces toward the external environment (i.e., mainly 3D CAD and InterNet connections) . What is still lacking is the development of empirically tested supportive models to help managers in designing and implementing organisational and managerial tools to foster Knowledge Management. Agenda for future research should therefore analyse in more detail processes through which knowledge, in its different forms, is assimilated, created, transferred, stored and retrieved across wide and dynamic networks, as well as the organisational and managerial tools through which firms can influence such processes. Finally, much more emphasis should be devoted to the influence and potential benefits of emerging Information and Communication Technologies based on internetworking. In the present chapter we are exploring three research questions: RQ1. Find out how widespread the three KM Configurations are and if these configurations coverthe whole field. Three hypotheses arepossible: 1) allthe configurations existin sufficiently large numbers, and together they cover a great percentage of all possible KM configurations; 2) only one or two configurations are really widespread, and there is no other widespread configuration; 3) only one or two configurations are widespread and there are also one or two other large configurations; RQ2. For those configurations, we investigate the drivers which explainsuch choices; and RQ3. Their impact on performance. 4. THE INVESTIGATION FRAMEWORK

Based on literature and previous case studies, we developed the research investigation framework shown in Figure 2, which analyzes three groups of variables and their relationships: Contingencies, KM Configurations and Performances. 4These configurations are used as cluster seed point in the survey data analysis (see Table 5).

52

Corso et al.

b)

CONTINGENCIES

RQ3

PERFORMANCES

KNOWLEDGE MANAGEMENT CONFIGURATIONS

(RQ!)

c)

Figure 2. The Investigation Framework.

Contingencies are exogenous to the model and point out how some firm-specific variables can influence the choice of the ICT and organizational tools-the Leverswhich support the KM process in Continuous Product Innovation. KM Configurations identify the set of Levers SMEs adopt in order to transfer and consolidate knowledge. Knowledge transfer focuses on the flow of knowledge both within and outside the organizational boundaries ofthe firm, while knowledge consolidation represents the efforts organizations perform to capture and consolidate knowledge for future retrievals. Finally, the last block-Performances-sheds light on the effects that the different Configurations have in terms of performance. In the model, the choice of the Levers, made according to Contingencies (arrow a), produces effects in terms of Performance (arrow b). The relationship between Levers and Performances is not one way: if in the short run Levers can have a relevant impact on Performance, in the long run, they tend to affect the choice and use ofICT tools, as well as the selection of the most appropriate organizational tools (arrow c). Specific variables in each group were identified in the previous phase ofthe research, using comparative case studies based on semi-structured interviews [103; 104]. Although a large number of Contingencies were identified in this first part of the research, we focused on those which evidence from case studies showed to have the greatest influence in increasing PI complexity inside SMEs, namely the level of geographical dispersion, the product complexity, the degree of customization and the position in the supply chain. The level of dispersion specifies the existence of only one manufacturing site in front of two or more manufacturing sites. Particularly in SMEs, innovation focuses on the engineering phase rather than on R&D; for this reason the level of geographical dispersion influences the need to transfer knowledge between the different sites. Two indicators define the Contingency connected with product complexity: the internal architectural complexity and the technological complexity [103; 104]. The former conceptualizes the need to integrate the different items into the product's final architecture: the larger the number of components and subsystems,

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53

the more difficult the architectural choice and, consequently, the more relevant is the role of the architectural knowledge [30]. It is measured in terms of the number of both components and sub-systems (from now on called items) in the bill of materials. Technological complexity translates the variety of distinct knowledge and skill basis which need to be integrated into the final product: the greater the technological complexity, the greater the span of control; that is, the more the variety of skills and required capabilities within the firm. Hence, the multi-technological nature of the products has significant implications for their management in terms ofcompetencies to be developed and knowledge bases to be mastered and integrated. The technological complexity is measured by the Herfindahl-Hirschman Index, which considers the sum of the squared cost percentages attributed to the different embedded technologies (mainly mechanical, electromechanical, electronic, hydraulic and software). Catalogue or custom production (degree of customization) are the variables which explain the different knowledge source: differently from what happens in catalogue production, in the case of customization the customer contributes to the definition of product characteristics, thus becoming an external source of knowledge. Finally, the position in the supply chain is defined by the production of final products or components/subcomponents: it conceptualizes the need to integrate the manufactured item into the final product architecture, hence (in KM terms) the need to own the architectural competence regarding the modality of the integration. KM Configurations are identified by organizational and ICT Levers, which represent 'vehicles' capturing and disseminating knowledge within and outside organizational boundaries (final customers and suppliers) and to future projects. Organizational Levers refer to [12] i) people and ii) reports and databases. People (i) [97) are represented in this chapter by the following Levers: 1) interpersonal relationships between the R&D department members, 2) internal meetings for the transfer of design solutions which emerged in past projects, 3) project teams involving members from other departments (3a) or customers/suppliers (3b), 4) gatekeepers connecting the investigated firm with the external environment and, finally, 5) interaction with customers and with suppliers. Reports and databases (ii) are represented in this chapter by 6) paper documents and 7) ad hoc databases (DB) for storing design solutions. The abovementioned variables can be classified according to two dimensions (Table 1): i) the level of codification Table 1 Organizational levers classification Level of codification Articulated/ explicit levers bJl

.5 c

"0

0-

'0

"" bJl l-o

"

Ci

";;l

>:

s

l-o

.5 ";;l

>:

l-o

lJ

>< >LI

- Meetings (2) - Paper documents (6) - Databases for design solutions (7)

Tacit levers - Interpersonal relations (1) - Project Teams (3a) - Project Teams (3b) - Gatekeepers (4) - Interaction with Customers/Suppliers (5)

54 Corso et al.

of the Levers-that is, the possibility to articulate and, hence, embody knowledge in concrete and tangible representations [41] such as documents and software [105], and ii) the degree of openness towards the external environment [103]. ICT Levers can be classified into two groups: the specific ICT tools adopted in the PI process and the tools supporting integration among organizational units and external actors. In reference to the first aspect, a large number ofICTs have been analyzed: Product Data Management (PDM), two-dimensional Computer Aided Design (2D CAD), Computer Aided Engineering (CAE) and Computer Aided Manufacturing (CAM). With regard to the second aspect-ICT supporting integration-the degree of openness towards the other departments and the external environment has been analyzed. We investigated the presence of i) internal networks (intra-Nets), which connect different departments inside organizational borders or within a group (including e-mail and file sharing to support communication within the technical office and with the other departments), ii) external networks (inter-Nets) connecting different actors along the supply chain and iii) three-dimensional CAD (3D CAD) which-allowing to share virtual objects that can be jointly modified with customers and suppliers-has been included in the tools supporting integration, as the previous step of the research [103] suggested how this tool has usually been adopted by SMEs in order to technologically support coordination especially with customers. The last block in Figure 2 deals with the Performances connected with the PI process: they typically have an operative nature and assess the effectiveness and the efficiency of the PI process. 5. THE RESEARCH METHODOLOGY

The first phase of the research project started in 1999. In this stage, the research framework illustrated in the previous section was refined and specific variables in each class were identified. Evidence was based on a comparison of case studies in a statistically non significant sample based on interest. The use of semi-structured interviews and the selection of an interest based sample, although introducing statistical limitations, allowed researchers to gain a broader understanding of the research issue [for more information see the previous publications of the authors referred as 103; 104]. The second stage of the research-whose results are described in this chapter-was fielded in 2000. One of the explicit objectives of this study was to investigate the emergent Configurations ofICT and organizational tools for KM in SMEs, discussing Contingencies driving the choice of such Configurations and their impact on Performances. In this second stage, the triangulation with the survey methodology aimed at validating the results obtained by means of case studies. The research sample

The study was carried out on 127 SMEs in Northern (Piedmont and Lombardy) and Central (Tuscany) Italy, operating in the mechanical, electronic, plastic and chemical industries.

55

Knowledge management systems in continuous product innovation

Table 2 Population and sample characteristics Sample Population (%) Industries Mechanical Electronic Plastic Chemical Total

Lombardy

Lombardy

Piedmont

Tuscany N.

46.00 23.00 17.00 14.00

76.00 17.17 1.00 5.83

30.30 27.40 23.10 19.20

24

100.00

100.00

100.00

Piedmont

Tuscany

Total

%

N.

%

13 7

43.63 20.00 23.63 12.74

36 12 1 2

70.58 23.53 1.96 3.93

5 8 5 3

22.73 36.36 22.73 18.18

65 31 19 12

51.18 24.41 14.96 9.45

55

100.00

51

100.00

21

100.00

127

100.00

11

N.

%

N.

%

Table 3 Sample characteristics (turnover/employee and the average employee number per industry)

Turnover/employee (Euro)

Average employee number

Mechanical Electronic Plastic Chemical

160,102 232,406 227,241 268,557

100 110 67 148

Total

196,254

102

Industries

The source of the firm nominatives was the Kompass yearbook. Two main criteria were used in deciding the random selection of the sample: - Small and medium size, in terms of employees (from 35 to 350) and turnover (from 2.5 to 60 million Euro), because of the need to define what we meant by SME; - Manufacturing firms belonging to the mechanical, electronic, plastic and chemical industries, because ofthe importance ofsuch sectors in the Italian economic systemboth in terms of number of firms and turnover amount on the Italian GDP In Lombardy, 535 companies were contacted; ofthese, 61 firms (11.4%) returned the questionnaire, but only 55 had been completed (10.28%). In Piedmont, 600 SMEs were contacted, with a 12.17% response rate (73 firms), but only 51 SMEs (8.5%) completed the questionnaire. In Tuscany, 139 SMEs were contacted: 21 of them returned the questionnaire (15.11%) completely filled in. The higher response rate for Tuscany does not depend on the way firms were contacted; the gap with the other two regions can be explained in terms ofa lower number of callsfor survey participation in research projects in this area. In Table 2, population and sample characteristics in terms of SME distribution per geographical area and industry are summarized. Table 3 describes the ratio between turnover and employee number and the average employee number for each industry.

56

Corso et al.

Survey development

After a first telephone contact and a preliminary discussion with managers regarding research project aims, selected SMEs were invited to fill in the questionnaire published on the Web. A message with the link to the research project Website was sent to all people who were contacted for the survey. The Website contains a description of the research aims, instructions for filling in the questionnaire, the researchers' telephone numbers for further explanations/assistance and the questionnaire in html version. The representative of each SME responsible for filling in the questionnaire could read, fill in and send the questionnaire on line. Data were automatically transferred to a database, and then checked for reliability by the researchers. In comparison with traditional survey tools, the use of the Internet allowed advantages both for the interviewers (rapidity in receiving filled-in questionnaires and in data entry) and the interviewees (rapidity of the filling-in and forwarding phase). However, for those firms who do not have Internet access or are unwilling to use it, the questionnaire was sent by fax. In order to reduce fill-in time, the questionnaire tackled only comparative scale answers (ordinal scales in which respondents have to choose the answer with the highest priority), multiple choice answers, interval data (for example: numerical scales asking firms to give a vote ranging from 1 to 4) and relative data. Non-comparative scalesor open questions were used only for quantitative information or when there was not any ambiguity in the answer or when it was impossible to fix a priori alternatives or intervals. Moreover, the html format of the questionnaire allowed a tight control on its filling in. The questionnaire, which contains 87 questions, is structured into five sections: 1) general information regarding SMEs in order to characterize each firm based on its size, dispersion and competitive context; 2) the manufacturing system: its complexity, the innovations recently introduced, the relationships with customers and suppliers; 3) the product: its complexity and the innovations introduced; 4) the PI organization; 5) the use ofICT tools within SMEs in PI. The incentive provided to participants consisted of a personalized report containing the comparison between the KM approach they followed with the one adopted by firms with similar characteristics. Working papers derived from the research were also provided. Data analysis tools

Different statistical techniques have been used in relation to each research question (Table 4) because of the different objects analyzed. The explanation of the statistical tool choice is reported in the following section. Table 4 Data analysis tools Research question

Statistical tools

RQ1: KM Configuration identification RQ2: KM Configuration Drivers RQ3: KM Configuration impact on Performances

Cluster analysis Non-linear regression (Probit model) Factor analysis and Association analysis

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57

6. RESULTS

RQ1. Analysis of the d!ffusion level of the three KM configuration METHODOLOGY. In order to analyse the diffusion level of the three KM Configurations and respond to the first research question we resorted to cluster analysis. In particular, we used K-means clustering (i.e., non-hierarchical technique). The potential risk of poor explanations that could derive from cluster analysis in pure survey approaches was bypassed thanks to the insight gained in the first stage of our research project [106]. As a matter of fact, the three different approaches-the Traditional, the Codification and the Network-based-identified in the first research step [104] were used as cluster seed points [106] (Table 5). RESULTS. Cluster analysis divided SMEs into three groups (Table 6), which represent the ICT and organizational approaches to KM in Product Innovation. Only the Levers with a clustering role were included in the analysis process: the elimination of variables that are not distinctive (i.e., that do not differ significantly) across the derived clusters, allows the cluster analysis to maximally define clusters based only on those

Table 5 Cluster seed points

Cluster

Interaction 2D 3D CAE/ Intra- InterProject DB for design with customers teams solutions and suppliers Gatekeep. CAD CAD CAM Net Net

Traditional Codification Network-based

1 0 1

0 1 0

1 0 1

1 0 1

0 1 0

0 0 1

0 1 0

0 1 0

0 0 1

In this Table. which should be read horizontally, only Levers actually used in the clusteranalysis havebeen inserted.

Table 6 KM configuration clusters ClustefTRADITIONAL Organizational tools

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Project Teams DB for design solutions Interaction with eust/sup Gatekeepers

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ICT tools

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0 Source: ...kFONT size="+2">1 hello kFONT size=1>1 world ... A linear wrapper extracting a digital camera model name from an HTML snippet. Extraction Rule: sktptot-B»), extractUntil(X, .....

Figure 1. Typical expressions of wrappers of various technologies and their extracted result (framed text).

to another application, such as a spreadsheet. Even for the experienced web user this workload discourages such a task. The discussion above makes clear that there is a need for software tools that allow the as effortless as possible creation of comparison shopping charts by gathering product specification information from various known sites. This is not an information retrieval task but rather an information extraction one. A web search engine can probably help to locate an information resource but is unable to process that resource, extract featurevalue pairs and integrate that information into a singe comparison table. In the recent years, various researchers have proposed methods and developed tools towards the web information extraction task, with the buzzword of the field being the term wrapper. A wrapper (or extraction rule) is a mapping that populates a data repository with implicit objects that exist inside a given web page. Creating a wrapper, usually involves some training (wrapper induction-[31]) by which the wrapper learns to identify the desired information. Unlike Natural Language Processing (NLP) techniques that rely on specific domain knowledge and make use of semantic and syntactic constraints, wrapper induction mainly focuses on the features that surround the desired information (delimiters). These features are usually the HTML tags that tell a web browser how to render the page. In addition, the extraction of typed information like addresses, telephone numbers, prices, etc., is usually performed through extensive usage of regular expressions (Figure 1). Regular expressions are textual patterns that abstractly, but precisely, describe some content. For example, a regular expression describing a price in euros could be something like "€\d". Besides regular expressions, there are two major research directions in wrapper induction. The first and older one, treats the HTML page as a linear sequence of HTML tags and textual content ([2], [26], [35], [37]). Under this perspective, a wrapper generation is a kind of substring detection problem. Such a wrapper, usually includes delimiters in the form of substrings that prefix and suffix the desired information. These delimiters can be either spotted to the wrapper generation program by the user

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F. Kokkoras, N. Bassiliades, and I. Vlahavas

(supervised learning) or located automatically (unsupervised learning). The former method usually requires less training examples but should be guided by a user with a good understanding of HTML. The latter approach usually requires more training examples but can be fully automated. As the Internet technologies emerge, a new breed of wrapper induction techniques appeared ([8], [12], [30]), that treat the HTML document as a tree structure, according to the Document Object Model (DaM) [18]. Basically, such a tree wrapper uses path expressions to refer to page elements that contain the desired information (Figure 1). Tree wrappers seem to be more powerful that string wrappers. Actually, if input documents are well structured and tags at the lowest level does not contain several types of data, then a string wrapper can always be expressed as a tree wrapper [36]. Thanks to the advanced tools that are available for web page design, HTML pages are nowadays highly well-formed, but at the same time the content is more decorated by using more HTML tags and attributes. As a result, although approximate location of desired information is relatively easy thanks to tree wrappers, extraction of the exact piece of information requires regular expressions or even NLP (Figure 1). Thus, hybrid approaches are becoming quite popular. In general, wrapper induction technology demonstrates that shallow pattern matching techniques, which are based on document structural information rather that linguistic knowledge, can be very effective. Until the semantic web [7] becomes a common place, information extraction techniques will continue to play an important role towards the informed customer concept. In the comparison chart building problem, extracting and integrating information from heterogeneous web sources requires more than one wrappers. Variety in the way information is encoded and presented requires the cooperation of individual information extraction agents that are specialized for certain pieces of information and web sources. Creating, coordinating and maintaining a large number of wrappers is not a simple task though. A crucial factor that can alleviate this burden is the way wrappers are encoded and trained. Having to modify an ill-described wrapped that ceased to work efficiently due to certain reasons, is much more difficult than modifying a wrapper described in a human friendly way. This need is becoming critical as more non-expert users are adapting information extraction technologies for personalization and information filtering. Visual tools that allow the easy creation of wrappers ([1], [4], [20], [27], [32]) and declarative languages ([4], [29], [32]) for wrapper encoding is the current established trend. In this chapter, we present a knowledge based approach on comparison chart building from heterogeneous, semi-structured sources (product specification web pages). We propose the usage of the Conceptual Graphs (CGs) knowledge representation and reasoning formalism to train and describe information extraction wrappers. CGs naturally supports the wrapper induction problem as a series of conceptual graph (CG) generalization and specialization operations between training examples expressed as CGs. From the other hand, wrapper evaluation corresponds to the CG projection operation. Additionally, using DaM and product related domain knowledge, as well as advanced visual tools, we turn the wrapper creation and testing problem in an effortless

Aggregator: a knowledge based comparison chart builder for eshopping

143

task. Finally, we present the Aggregator, a comparison chart builder program that is based on the proposed approach. Aggregator can be taught how to gather specification information from web pages offered by brand sites and then use this knowledge to create side-by-side feature comparison charts by mining web pages in a highly automated and accurate fashion. The rest of the chapter is organized as following: Section 2 presents related work in the field ofwrapper induction and information extraction, emphasizing in comparison shopping and visual approaches. Section 3 gives a short introduction to CGs and proposes a novel approach for wrapper training, modeling and evaluation that is based on CGs. Section 4, presents how our CG-based wrappers and domain knowledge can be used to create comparison charts from heterogeneous web sources. Section 5 outlines the Aggregator, a tool that allows to visually train and apply CG-based wrappers, and finally, Section 6 concludes the chapter and gives insight for future work. 2. RELATED WORK

In the last few years, many approaches and related tools have been proposed to address the web information extraction problem. In the following, we give some detail about approaches that are closer to ours, in the sense that, they either exploit a tree representation of a web page ([4], [29], [32]) or use target structures that describe objects of interest and try to locate portions of web pages that implicitly conform to that structures ([1], [20], [27]). A good survey on information extraction from the web can be found in [28]. XWRAP [29] is an interactive system for semi-automatic generation of wrapper programs. Its core procedure is a three step task in which the user, first identifies interesting regions, then identifies token name and token value pairs, and finally identifies the useful hierarchical structures of the retrieved document. Each step results in a set of extraction rules specified in a declarative language. At the end, these rules are converted into a Java program which is a wrapper for a specific source. XWRAP features a component library that provides source independent, basic building blocks for wrappers and provide heuristics to locate data objects of interest. In W 4F ([32], [33]), a toolkit for building wrappers, the user first uses one or more retrieval rules to describe how a web document is accessed. Then, he/she uses a DOM representation and a web page annotated with additional information, to describe what pieces of data to extract. Finally, he/she declares what target structure to use for storing the extracted data. W 4F offers a wizard to assist the user in writing extraction rules which are described in HEL (HTML Extraction Language) and denote an assignment between a variable name and a path-expression. The wizard cannot deal with collection ofitems, so if the user is interest in various items of the same type with the one clicked on, conditions must be attached to the path expression to write robust extraction rules. Lixto ([3], [4]) is a system that assists the user to semi-automatically create wrapper programs by providing a visual and interactive user interface. It allows the extraction of target patterns based on surrounding landmarks, on the content itself, on HTML attributes, on the order of appearance and on semantic and syntactic concepts. In

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addition, it allows disjunctive wrapper definition, crawling to other pages during extraction and recursive wrapping. Wrappers created with Lixto are encoded in Elog, a declarative extraction language which uses a datalog-like logical syntax and semantics. Lixto TS [5] is an extension to the basic system aiming at web aggregation applications through visual programming. NoDoSE [1] provides a graphical user interface in which the user hierarchically decomposes the web document, outlining its interesting regions and describing their semantics. This decomposition occurs in levels; for each one of them the user builds an object with a complex structure and then decomposes it in other objects with a more simple structure. The system uses this object hierarchy to identity other similar objects in the document. This is accomplished by a mining component that attempts to infer the grammar of the document from objects constructed by the user. DEByE [27] is an interactive tool that allows the user to assemble nested tables (with possible variations in structure) using pieces of data taken from the sample page. The tables assembled are examples ofthe objects to be identified on the similar target pages. DEByE generates object extraction patterns that indicate the structure and the textual surroundings of the objects to be extracted. These patters are then fed to a bottom-up extraction algorithm that takes a target page as input, identifies on it atomic values in this page and assembles complex objects using the structure of the pattern as a guide. In [20], an ontology based approach to information extraction is presented. The ontology (conceptual model), which is described in the Object-oriented Systems Model, is constructed prior to extraction and describe the data of interest, relationships, lexical appearance and context keywords. The extraction tool uses this ontology to determine what to extract from record-sized chunks that are derived from a web page and are cleared from HTML tags. This use of ontological knowledge enables a wrapper to "sustain" in small variations existing in similar web pages (improved resiliency) and to be able to work better with documents presenting similar information but differently organized (improved adaptivity). Our proposed framework for wrapper creation offers very similar functionality with all of the above approaches, in the sense that it provides a visual environment for wrapper creation. There exists a major difference though in the core technology used, which, for our tool is the Conceptual Graph formalism. Our choice allow us to exploit both DOM representations of web documents (approach used in [4], [29] and [32]), as well as user defined structures that describe objects of interest (approach used in [1] and [27]). We achieve this by using CG-based generic wrapper descriptions which are detailed by the user in an interactive way, using visual tools that combine not only the DOM representation, but the browser itself. The CG formalism, naturally supports all the major steps in information extraction with wrappers, with its generalization, specialization and projection operations. In addition, CGs is a proven technology to encode ontological knowledge to provide a common schema for information integration and to improve wrapper's resiliency and adaptivity in the way [20] does. Beyond that, the representation we use provides the operations required to create a functional

Aggregator: a knowledge based comparison chart builder for eshopping

[Wrapper]

f-

(targetURL)

f-

145

[URL]

Figure 2. A Conceptual Graph stating that there exists a wrapper aiming at some URL.

reasoning system. This allows the creation of dynamic ontologies, where static and axiomatic/rule knowledge co-exist [15]. For example, we can use such knowledge to create structural dependencies between two wrappers. Finally, the CG formalism has, by nature, better visualization potential. This enables our system to provide a more comprehensible wrapper representation to the end-user. Regarding comparison shopping, one of the earliest attempts is ShopBot [19]. It focuses on vendor sites with form based search pages, returning lists of products with a tabular format. With today standards, ShopBot is quite restricted since it uses linear wrappers and focuses on highly structured pages. A commercial version of ShopBot, known asJango, was bought by Excite. Apart from Lixto TS [5], there are many other commercial wrapping services available on the Internet, such as Junglee (bought by Amazon), Jango, mySimon, RoboShopper and PriceGrabber. Jango and mySimon use real time information gathering from merchant sites, while Junglee pre-fetches information in a local database and updates it when necessary. All sites provide comparative shopping based on integrated information delivered from other vendor sites. Besides their unknown technology which is considered a business asset, most of these sites put emphasis on the price and provide very limited product specification information. Only PriceGrabber offers side-by-side and specification information rich, comparison charts. 3. WRAPPERS AS CONCEPTUAL GRAPHS

In this section we first give a small introduction to CGs, focusing mainly on the generalization, the specialization and the projection operations which are the key ideas behind our proposed CG-Wrap model. Then we present how CGs can be used to model information extraction wrappers. 3.1. Conceptual graphs background

The elements of CG theory ([14], [34]) are concept-types, concepts, relation-types and relations. Concept-types represent classes of entity, attribute, state and event. Concepttypes can be merged in a lattice whose partial ordering relation < can be interpreted as a categorical generalization relation. A concept is an instantiation ofa concept-type and is usually denoted by a concept-type label inside a box or between "[" and "]" (Figure 2). To refer to specific individuals, a referent field is added to the concept ([table:*]-a table, [table:{*}@3]-three tables, etc.). Relations are instantiations of relation-types and show the relation between concepts. They are usually denoted as a relation label inside a circle or between parenthesis (Figure 2). A relation type determines the number of arcs allowed on the relation as well as the type of the concepts (or their subtypes) linked on these arcs.

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F. Kokkoras, N. Bassiliades, and I. Vlahavas

CG1: [HTMLElement: #3] CG2: [HTMLElement: #9]

f-

(attribute)

ff-

CG3: [HTMLElement]

f-

f-

[BGColor: "#FFFFFF]

(attribute) f- [BGColor: "#CCFF12] (parent) f- [HTMLElement: #8]

(attribute)

f-

[BGColor]

Figure 3. CG3 is the minimum common generalization ofCG! and CG2.

A Conceptual Graph is a finite, connected, bipartite graph consisting of concept and relation nodes (Figure 2). Each relation is linked only to its requisite number of concepts and each concept to zero or more relations. CGs represent information about typical objects or classes of objects in the world and can be used to define new concepts in terms of old ones. The type hierarchy established for both concepts and relations is based on the intuition that some types subsume other types, for example, every instance of the concept "Table would also have all the properties of HTMLElement. In addition, with a number of defined operations on CGs (canonical formation rules) one can derive allowable CGs from other CGs. These rules enforce constraints on meaningfulness; they do not allow nonsensical graphs to be created from meaningful ones. Among other operations defined over CGs, the most useful and related to the information extraction problem, are the generalization, the specialization and the projection operations. The generalization is an operation that monotonically increases the set of models for which some CG is true. For example, CG 3 in Figure 3 is the minimum common generalization ofCG j and CG z. Only common parts (concepts and relations) of OG, and CG z are kept in CG 3 . In addition, individual concepts like [BGColor:"#FFFFFFj have become generic by removing the referent field. Specialization is the opposite to the generalization operation. It monotonically decreases the set of models for which some CG is true. This is achieved by either adding more parts (concepts and/or relations) to a CG, or by assigning an individual referent to some generic concept. Projection is a complex operation that projects a CG v over another CG u which is a specialization of v (u ::: v), that is, there is a sub graph u' embedded in u that represents the original v. The result is one or more CGs IT v which are similar to v but some of its concepts is possible to have been specialized by either specializing the concept type or assigning a value to some generic referent, or both. Under the machine learning perspective, training information extraction wrappers is a combination of automatic generalization and manual specialization operations that result in a model (pattern) that describes best the training instances and that can be used to detect new, unknown instances. This is similar to the generalization and specialization operations of the CG theory. A CG wrapper is the result of generalization and specialization operations over two or more training instances expressed as CGs. Moreover, applying a CG wrapper is equivalent to a projection operation of the wrapper over web page elements expressed as CGs. Based on these analogies, we present next how CGs can be used to model and train information extraction wrappers.

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[Wrapper]

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Figure 5. An HTML element in CG form (simplified and reduced version).

3.2. Modeling and training wrappers with CGs

The ability ofCGs to represent entities ofarbitrary complexity in a comprehensible way, make them a promising candidate for modeling information extraction wrappers. This perception is strengthened by the highly structured document representation which is defined by the DOM specification. This tree structure allows the easy mapping of web document elements to CG components. In general, a wrapper accesses a page located at a specific URL, searches inside this page for some specific HTML element which is the container of the desired information and extracts that information from it. This abstract description is encoded as the CG depicted in Figure 4. In practice, such a generic wrapper is useless, in the sense that it describes every single element of an HTML page. More specialization is required, particularly in the HTMLEIement concept. Towards this, we exploit the highly structured and information rich HTML element description provided by modern browsers. Such information includes, among others, the text contained inside the element, its attributes, the parent element under the DOM perspective, its tag name, etc. Besides this information, which is directly accessed, we also exploit calculated information that is derived if someone considers the neighborhood of some element. Such information includes, for example, the sibling order of this element being a child of its parent element and the total number of siblings. With this information in hand, a complex HTML element description can be created in CG form. Such a CG is presented in Figure 5. Note that, for clarity, Figure 5 presents a simplification (CG operation) of six CGs over the common [HTMLElement] concept presented on the left. Moreover, for space economy, a reduced version is presented, since the actual description is quite more complex. We demonstrate how our generic wrapper can be specialized using the classical problem of extracting information from an electronic flea market. Figure 6 presents a snippet from a web page of such a site. Information is organized in an HTML table,

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Using the generalization operation of the CG theory for the two CG wrapper instances, a generic wrapper describing (extracting) both product names can be created (Figure 9). This wrapper is generic enough to extract all product names of the table in Figure 6, but it also extracts the first header cell. Further specialization of our CG wrapper is required to exclude the header cell. This can be established over the HTML element that is the parent ofthe element containing the extracted information. This element refers to a row of the product table. Excluding this row is as simple as requesting that this element's sibling order is greater than one. The final wrapper is presented in Figure 10. Note that the concept of the CG wrapper that contains the desired information ([Info]) is fed by the [Text: ?X] concept, since this part of the web page contains the desired information. In addition, parts of the final wrapper description that do not affect the accuracy of the wrapper, such as the [BGCOLOR] can be dropped out. Finally, regular expressions can be used over the initially extracted information in order to fine-tune the output. For example, extracting the price in euros from the flea market example, requires the replacement of?X with some proper regular expression that is applied over X. Thus, training a CG-Wrapper, is a set of automatic generalization and manual specialization tasks that results in a model (CG) that accurately describes the desired information inside a web page.

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[HTMlElement: #25] [HTMlElement: #26]

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Figure 11. Two nodes of an HTML tree, in CG form (partially presented).

[Wrapper: fleaName]

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Figure 12. The wrapper of Figure 10 after applying it over the CGs of Figure 11.

We propose two execution models for our CG-Wrappers, a naive and an optimized one. According to the naive execution model, we iterate over all the nodes of the HTML tree trying to satisfy the constraints imposed by the wrapper components. In the optimized execution model we first do some short of filtering, to exclude nodes that are definitely irrelevant. For example, the wrapper of Figure 10 can be evaluated only over the nodes that have a TD tag. Selecting only those nodes is possible by exploiting the browser's application programming interface (API). The semantics of both execution models are derived from the CG theory: The evaluation of a CG-Wrapper is the result JrV of a projection operation that projects the container part u of the wrapper over an HTML node v expressed as CG. For example, consider the two CGs of Figure 11 which refer to the table of Figure 6, representing the second product row and the first cell ofthis row, respectively.Projecting the container part of the CG wrapper of Figure 10 over the second CG of Figure 11 results in an instantiated CG wrapper where the unbound X referent of the [Text: ?X] concept have been unified with "DIAMOND SUPRA v92 inte ... ". Note that, the exact projection involves also a replacement of the concept [HTMLElement: #25] of the second CG, with the CG definition of this concept (that is, the first CG in Figure 11). This inner task corresponds to the expansion operation of the CG theory, where a concept is replaced by its CG definition. The final instantiated wrapper is displayed in Figure 12.

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operation platform (physical)

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Figure 8. Conceptual model of organizations knowledge management system.

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196 Janis Grundspenkis and Marite Kirikova

sensors, knowledge storage in some kind of memory, inferencing, knowledge retrieval and representation. The conceptual model consists of two main parts: an organization as a multiagent system for business process support and a knowledge management system. The conceptual model has three layers called an "engine-room", a structural layer and a "co-operation platform". The "engine-room" is an integrated set of technologies, hardware and software to provide knowledge acquisition, storage, processing, retrieval and representation. The purpose of the structural layer is to identify intellectual resources of the organization, and to organize knowledge to make it easily accessible and applicable. A "co-operation platform" is the physical and/or visual environment where organization's intelligent agents may communicate with each other for effective knowledge sharing and distribution to achieve the business process goals. A "co-operation platform" maintains such components as video conferencing, chat rooms, electronic white boards and other tools for co-operative work (groupware). It is needed to point out that at the present moment the proposed conceptual model of OKMS has not been implemented. The next step towards the implementation of the conceptual model is estimation of the potential already manifested by intelligent agents and multiagent systems for KM. For this purpose let us mark out three groups of agents: 1) Agents that may promote the knowledge management and may be used as organization's common vehicle of the "engine-room". 2) Agents that provide communications. 3) Personal agents of knowledge workers. Starting this overview, it is worth to point out some relevant features of KMS that show the similarities between the proposed conceptual model and the known concepts on which KMS's notions are based. According to [8] a framework of the KMS consists of: • The use ofproblem finding and its related techniques to determine present and future problems and to identify future opportunities. • A knowledge infrastructure that is related to very large databases, data warehouses, and data mining (authors remark: we wander why knowledge bases are missed in this list?) . • Network computing (company's intranets and extranets, and Internet) to allow dissemination of relevant knowledge. • An appropriate software that is focused on data, information and knowledge collection, search for needed knowledge, and sharing of knowledge. Thus, the KMS centers on the organization, representation (codification) and dissemination ofknowledge in an organization. The KMS represents a collaborative work environment in which organizational knowledge is captured, structured and made accessible to facilitate a more effective decision making and actions to reach the business

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process goals. KMS has been influenced by differnet kinds of prior information and knowledge-based systems [52]. First, there were management information systems (MIS) that provide periodical reports and give periodical answers what should have been done [8]. The next step was addition of a viewpoint of decision maker implemented in decision support systems (DSS). These systems were designed to support problem-finding and problem-solving decisions of the manager. The evolution of the DSS resulted in three new types of systems, namely, group decision support systems (GDSS), executive information systems (EIS) and idea processing systems (IPS) [8]. GDSS combines computers, data communication, and decision technologies to support problem-finding and problem-solving for managers and their staff. The emergence of technologies such as groupware, electronic boardrooms equipped with electronic whiteboards or large screen projectors, LAN, Web and video conferencing, decision support software, etc. have promoted interest in these systems. EIS bring together relevant data from various internal and external sources to obtain useful information that helps to make strategic and competitive decisions. These systems filter, compress and track relevant data as determined by each individual executive end user. IPS are the subset ofGDSS and are designed to capture, evaluate, and synthesize ideas into a large context that has real meaning for decision makers. The inputs of these systems are the problem statement and the observations about the problem. Processing involves idea generation and evaluation for problem solving. The outputs are report and dissemination of information about specific ideas how to solve the problem. The on-line analytical processing (OUP) systems are closely related to the previous kinds of systems. These systems center on the question "what happened" and provide a multidimensional view on aggregated and summarized data, that is, OLAP tools allow to look at different dimensions of the same data stored in data bases and data warehouses. As such, these systems and tools provide a starting point for knowledge discovery within the KMS's operating mode [8]. From the broader view knowledge discovery or data mining tools are needed to complement OLAP systems because these tools tell decision makers why something has happened in their business. Knowledge discovery tools are capable of uncovering patterns that can lead to discovering new knowledge. So, they are considered to be the next step beyond OLAP systems for querying data warehouses, and as a prerequisite for interpretation and dissemination of knowledge. Knowledge acquisition, processing and usage typically have been implemented in knowledge-based systems (KBS), in particular, in expert systems. These systems are designed to simulate expert's problem-solving abilities in a narrowly specified problem domain. In KM context expert systems can be thought ofas knowledge transfer agents [8]. The problem with expert systems is well known-they are able to respond only to queries about something that is stored in their knowledge bases, otherwise they cannot respond. This is where neural networks could help because neural networks learn the human decision-making process by examples internally developing the proper algorithms for problem-solving. Thus, neural networks do not require a complete knowledge base and extensive interpretation of its contents by an inference engine. Neural networks

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are effective in processing fuzzy, incomplete, distorted, and "noisy" data. They are suitable for decision support under conditions of uncertainty, and extremely useful in data mining and other database specialized tasks. Recent trends manifest the transition to a combined environment of KMS and advanced techniques, such as virtual reality, multiagent systems, and VVeb intelligence [29, 30]. The integration ofKMS with virtual reality allows decisions makers to think from a different perspective and, as a consequence, to enhance their skills. Using sophisticated interactive computer graphic, special clothing .and fiber optic sensors it is possible to treat system-generated objects almost as real things. These developments to a large extent are related with the appearance of the notion "cyberspace" as the environment for both humans and intelligent agents [53] to support interactive users. Building agents that can live and survive in the broad variety of environments, including hostile ones, promote new exciting results of AI and new promising applications as well. It will be shown later that intelligent software agents, in particular, offer an ideal technology platform for providing data sharing, personal services, and pooled knowledge

[54].

9.1. Intelligent agents for OKMS "Engine Room"

How is the intelligent agent paradigm exploited in KM already now and what are the perspectives of single agents and multiagent systems in this field? To answer the question, let us describe the agents from each of the mentioned above groups, starting with the first group-agents that may be used to build an OKMS's "engine room". At the beginning two aspects should be stressed. First, we have neither intention nor possibility to give an exhaustive description due to the sweeping changes in this field. Second, our division of groups has fuzzy boundaries because several agents may be included in more than one group. Nowadays agents are good at performing lists oftasks when specified triggers (events like "report completed", "fax received", and so on) prove to be true [30]. Agents serve for monitoring and collecting information from data streams and taking action on what they encounter. In this case multiple agents are responsible for network access, searching for information and filtering it. They are designed for information handling in information environments like WAN and LAN, for instance, the Internet, organization's intranet, etc. These agents are more commonly used because for most people navigation and using network systems is increasingly difficult and time consuming. Moreover, for intelligent agents others than humans information available on the Web is not understandable at all and hopes to change this are connected with the evolution of the Semantic Web. In [30] Knapik and Johnson list a plethora ofagents that can be useful in KMS. First, there are network agents like NetWare management agent (NMA) or NetWare LANalyzer agent, and many others. The NMA provides the NetWare management system with server statistics and notification of alarms so the network supervisor can monitor, maintain, and optimize server performance in a distributed computing environment from a single location. The NetWare LANalyzer agent is designed to complement the

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NetWare management system. This agent monitors the interaction between various devices on the network, warns of potential problems and lets optimize Ethernet and Token Ring segments. Network software distribution agents make the process of installing or updating software (operating systems, new data, applications) completely transparent to users across any size of network. The network administrator can graphically configure and launch an agent. After that, the agent can either install software or data on all nodes in the network, or delay it until the network is less crowded. Connection and access agents automatically configure and connect the user to the correct service depending on his/her needs and available resources. Second, database agents become even more valuable in database managment due to the fact that data warehouses become huge and complex. This class of agents can perform many useful tasks, including data integrity support in the database, providing constraints, for instance, preventing out-of-range data from being stored or illegal operations for trashing data, and distributing reports that can be automatically formatted in many different ways and distributed via E-mail, fax, on-line services, the Internet, and so on. In distributed database systems agents can perform backups and other routine tasks. Database agents in the future may automate all database access and updating, checking the validity of data, and perform natural language queries. They will also coordinate application execution among distributed databases, support data security requirements and maintain referential integrity [30]. Without doubt the richest source of data, information and knowledge that is accessible for any organization nowadays is the WWW (the Web, in brief). Unfortunately, we must conclude that the Web currently contains a lot of data, more and more structured data (structured documents, online databases) and simple metadata but very little knowledge, i.e., very few formal knowledge representations [55]. One of the main reasons is that the knowledge is encoded using various languages and practically unconnected ontologies. As a consequence, each knowledge source requires the development of a special wrapper for its knowledge to be interpreted and hence retrieved, combined and used. Many researchers are trying to overcome these problems. Their efforts resulted in the appearance of a new paradigm, so called VVeb intelligence for developing the Web-supported social network intelligence. Many details on developed approaches and tools in this very hot research topic, for example, intelligent Web agents, information foraging agents living in the Web space, social agents in Web applications, Web mining and farming for Web intelligence, intelligent Web information retrivel, Web knowledge management, and Web intelligent systems are given in [29]. The final goal of all these research efforts is a Semantic VVeb. Though the Semantic Web vision begins with information discovery [56] its potential goes well beyond information discovery and quering. In fact, it encompasses the automation of Web-based services. The influence ofSemantic Web on knowledge management is obvious. New exciting perspectives will appear when researchers come closer and closer to the goal of the Semantic Web-the Web that is unambiguously computer interpretable, and thus very accessible to intelligent agents. The Semantic Web would allow intelligent agents to do the work of searching for and utilizing services required by organizations as well as humans [27].

200 Janis Grundspenkis and Marite Kirikova

9.2. Agents that provide communications

Now let us continue with the second group of intelligent agents-agents that provide communications. Communications between individual of the multiagent community is the most relevant issue for effective knowledge creation, sharing and distribution in the KMS. Several communication management agents are already known and many others will appear in the near future. Messaging agents, for instance, Wildfire can connect people with each other no matter where they are and what communication medium they use [30]. Agents that are responsible for real-time monitoring and management of telecommunication networks, that is, for call forwarding and signal switching and transmission also belong to this class of agents. Assistant agents can perform automated meeting scheduling, inviting the right people, fixing meeting details like location, time, and agenda, arranging teleconferencing and videoconferencing if necessary. The next step in agent technologies that provide communications is the use of cooperative agents that are able to communicate with other agents and collaborative agents that are able to cooperate with other agents. Ending the short overview of agents that provide communications let us point out the fact that in [57] the discussed classes of agents are included into a groupware that is hardware and software technology to assist interacting groups. Computer Supported Cooperative Work, in its turn, is the studies how groups work, and how this technology helps to enhance group interaction and collaboration for promotion of knowledge flow and transformation in the OKMS. There are many groupware systems, for instance, GDSS, Workflow management systems, meeting coordinators, desktop conferencing (audio and video) systems, distance learning systems, systems for group (concurrent) editing and reviewing documents, etc. In addition, computer aided software engineering (CASE) and computer aided design (CAD) tools are well known representatives of groupware systems. Besides groupware modules relevant for operating of the entire groupware system, the modules that perform specialized functions and involve specialized domain knowledge are frequently needed. These modules are called team agents [57]. Examples of team agents are user interface agents, "social mediators" within an electronic meeting, and appointment schedulers that allow to schedule a meeting along a group ofpeople by selecting a free time slot for all participants. 9.3. Personal agents

Finally, let us discuss the role of personal agents in KM. Perosonal agents belong to humans, support human computer interaction and help knowledge workers to acquire, process and use the knowledge. Several types ofthese agents can be considered, namely, search, assistant, filtering and work-fiow agents [30]. Search agents are the most commonly used ones and work in different ways. Some agents search titles of documents or documents themselves, while others search other indexes or directories on the Web. Filtering agents may monitor the data stream searching the text for knowledge and phrases as well as the list ofsynonyms, and try to forward only the information that the users really need. These relatively simple agents can ideally search any document found and download it if search criteria are met. More

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Figure 9. An agent-based environment of the knowled ge worker.

sophisticated filtering agents can be trained by proving the sets of examples illustrating articles that users choose to read. Afterwards these agents begin to make suggestio ns to the user and receive feedback, which leads to a more representative profile of the user's nee ds. Assistant agents are designed to wait for events such as E- mail messages to occur, th en to sort them by sender, pr ior ity, subj ect, etc. T hese agents can also autom atically track clients and remind users of follow-up action s and commitments. In KM work-flow agents are useful for daily task coordination , appoi ntme nt and meeting scheduling, and routing communication from E-mail, teleph on es and fax machines. The facilities to support these types of agents are going to appear because the embedded real-time operating system vendors start to incorporate the standard infrastru cture and language support [30]. The progress in personal agent techn ologies is connected with the use of smart agmts [54] that exhibit a combination of all capabilities that are characteristic for coo perative, adaptive, person al and collabora tive agents. Smart agents will be able to collect informatio n about databases and business applicatio ns, as well as to acquire, store, generate and distribute knowledge. N owadays we enter the int elligent agents age using relatively simple agents but even th is situation offers pretty goo d opportuni ties to build an agent-based enviroment for knowledge worker suppor t in the KM S, as it is shown in Figure 9.

202 Janis Grundspenki s and Marite Kiri kova

For future vision let us speculate on the future impact of intelligent agents on the KMS. Today we are makin g only the first steps towards th e development of a cybercivilization w here agents will help efficiently by providing uniform access to the Web resources (the Semantic Web, whi ch is understandable for inte lligent agents instead of the Web w hich is understandable only for humans), makin g it possible to get information in time , to acquire, store, process and share knowl edge. The future evolution of suitable agents for KM is connected with information agent s and their ex tension- know/edge agents that will be able to learn from their environments and from each other as well to coo perate with each other [30]. They will have access to many types of information and kno wledge sources and will be able to manipulate information and knowledge in order to answer querie s posed by humans and othe r kn owledge agents. Teams of agents will be able to search Web sites, heterogeneo us databases and kno wledge bases, and work togeth er to answer queries that are outside the scope of any individual intelligent agent. These agents will execute searching in parallel, showing a considerable degree of natural language understanding, using sophisticated pattern extraction, graphical pattern matching, and context-sensitive searches. Coordination of agents will be handled either by supervi sing agents, or via communication between searchin g agents. So, more and more activities perfo rmed by hum ans will be automated that allow not only to speak about an agent- enhanced hum an but even to replace at least part of humans that now provide information and knowl edge base services by int elligent agents and their communities. This, in turn, will cruc ially impa ct the evolution of the KMS makin g them more and more intelligent. 10. CONCLUSIONS

T he analysis ofthe two different tracks in KM , namel y, people knowl edge management and informatio n technology knowledge management, reveals the existing gap between them . In this paper the intelligent agent paradigm is used as "a bridge" between these two rather isolated fields. Amalgamation of advanced AI and KM techniques may give a synergy effect for the developm ent of OKMS based on single intelligent agent s and their communities . The paper has several objectives. First, in order to realize the importance of concepts used in KM, we discuss the paradigm shift in organizational thinking from information to knowledge processing. Second, we consider many sometimes even conflicting definition s of knowledge man agement and classify them into three classes using such criteria as formal, process and organizationa l aspects. Third, we have introduced th e reader int o the intelligent agent paradigm and describe the essence of simple agents as well as mu ltiagent systems . Fourth, we discussed the notion "knowledge" in details and describe knowle dge possessors, knowledge types and kn owled ge sources. Fifth, in accordance with their active agent and passive object compo nents we have divided organization s into fourteen different systems. We show that an organization as a whole may be analyzed as an intelligent agent, introduce the notion of organization's " knowledge space" and outline the role of KM for organization's business pro cess improvement . We propose a novel conceptual mo del of th e OKMS based on the intelligent agent paradigm. Regardless of efforts needed to

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achieve a considerable amalgamation of AI and KM techinques, we see a potential of the proposed model moving towards the development of a general approach that will make the intellectual capital of an organization work as an effective knowledge engine in the framework of the KMS. Practical implementation of the proposed model is the major topic of the future work. It could serve as a research platform for integration of interdisciplinary approaches to knowledge management. Being enthusiastic about the perspectives of intelligent agents and multiagent systems in general, we could not neglect the dark sides of agents that can impede the evolution of the KMS. Rogue or maliciously programmed agents can make the worst viruses or try to destroy the whole KMS and, as a consequence, the organization itself. On the other hand, taking care about the agent security and privacy issues in the world of distributed agents, we can achieve vital progress in the KMS development making them more and more intelligent. To conclude, subsequent efforts are needed with a focus on the implementation and application of various intelligent agents and multi agent systems, to develop advanced KMS. ACKNOWLEDGMENTS

This work would not have been possible without the contribution of Dace Apshvalka, MSc. REFERENCES

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(eds.). Knowledge Management for the Information Professional. ASIS Monograph Series, Medford, New Jersey, 2000, pp. 7-17. [5] Koenig, M. E. n, and Srikantaiah, T. K. The evolution of knowledge management. In Srikantaiah, T. K., Koenig, M. E. D. (eds.). Knowledge Management for the Information Professional. ASIS Monograph Series, Medford, New Jersey, 2000, pp. 23-36. [6] May, n and Taylor, P. Knowledge Management with Patterns. Communications of the ACM, 46(7), 2003, pp. 94-99. [7] Information Technology for Knowledge Management. Borghoff, U. M., Pareschi, R. (eds.). SpringerVerlag, Berlin, Heidelberg, New York, 1998. [8] Thierauf, R. J. Knowledge Management Systems for Business. Quorum Books, Westport, Connecticut, London, 1999. [9] Liebowitz, J. Building Organizational Intelligence: a Knowledge Management Primer, CRC Press, Boca Raton, Florida, 2000. [10] Beckman, T. J. The current state of knowledge management. In Liebowitz, J. (ed.). Knowledge Management Handbook, CRC Press, Boca Raton, Florida, 1999, pp. 1.1-1.21. [11] Tiwana, A. The Knowledge Management Toolkit, Prentice-Hall, New-Jersey, 2000. [12] Sarvary, M. Knowledge management and competition in the consulting industry. California Management Review, 41,1999, pp. 95-107. [13] Galliers, R. D. and Newell, S. Back to the future: from knowledge management to data management. In Smithson, S., et. al. (eds.). Proceedings of the 9th European Conference on Information Systems, University ofMaribor, Slovenia, 2001, pp. 609-615. [14] Stewart, T. A. Your company's most valuable asset: intellectual capital. Fortune, 130, 1994, pp. 68-74.

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[15] Nahapiet, J. and Ghoshal, S. Social capital, intellectual capital, and the organizational advantage. Academy of Management Review, 23(2), 1998, pp. 242-266. [16] Wang, K. personal communication, 2000. [17] White, C. M. Telecommunications and networks in knowledge management. In Srikantaiah, T. K., Koenig, M. E. D. (eds.). Knowledge Management for the Information Professional, ASIS Monograph Series, Medford, New Jersey, 2000, pp. 237-253. [18] Knowledge Management and Virtual Organizations, Malhotra, Y. (ed.). Idea Group Publishing, Hershey USA, London UK, 2000. [19] Grey, D. Knowledge Management Tools, Smith Weaver Smith Cultural Changemakers, 1998. Available at http://www.smithweaversmith.com/kmtools&.htm. [20] Rusell, S. and Norvig, P Artificial Intelligence. A Modern Approach, Prentice Hall, New Jersey, 2nd ed., 2003. [21] Murch, R. and Johnson, T. Intelligent Software Agents. Prentice Hall PTR, New Jersey, 1999. [22] Genesereth, M. R. and Ketchpel, S. P Software agents. Communications of the ACM, 37(7), 1994, pp.48-53. [23] Multiagent Systems. A Modern Approach to Distributed Artificial Intelligence. Weiss, G. (ed.). The MIT Press, Massachusetts, 2000. [24] Shoham, Y. Agent-oriented programming. Artificial Intelligence, 60,1993, pp. 51-92. [25] Wagner, G. Agent-Oriented Analysis and Design of Organizational Information Systems. In Barzdins, J., Caplinskas, A. (eds.). Databases and Information Systems, KIuwer Academic Publishers, 2001, pp.111-124. [26] Hayes-Roth, B. An architecture for adaptive intelligent systems. Artificial Intelligence, 72, 1995, pp. 329-365. [27] Bryson, J. J. et al. Agent-Based Composite Services in DAML-S: the Behaviour-Oriented Design of an Intelligent Semantic Web. In Ning Zhong, Jiming Liu, Yiyu Yao (eds.). Web Intelligence, Springer, Berlin, 2003. [28] Huhns, M. N. and Singh, M. P Agents and multiagent systems: themes, approaches and challenges. In Huhns, M. N., Singh, M. P (eds.). Readings in Agents, Morgan Kaufman, San Francisco, CA, 1998, pp. 1-23. [29] Web Intelligence. Ning Zhong, Jiming Liu, Yiyu Yao (eds.). Springer, Berlin, 2003. [30] Knapik, M. and Johnson,]. Developing Intelligent Agents for Distributed Systems. McGraw-Hill, New York, 1998. [31] Genesereth, M. R. Interoperability: an agent based framework. AI Expert, March, 1995, pp. 34-40. [32] FlPA-Foundation for Intelligent Physical Agents. Available at http://www.FlPA.org. [33] Jennings, N. R., Sycara, K., and Wooldridge, M. A roadmap of agent research and development. Autonomous Agents and Multi-Agent Systems, 1(1): 7,1998, pp. 7-38. [34] Musgrave, A. Common Sense, Science and Scepticism: A Historical Introduction to the Theory of Knowledge. Cambridge University Press, Cambridge, 1993. [35] Moser, P. K. and Nat, A. Human Knowledge: Classical and Contemporary Approaches. Oxford University Press, New York, Oxford, 1995. [36] Davenport, Th. H. and Prusak, L. Working Knowledge: How Organisations Manage What They Know. Harvard Business School Press, Boston, 1998. [37] Aadmot, A. and Nygard, M. Different roles and mutual dependencies of data, information and knowledge-an AI perspective on their integration. Data & Knowledge Engineering, 16, 1995, pp. 191-212. [38] Sildjmae, I. J. Artificial Intelligence: Knowledge and Thinking, Tartu Technical University, Tartu, Estonia, 1989, (in Russian). [39] Kangassalo, H. Conceptual level interfaces for data bases and information systems. In Jaakola, H., Kangassalo, H., Ohsuga, S. (eds.). Advances in Information Modelling and Knowledge, lOS Press, Amsterdam, Washington, Tokyo, 1991, pp. 66-90. [40] Zack, M. H. Managing codified knowledge. Sloan Management Review, 40(4), 1999, pp. 45-58. [41] Kirikova, M. and Grundspenkis,]. Using knowledge distribution in requirements engineering. In Leondes, C. T. (ed.). Knowledge Based Systems, Techniques and Applications, Vol. 1, Academic Press, San Diego, 2000, pp. 149-184. [42] Anderson, J. R. Cognitive Psychology and Its Implementations. W H. Freeman and Company, New York, 1995. [43] Maier, R. Knowledge Management Systems: Information and Communication Technologies for Knowledge Management. Springer, Berlin, Heidelberg, 2002.

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[44] Drott, M K. Cognition defined' Available at http://drott.cis.drexel.edu/I625?1625def.html. [45] Mertins, K., Heisig, P., and Vorbeck, J. Knowledge Management: Best Practices in Europe, Springer Verlag, Berlin, Heidelberg, 2001. [46] Venzin, M., von Krogh, G., and Roos, J. Future Research into Knowledge Management. In Knowing in Firms: Understanding, Managing and Measuring Knowledge, Sage Publications, London, 1998, PI"26-66. [47] Kirikova, M. and Grundspenkis,J. Typesand Sources of Knowledge. In Scientific Proceedings of Riga Technical University, 5th Series: Computer Science, Applied Computer Systems-3'd Thematic Issue, Riga Technical University, Riga, 2002, PI'. 109-119. [48] Watson, H. J., Haudeshel , G., and Rainer, R. K., jr. Building Executive Information Systems and Other Decision Support Applications. John Wiley & Sons, Toronto, 1997. [49] Wikstrom, S. and Normann, R. Knowledge and Value: A New Perspective on Corporate Transformation. Routledge, London, 1994. [50] Woodridge, M. and Jennings, N. R. Intelligent agents: theory and practice. The Knowledge Engineering Review, 10(2), 1995, PI'. 115-152. [51J Jennings, N. R. On agent-based software engineering. Artificial Intelligence, 117, 2000, PI'. 277296. [52] Grundspenkis, J. Concept of Intelligent Enterprise Memory for Integration of Two Approaches to Knowledge Management. In Haav, H.-M., Kalja, A. (eds.). Databases and Information Systems II. Kluweer Academic Publishers, Dordrecht, 2002, PI'. 121-134. [53] Bradshaw,J. M., et al. Terraforming cyberspace. Computer, July, 2001, PI'. 48-56. [54] Case, S., Azarmi, M., Thint, M., and Ohtani, T. Enhancing e-communities with agent-based systems. Computer, July, 2001, PI'. 64-69. [55] Martin, P. Knowledge Representation, Sharing, and Retrieval on the Web. In Ning Zhong, Jiming Liu, Yiyu Yao (eds.). Web Intelligence, Springer, Berlin, 2003, PI'. 243-276. [56] Berners-Lee, T., Hendler, J., and Lassila, 0. The Semantic Web. Scientific American, 284(5), 2001, PI"34-43. [57] Ellis, C. and Wainer, J. Groupware and Computer Supported Cooperative Work. In Weiss, G. (ed.). Multiagent Systems. A Modern Approach to Distributed Artificial Intelligence, The MIT Press, Massachusetts, 2000, PI'. 425-458.

Definitions of knowledge differ depending on the field of investigations. These differences help to understand the inherent properties and nature of knowledge. The philosopher John Lock has defined knowledge as follows: "Knowledge then seems to me to be nothing but perception of the connexion of an agreement, or disagreement and repugnancy of any of our ideas" [35].

There are two important aspects in the definition given above, namely, first, the systemic nature of knowledge is revealed by emphasis on connection, and, second, the fact of the ownership of knowledge is mentioned by referring not to the general but to the particular ideas. In the area ofknowledge management one ofthe most popular definitions ofknow1edge is given by Davenport and Prusak [36]: "Knowledge is a fluid mix of framed experience, values, contextual information, expert insight and grounded intuition that provides an environment and framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds ofknowers. In organization it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices, and norms."

206 Janis Grundspenkis and Marite Kirikova

Davenport and Prusak's definition shows that fact that the agent needs to possess the knowledge to be capable to acquire knowledge, as, according to the definition, the knowledge provides environment and framework for incorporating new experiences and information. This view is in line with the understanding of knowledge that is expressed in literature of AI [37]. Janis Grundspenkis is a professor at Riga Technical University. He currently teaches in systems theory and artificial intelligence. His research interests are in the area of applications of intelligent agent technologies in knowledge representation and processing for knowledge management purposes. He leads the research project "Modeling of Intelligent Agent Cooperative Work for Knowledge Management and Process Reengineering in Organizations." His 38-year career has focused on the development of structural modeling methods and tools for heterogeneous system diagnosis. He has published more than 140 scientific publications in this and related fields. Marite Kirikova has Dr.sc.ing. in Information and Information Systems. She is an author of more than 30 scientific publications. Marite Kirikova is a scientific researcher and associated professor at Riga Technical University. She has done fieldwork at Stockholm University and Royal Institute of Technology, and Copenhagen University. Marite Kirikova currently lectures in systems analysis, knowledge management, and requirements engineering. She also participates in the research project "Modeling of Intelligent Agent Co-operative Work for Knowledge Management and Process Reengineering in Organizations." Contact address: Department of Systems Theory and Design, Faculty of Computer Science and Information Technology, Riga Technical University, 1 Kalku Street, Riga, LV-1658, Latvia. E-mail: [email protected]. [email protected]

METHODS OF BUILDING KNOWLEDGE-BASED SYSTEMS APPLIED IN SOFTWARE PROJECT MANAGEMENT

CEZARY ORLOWSKI

INTRODUCTION

Information Technology today penetrates all fields of human activity and is becoming a general element in the functioning of contemporary society. It is an instrument of multi-sided communication and information exchange and embraces all areas of life to an ever-increasing degree. The global dimension of information systems introduced into firms has created diverse communication media and is causing radical changes in world economic and organizational structures. Information techniques and tools are one of the most significant elements in these developments, deciding the global character ofmanagement, speed and transfer ofinformation as well as speed of decision making. The development of computerization and telecommunications and the fusion of the two technologies provides managers with ever more effective information systems (adapted to the needs of the user, precise, fast and able to meet deadlines), which are tools in shaping products of high quality and profitability. The implementation of knowledge-based information projects, which is becoming an important problem for individual companies and for the economy as a whole, involves engaging considerable financial resources and a large implementation risk. In the case of enterprises financed from public money the high costs are linked to considerable social expectations. These expectations as well as the high implementation risk mean that complex research is being undertaken, covering technical analysis of cases ofintroduction and the possibilities ofmaking use ofexisting methods, techniques and models to find new solutions in creating knowledge-based systems [14]. 207

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In this chapter, existing methods of building knowledge-based systems in Software Project Management (SPM) are discussed. New possibilities of modelling these systems are indicated and an example of building a model ofa social system is presented-a fuzzy model of information project management. The field of research was narrowed down to implementation of systems by international project consortiums, consisting of several or more project teams, understood in work as "distinguished from the structure of the organization, commissioned for a defined period and consisting of specialists from various fields, whose knowledge and experience have a bearing on the problem" [68]. The concept of knowledge-based systems (KBS) is used in literature in a variety of senses: Ullman [71], Bazewicz [2], Bubnicki [7], and Hickman [28]. In the present analysis it is taken to refer to an information system with rule-object representation of knowledge in the form of a hierarchical decision network and mixed (before and after) conclusion-drawing type. The first part presents the state of modelling knowledge-based systems for SPM. The existing methods and project tools are presented and methods of assessing project organization are indicated. The second part discusses new ways of creating SPM models. The possibilities of applying fuzzy sets and fuzzy regulators are examined. In the third part an example of constructing a model of a fuzzy system of SPM is presented, on the basis of the theory of fuzzy regulators and fuzzy systems. First, conceptions of the model are discussed and then details of the model's construction are described: hierarchical-presenting the hierarchy of levels in managing projects and teams; structural-emphasising the variables: input and output state, static, dynamic; and fuzzy-formalizing knowledge with the help of fuzzy sets. 1. PROBLEMS OF MODELLING SPM

In attempting to build a SPM model for a knowledge-based system, the aim of recognising the state-of the-art was set. This knowledge indicates the hierarchy of problems in managing and implementing projects (fig. 1). In management [20] these problems concern access to expert knowledge of SPM, use by managers of management methods to support project implementation and application of models for assessing project processes and teams. The consequences of these problems are exceeding the budget, failure to meet deadlines and limitation of the aim of the enterprise [5]. 1.1. Expert knowledge of project management

According to SPM experts [23], scope, time resources, communication, risk and project changes are inter-related management problems whose occurrence makes knowledge of SPM and the experience of implementation described in project experiment documentation important elements in assessingand directing future projects [73]. Access to such documentation is however made difficult because of the unwillingness of firms to

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publish their own failures, as also in many cases by the absence of records [20]. Recreating such knowledge is in turn difficult because of the limited possibilities (one-off events without obvious marks of the frequency of their occurrence) of describing risk and change management. This is also a consequence of the difficulty of documenting project management processes, of lack of adequate knowledge of the mechanisms occurring during their implementation and of the problems of formalizing knowledge of SPM [40]. It is also conditioned by the commercial nature of the enterprises [19]. Because of this there is also a lack of knowledge of implementations and of managing them [61]. For project directors, a source of knowledge may be experience of implementing previous projects [601. This may constitute a source of knowledge on management, but it depends on the specific character of the projects and on the director's ability to make use of this experience in implementing further enterprises in a new field and with a new project team.

Problems of project management

Limited application of the model in assessing projec t processes and project teams

Difficult ies of using management methods to support proj ect implementation

Lack of expert knowledge of project management 'the art of management '

Pr obl ems of project implementation Exceeding budget

Exceeding schedule

Limitation of project aim

Figure 1. Relationship between management problems and implementation of information projects.

1.2, Methods of supporting management processes

Apart from expert knowledge, a source of knowledge on enterprise management are the methods applied in them, gained for the most part by firms dealing with designing and introducing information systems on the basis of their own experience. They constitute guides to formal behaviour in SPM. For example: KADS, presented in the

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work ofHickman and Killin [28] and Pragmatic KADS in the work ofKingston [33] divide enterprises into implementation phases and indicate a selection ofsolutions from the field of management for these phases. Adaptation of known methods of designing information systems, as presented in the work of Coleman and Somerland [11] as well as Nerson [50], is also possible. Individual methods created by large project teams are also applied, an example being the method of Project Management Methodology (PMM), worked out by the firm IBM, presented by Lopacinski and Kalinowska-Iszkowska [44]. It places the main emphasis on the processes ofplanning and implementing the project. In addition, as documentation of project experience of SPM, it makes use of the packet WSDDM (Worldwide Solution Design andDelivery Methods) supporting knowledge acquisition and project management in the phases distinguished: • project identification (assessment of the feasibility of implementing the project, definition and specification of user needs, definition of critical factors, estimation of risk level), carried out independently by the client and by the provider of the system; • project initiation (definition of project management structure, assembly of team and assignment of tasks to particular people, definition of processes ensuring quality and management of exceptions as well as definition of criteria for acceptance of results, costs and duration); • project implementation (cyclical implementation of tasks, regular team meetings, reports on work in progress, internal and external controls, analysis of exceptional situations); • project completion (preparation of documents of implementation and experience). With the use of this method, definition of processes is also possible: • plan management-preparation of plans and reports, analysis of progress of work; • contract management including documentation; • exception management-covering implementation risk; • reaction to changes-decision-making when problems and errors occur; • quality assurance-surveys of correspondence between project implementation and methodology adopted; • management of personnel and organization-definition of project structure; • assignment of tasks to key people and identification of processes, plans of work and employment, team management, taking account of changes and development. Solutions ofthe PMM type can be applied by the IBM project team, but considerable risk is involved in adapting them for project teams on less mature levels. According to Boehm [3], algorithmic methods from the groups COCOMO and COCOMO II are used for economic assessment of enterprises. COCOMO II, which takes account of the maturity of project processes, contains three models: Application

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Composition, Early Design and Post Architecture; while COCOMO classifies information projects with regard to risk group: • organic projects, whose particular characteristic is small teams of a high technical level with projects of recognised object field and known information tools and methods; • semi-detached projects-quasi-autonomous, in which the team members represent various levels of technical knowledge, while the object area and the information tools and design methods applied are generally known; • embedded projects, in which a complex project of unrecognised object area is implemented, with methods and information resources unknown for the given area, but capable of application. Other methods support project management in respect to cost, composition and size of teams as well as labour intensity [64]:

• estimation by analogy-assessment ofprojects on the basis of earlier implemented and documented projects; • expert assessment carried out by a group of independent experts; • input estimation: based on elementary work units (Work Breakdown StructureWBS); • top down estimation-method of design within the limits of costs set (Design to Cost)-introductory decomposition on simpler tasks (Workpackages) and definition of the necessary outlay of work, further decomposition to tasks and exact processes, assuming that the cost of the enterprise is the sum of the costs of individual tasks; in cases where the costs are exceeded modification of the system is required. Top down estimation is: • estimation based on a parametric model (relationship between output of work and duration of project as well as factors directly bearing on it); • estimation in order to win (Price to Win)-assessment of the enterprise is conducted in such as way as to outdo potential competitors. To manage time and resources, the method offunction points analysisis applied. This method was worked out by Albrecht of the firm IBM in 1979 and later perfected by IFPUG (International Function User Group) [24]. Its main aim is to calculate "attributes of productivity of the information system" [1] by receiving: • input variables; • output variables; • internal data collections; • external data collections; • questions for the system. Each of these attributes is subordinated to three degrees of complexity: simple, moderate and complex. Each degree of complexity is assigned a weight. For example:

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for the category of input to a system with a moderate degree of complexity, the weight is 6. The total value of uncorrected function points is calculated by applying the equation:

L Li=1 111; Nj 5

NPF =

3

(1)

i=1

where: NPF - total value of uncorrected function points TV;; - value of the co-factor of weight N} - number of elements in the project i-number of the conversion element j - number of the complexity level.

The next stage is correction of the calculated value resulting from the conditions of implementation of the real system, embracing 14 factors, including: conversion distribution, productivity of the final user, simplicity of installation. It is assumed that assessment of the value of these factors is subjective and arises in the course of observing the implementation of the information system. The complex value of the corrected function points is calculated on the basis of the equation: PF = NPF x (0.65

+ 0.01

x

t

K;)

(2)

where: PF - complex value of the corrected function points NPF - total value of uncorrected function points K; - value of corrective co-factor. The value PF is the basis for assessing the labour intensity (expressed in people months) of implementation of the information system. Trans-calculation of the cofactor PF to a labour intensity value takes place with the help of the labour intensity curve, arising on the basis of assessment of implemented information projects. The method offunction points analysis,like COCOMO, is characterised to a considerable degree by the influence of subjective judgment, and not by objective indicators in the assessment of project implementation. This means that assessment of information projects by the use of the solutions presented demands considerable experience and acquaintance with often complicated algorithms of conduct in applying these assessments. It also means that their use to manage changes and risk is markedly limited because of the complexity of the risk and change issues in enterprises. 1.3. Description of project teams

Managing information enterprises demands a considerable involvement of human resources. The most beneficial solution seems to be cooperation with the external

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organiser of the enterprise, definition of attitudes and tasks of particular people and assignment of work to be carried out to a team consisting of employees possessing a high level of subject knowledge and considerable organisational skills. In assembling the team, account should be taken (see Heller [27] and Kerzner [32]) of the changes that occur in organisational structures. This means that the production processes should take account of the roles of the employees and their influence on the product under production. Such organizations are seen as a complex system in which information tools and management techniques are directly connected with each other (Workflow Management, Groupware Process Reengineering, Computer Supported Co-operative Work) and function as a team defining and implementing the aims. In speaking of project teams we define them as [68] "distinguished from the structure of the organization, commissioned for a defined period and consisting of specialists from various fields, whose knowledge and experience have a bearing on the problem". They are called into being as a result of the small efficiency of operation of the organization and the necessity of implementing project tasks. Today, in the era of innovative approaches to organization, the role of project teams has increased owing to the notable effectiveness of their operation. The majority of projects implemented today both in industry concept of manufacturing body cars and in the field of show business depend on the cooperation of groups of people working within the framework of project teams. The idea of project teams derives from the concept of the synergy of knowledge. For this reason also the results of team work are not commensurate with the results of the activity of groups that do not co-operate with one another. It is assumed that project teams can be of both formal and informal composition. The first are assembled to implement a particular task, while the second are often structures functioning within enterprises for implementing shared tasks. Project teams are brought together to implement a particular task. It is assumed that the project team is assembled by the project manager, whose task is to present to the team the aims of the team's operation. As a rule the team assembled is interdisciplinary, which makes it essential to apply solutions for consolidating the team (a variety of specialists, various visions of the aim, sources of conflict in implementing the tasks). According to Butler [10], another solution is to call into being an executive team to implement a task, create a system, or put a project into action. Typical executive teams are: problem teams (Work teams) summoned to assess a project, project teams (Project groups) assembled for a longer period of time to implement a longer-term task and advisory teams (Reference Groups). The aim of the advisory team is to manage the many project teams implementing partial aims. On the level ofinternational organizations there are also task forces such as steering groups (Steering Committee) whose members include representative-experts. Their role depends on directing large global and international economic enterprises. Another type of project team is the group with a particular defined aim (Task Force). A characteristic of these teams is the fact that they consist of a narrow group of specialists concentrated on carrying out a narrow task.

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1.4. Models for assessing team and project processes

I

f I

Optimizing level (5)

I

(4)

I

Defined level

I

(3)

Repeatable level (2)

I

I

l i initial level (1)

Figure 2. Five levels of process maturity.

In this chapter, the presentation ofmodels for assessingteams and project processes is important with regard to recognising the formal possibilities of assessing project teams and processes implemented. The models CMM, SPICE and norms of the ISO 9000 type are presented. According to Paulk [126] they constitute effective mechanisms for assessing team and project processes and support the work of managers. The CMM model-Model of Process Maturity-should supply solutions making possible the control of project processes. Its application supports assessment of team management processes and defines their level of maturity. It also identifies critical elements of the process that affect the quality of the system being created. Details of the construction of the model are contained in the work of Paulk and Weber [55], while the former also contains information on its use [54]. The team is assessed on the basis of five levels of maturity of the project processes: initial, repeatable, defined, management and optimizing. The structure of the CMM model is presented in fig. 2. Assessment of the level of project teams is dependent on their method of implementing project processes and on the influence of the environment. For example, a team on the initial level is characterised by the lack of a stable environment. A team on the repeatable level is controlled by the agency of a system of information management (Management Software) [35]. It is distinguished by stable planning processes and by tracking the project, which means that it is properly managed and constitutes an integrated work environment.

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Table 1 Key areas for the levels of process maturity Maturity level

Key areas of the process

Initial level Repeatable level

Lack of stable environment Management of project solutions Assessment of quality of programming Management of contract details Tracing and supervising project processes Planning project processes Management of demands Quality management Measurement of processes and analyses Management of changes in processes Technological innovations Protection against errors

Management level Optimizing level

A team on the defined level is characterised by the use of standard processes in creating information systems. It applies information systems to management of the project team as well as supportive design processes that create an integrated project environment [34]. It is defined as SEPG (Software Engineering Process Group)-a team making use ofinformation tools to define and support its activity by constantly training the work force, raising their skills in imparting knowledge. Members of the team define their own processes for specific types of projects under implementation. In implementing project processes their assessment is applied (Peer Review) in order to raise the quality of the information system [38]. A team on the management level defines quantitative criteria for assessingthe quality of the system being created. Productivity and quality become measurable values. Systems making use ofdatabases collect up-to-date information on the subject ofprocesses being implemented. Processes and products are defined quantitatively.

The functioning of teams on the optimizing level depends on the concentration of work around processes that ensure the possibility of their being constantly improved. Weak elements are sought out and strengthened as they appear. Innovative solutions are introduced, mainly based on new technologies. Key areas of the processes for the levels of maturity are defined (table 1). Thus the model CMM constitutes a solution whose nature is both qualitative and quantitative, which can be applied to assessingproject teams. The assessment indicates the level of maturity and at the same time the level of risk in implementing the enterprise. It is therefore an important indicator in team selection (level of maturity of the team), method of directing the team (key areas of processes) and exploitation of information technologies. The question arises, however, whether this model can be a pattern of conduct in defining maturity levels of teams and processes. In the course of creating a project consortium with the task of implementing project COMMODORE (financed from European Union resources), an assessment of the level of maturity of the organization was carried out by a potential coordinator on the basis of questionnaires issued to two project teams. This showed that:

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implementation

Figure 3. M ethod of assessing project processes.

• identifiers are defined too precisely, which causes problems in referring them to the functioning of real teams; • in order to conduct assessment, considerable knowl edge and experience is needed in assessing teams on the basis of identifiers; • the assessment refers to the initial state of the team (before implementation of the enterprise), whereas its level of maturity may undergo change in the course of implementation ; • assessme nt of teams is carried out on the basis of states, and not the character of processes, e.g. whether it uses Cants's diagrams, not how it uses them, whether it tests user quality, not how it tests user quality; • problem s appear with quantitative assessment of the solution obtained on two levels of maturity. The SPIC E [29] model for assessing processes is an example of a solution of both quantitative and qualitative character for estimating the processes of creating information systems. It is applied in estimating project processes. The procedure is present ed in fig. 3. S PIC E can be used by organizations dealing with monitoring, developing and improving proje ct processes, covering [4]: • possibility of self-assessment of implement ed project processes; • assessme nt of proje ct implem entation environment; • creation of a set of methods for assessing processes (profile of pro cesses); • creation of conditions for directing pro cesses. The model may be used in th e work of project teams of varied sizes and implement ation capacities. It is accepted that the estimation of processes is based on their repeatability.

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Table 2 Description of process categories Process category

Description of category

Customer-supplier-CUS Project ENG (Engineering) Project management PRO (Project)

Processes on which the user has a direct influence Processes that specify, implement and maintain the system Processes of creating the project infrastructure, co-ordination and management of resources Processes supporting other processes in the project Processes of assessment and support for the business character of teams as well as product improvement

Support SUP Organization

Initial assess ment of processes .ItS Aim of the project .ItS Scope itS Limitations .itS Possibilities of

Prat e s asse sment Too ls for process assessment .ItS Indicators .ItS Comparative scales Process models itS Process selection itS Verification

.ItS itS

Final assess ment Quantitative adequacy Process capaci ties

Figure 4. Elements of SPICE.

It is assumed that each implemented project process is characterised by certainty, weakness and executive risk. It may be assessed according to the stated aim, time of implementation and costs incurred, as well as the possibilities of its implementation and the project risk. The stages of assessment of the project processes (fig. 4) provide the initial and final assessment, in the course of which the model of project processes and information tools are used. Initial assessment of the processes takes into account: the aim of implementing the project, its scope, limitations and implementation capacities as well as definitions. The final assessment covers the level of adequacy in relation to the model processes and the possibilities of their implementation. Identifiers of processes are used for assessement, as are comparisons of real processes in relation to model ones. The SPICE model constitutes a complement to several other international standards of assessment ofprocesses, presented for instance in the work ofCrosby[12], Dion [16] and other models for assessing the capacities and effectiveness of teams and processes. In table 2 categories of processes are presented, while within the framework of particular categories the type of process is defined and codified as:

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• PC (Process Category)-identifier of the process category; • PR (Process Number)-number of the process; • CL (Capability LeveD-level of capability; • CF (Common Future Number)-common identifier; • PT (Practice Number)-number shared with another process. As examples, within the category of project process (ENG) particular processes are defined:

ENG. 1 ENG. 2 ENG. 3 ENG. 4 ENG. 5 ENG. 6 ENG. 7 -

changes of requirements in relation to project processes; changes of requirements in relation to project tools; changes of requirments in relation to the system; application of project tools; integration and software tests; integration and system tests; system maintenance and programming.

According to the Base Practice Adequacy Rating Scale these processes are estimated using the scale: • N - inappropriate-its implementation does nothing to meet the aims of the enterprise; • P - partially appropriate-its implementation does something to meet the aims of the enterprise; • L -largely appropriate-its implementation does a great deal to meet the aims of the enterprise; • F - totally appropriate-its implementation entirely meets the aims of the enterprise. Analysis ofthe SPICE model shows that the division presented there into categories and processes is too detailed, which means that managers have serious problems in classifying the implemented project processes. Identification of processes, e.g., designing the rules of the knowledge base, enables them to be placed in one of the categories, e.g., ENG. It is clear whether they are ENG3 processes-changes of requirement in relation to the system, or ENG5-integration and programming tests. Application of the Base Practice Adequacy RatingScale also becomes complicated-e.g., partially appropriate or largely appropriate, while the presence of subjective judgement considerably influences the result, which means that the main criterion in conducting assessment becomes acquaintance with the method and experience in applying it. It therefore becomes essential to seek solutions that make possible a quantitative assessment of teams and processes. They should: • minimalize the aspect of subjective assessment of processes and teams; • limit the complexity of the method of assessment and adapt it to the level of the manager;

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• create easy possibilities of implementation, taking account of the operative system and the project tools • be based on expert knowledge of SPM • fulfil the role of a storehouse of knowledge that is constantly updated by new experrence; • enable the scenario of action to be analysed.

It is worth emphasising that in practice team directors in situations in which implementation problems arise are not interested in applying methods for assessing processes but in completing the project in the set time and by the agreed means. 2. NEW POSSIBILITIES FOR CREATING THE SPM MODEL

The view often appears in subject literature [58] that SPM is more an art than a planned method ofaction. This view results form the fact that in the course ofproject work the decisions taken by the team leader are the results of processes that are difficult to plan, such as for example changes in the make-up of the team (the best programmer may be "bought up" by a rival firm). For this reason also many managers consider experience to be the main source of knowledge on enterprise management. They connect its success (completion within the set time, with the agreed means and previously established quality) with the ability to forecast changes and reactions to increased risk in completing the project. Others in turn, while not questioning experience, assert that it is not possible to plan, organise and control an enterprise without applying formal methods of procedure to management (system approaches). Today the top down approaches described by Budgen [8], Sommerville [67] and Yourdon [75] are used. According to Ganea [22], in top down approaches to modelling information enterprises, the task division is a function ofboth the particular features ofthe given enterprise and the standards ofwork accepted by the programming firm. This is connected with models of programming construction that define the methods of implementing project tasks [30], e.g., the cascade model, prototyping, incremental implementation, spiral model and formal transformations. Besides the model ofprogramming life cycle, another solution that makes use of the top down approach is diagnostic analysis, presented among others in the work of Kusiak [42]. Two phases of its application can be distinguished: analysis of the existing state and definition of the anticipated state. It is used in designing organisational, technical, economic and social systems. A conceptual approach is represented by prognostic analysis [52]. The procedures embrace: justification of the aim of research and a synthetic stage that contains a working out of the concept of the system and an analysis of individual elements on this basis. Methods based on selection and reduction of variants of the solution are used, ensuring the adaptation of the model to the conditions and limitations of the enterprise. System analysis from the cybernetic aspect [37] makes use of reversible spring techniques. It is used in analysis oftechnical systems [53] (ofthe SCADA type, diagnostic, advisory) and organisational ones (of the SWD type-decision support systems).

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The initial stage requires detailed description of the system, after which an attempt is made to describe it mathematically. Mathematical system analysis [42] is used mainly in building models ofthe black box type, in which the relationship between input and output parameters is analysed by means of the operator Ax [69] next a detailed identification of input parameters is conducted. This may take the form of Vetter's linear integral operator. The methods of assessing projects in fuzzy categories (Fuzzy Projects) discussed in the work of Slowinski [66], Weglarz [72], Hapke and Jaszkiewicz [26], are also used. These are methods of scheduling projects by metaheuristic algorithms, that is genetic/evolutionary algorithms, simulated annealment, searching out taboos. Such methods are also used in problems ofcontinuous and non linear, aswell asin problems of combination optimizing. The multifaceted variation ofthis algorithm (Pareto-Simulated Annealing-PSA) makes it possible to search for representations of competitive (not dominated) solutions. It is also possible to use the interactive search method (Light Beam Search) [48] to support the work ofthe manager in choosing one ofmany solutions in the area found by PSA. The formal, many-criterioned, static, dynamic and informal methods presented in the work ofBubnicki [7] and Ghezzi [23] are also used. A different group ofsystem models are the models of management in uncertain conditions presented by Pawlak [56] and Kacprzyk [31]. Their characteristic feature is incomplete or absent information. Examples of types of such models are relational, probability, game and fuzzy. Relational models are characterised by definition of the dependency between conditions and results. In statistical models of the probabilistic type of the breakdown of probabilities used in decision-making or selection of factors is analysed. Game models make sue of game theories to assess members of the team, decision-makers or coparticipants in decision making. Peled presents a model of certainty of solution [57]. Fuzzy models enable decisions or management processes to be analysed in situations in which an algorithmic description is impossible, but expert assessment are applied [49]. The work of Krawczyk and Mazurkiewicz [40] presents the creation of applications by the use of a method supported by heuristic techniques and information tools (Borland c++ Builder). This method is based on a conceptual model applied to a skeletal application which is then implemented. This fits into the concept of project patterns (Design Patterns) presented in the work of Buschmann and others [9]. An object approach is used in analysing and specifying as well as in implementing the system, which covers elements with "independent concurrent units". For what remains, the activity and methods of their use are defined: • method of making a component of a system-program, library, types of function; • inter-accessibility-method of communication between component and user; • functions used by other components. Projects and project groups are used. Projects enable elements to be built on a modular basis, while groups support the compilation process, making it possible also to use files which can be a base for creating components in other environments.

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2.1. Use of modelling and simulation theories

Conducting modelling processes demands definition of basic concepts appearing in modelling and ofthe relationship between the model and the real system. According to Cross [13], the aim of modelling is to examine "the relationship between real systems and models". The modelling relationship concerns the relevance of the model, that is its correspondence with the real system. The degree of this correspondence is measured on three levels. One: the model has repetitive relevance, if the data it generates correspond to the data obtained earlier from the real system. Two: the model has predicative relevance, that is the correspondence of the two groups of data can be assured before obtaining data from the real system. Three: the model has structurally relevance, if not only duplicates the observed reaction of the real system, but also faithfully reflects the way the real system works. The basic framework of the modelling process covers: • informal description of the model (with the use of the following notation: natural language, diagrams, support techniques-semantic networks, frames) with the aim of defining interaction of elements and descriptive variables; • formal description (structural or object) according to the following categories: time-continuous and discrete models; values accepted through chance variablesdiscrete, continuous and variable; chance variables-deterministic and stochastic; category of model's effect on the environment (lack-autonomous) and (effectnon-autonomous) [21]; • implementation of the model with the use of information tools. In system modelling hierarchical approaches are used with the following stages (fig. 5): • description of the real system; • description of the structure of the experiment; • definition of the basic model; • acceptance of the integrated model; • implementation of the model with the use of information tools; • testing the results obtained; • assessment of the model. The real system is defined as the source of data; it may also be defined as a natural, artificial or mixed system, analysed in categories of observable and non-observable descriptive variables. Observable variables include input and output variables (the result of the operation of input variables). The structure ofthe experiment involves a collection ofdata (subsets ofinput-output relations), in which the real system-management on the project team level-can be described. The basic model represents all existing input and output of the real system (within the framework of the experiment's structure) and should supply essential information about the reactions of the real system.

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Figure 5. Hierarchical approaches in modelling.

The relevant integrated model arises from a simplification of the basic model. 2.2. Application of fuzzy set theory Fuzzy models of the linguistic type (Linguistic Models LM) are models with a set of rules of the type IF-THEN of underfined conditions and fuzzy conclusion drawing. They are discussed by Yager [74]. In turn, models containing logical rules with fuzzy condition and functional conclusion are presented in the work of Tong [70]; they carry the name Takagi-Sugeno-Kanga (TSK). The most commonly used model is Mamdani's model [47], describing a real system with the help of linguistic rules. The example below presents the process of fuzzy modelling for a case involving two input variables and one output variable. The rules are:

(3)

where:

A, Bj , Ck - fuzzy sets u1,

U 2, Y - input and output variables i, j, k - quantity of fuzzy sets

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The Takagi-Sugeno-Kanga (TSK) models were also presented in the work ofKlir [36]. They are also known as quasi-linear models or fuzzy linear models. The TSK models differ from Mamdani's model by the form of the rules. IF (Uj is A,) AND (U2 is B i ) THEN (y is f(Uj, U2))

(4)

where:

f (u1, U2)

marks the function of the output variable oflinear or non linear form.

Relational models were worked out by Pedrycz and presented in the work of Piegat [59]. It is accepted in these that fuzzy rules are treated as partially true. The appropriate co-factor of trust is subordinated to them. The theory of relational equations is used in identifying the bases of the rules. The global and local fuzzy models presented in the work of Dean [15] refer to the conditions in which global space is divided into local spaces in order to obtain a high degree of accuracy. Here both Mamdani's model and TSK are created. The basis for building fuzzy models is the fuzzy set, which is used to assess physical size, states of the system and properties of the objects [76]. We describe as fuzzy sets (Ai), sets of pairs: (5)

where: f-LA i ( U l ) is a function the value

Uj'S

membership of the fuzzy set Ai.

Linguistic variables represent a type ofinput, output or state variable, e.g., state of management of an information enterprise. Linguistic value is a verbal assessment of the linguistic variable (example for the variable described above: adequate, inadequate). Examples of fuzzy numbers are: around zero, more or less 5, a little more than 9, somewhere between 10 and 12. In turn, the linguistic space oflinguistic variables (Linguistic Term-Sets) is a set of all the linguistic values applied in assessing linguistic variables. Membership function /LA i (u 1) realises the reflection of variable value u 1 to [0, 1]: (6)

Examples of the membership function for set Ai are presented in fig. 6. The number of pairs (f-L Ai (u1), U1) appearing in the set is called the power of the fuzzy set

IIAill = n

(7)

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Figure 6. Exampl es o f membership function.

F ZZI FICAI FE REI CE TIO Jl AI (II \ ) " 2 -

. I mbership functi n building for input value II,.U,

-

Jl B . (II 2 I

•

Rul bend n to rship functionhuilding for output value

DEF ZZI FIC T IO Cal ulating cri p valu u ing mem hip function

y

.v

Figure 7. Pro cesses of fuzzy m ode lling for a case invo lving two inputs and o ne output.

Th e processes of fuzzy mod elling for a case involving two inputs and one output are presented in fig. 7. The fuzzy modelling processes (for two inputs and one output) include: fuzzification, inference and defuzzification. In fuzzy processes for crisp values (u I , uz), constituting input to the mo del, their degree ofmembership offuzzy sets (Aj , Bj ) is calculated . A condition of implementing fuzzy processes is definition of the membership functi on (/LA;(lit), /L B, (liZ)) of fuzzy sets. ln conclusion drawing processes the memb ership function for the output value (/Lck (y)) is calculated on the basis ofthe input degree ofmembership (/LA; (III), /LB; (liZ)) ' C onstruc tion of th e membership function for the output variable (y) takes place in th e following stages: • construc tion of the rule base; • activation of the concl usion mechanism ; • definit ion of the degree of membership for the output value of the model; • calculation of the crisp value for the output value.

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Analysis of the structure ofthe fuzzy model and mechanisms offuzzi±ying, inference and defuzzifying reveals the considerable influence ofthe kind of operators used on the accuracy of the fuzzy model. It has been demonstrated that in the case of self-tuning models the selection of operators has less significance because of the model's learning processes. If we use untunable models, the influence ofoperators is considerably greater and cannot be compensated for in any way. According to Driankow [17], it is then necessary to use the trial and error method in the processes of selecting operators. According to Gupta [25], the indicator for applying various operators is the frequency of their use. Knowledge of the use of operators is the important in so far as in the course of constructing the model it allows preliminary estimates to be made of their effect on the accuracy of the model. 2.3. Application of elements of fuzzy regulator theory

On the basis of Drucker's work [18] it can be agreed that management processes include: planning, organization, motivation, monitoring and decision making. Modelling such processes requires knowledge of theories of steering and modelling mechanisms of feedback mechanism as well as definition of the object and regulator of steermg. The general form of the steering law for the arrangement presented in fig. 8 is as follows: (8)

where:

Jl - steering function, fuzzy rules t - time

in the case of fuzzy regulators described with the help of

where: 5 -

denotes the object of steering

w - value set

y - reaction of the object at output

c - steering regulator u - steering signal

e - error (e

=w

- y)

By the concept of regulators of the FLC type (Fuzzy Logic Control) we understand the steering law in the form ofrules ofthe IF- THEN type, with fuzzy conditions and steering mechanism based on fuzzy logic, the following are used in modelling them: • expert knowledge of system operation, definition of its function and the construction of an informal model in the phases of analysis and synthesis, modelling and production;

226 Cezary Orlowski

Figure 8. Block outline of the steering arrangement with feedback mechanism.

• experience of knowledge engineers and experts in creating and implementing a formal model-simulation and quality management methods are used; • decision techniques, agent systems [62], design patterns [9] and shell type environments [33]; • measurement data of system input/output (self-organising models); • measurement data of system input/output (self-organising and self-tuning models). A definition of the self-tuning model was given in the introduction, while the selforganising model is understood as characterised by ability to define the optimal number of rules, their form and the fuzzy sets [17]. Rules constructed with the use of expert knowledge are an example of solutions that integrate open and hidden knowledge from the content area. A drawback is the considerable influence of the expert's subjective judgement on the form of the rules representing open knowledge. Sometimes two different models of the same system arise. The selection of the parameters of the membership function minimalizes the model's error with regard to the system. The selection of the assessment criterion (size of model error) depends on the modeller, who accepts: average real error and maximum error. For the remaining cases the number of rules and fuzzy sets as well as the input and output data are automatically selected in the course of modelling. The construction of a self-tuning and self-organising model, treated as a dynamic fuzzy regulator, involves the following stages: • analysis of the method of modelling appropriate to building the fuzzy regulator; • analysis of fuzzy steering; • design of dynamic fuzzy regulator; • construction of a model of the fuzzy regulator; • denotation of linguistic variables; • construction of the knowledge base; • tuning. The steering function 11 in the FLC regulator for the dynamic input/output model is described by a rule base of the form: IF u, is B 10 AND u,_! is B ll AND ... AND Y'-n is AI" THEN y, is A 10

(9)

IF u, is B zo AND u,_! is B Zl AND ... AND Y'-n is A Zn THEN y, is A zo

(10)

IF u, is B mo AND u,_! is B m l AND ... AND Y'-n is A 3n THEN y, is A mo

(11)

Methods of building knowledge-based systems applied in software project management 227

where: B lO , B l l , ••• B l l1 -input fuzzy sets, A lO , All, ... A111 - output fuzzy sets, Ut-l - input values, Yt - output values, t - time. In turn, tuning processes will involve: • denoting FLC parameters and scaling co-factors; • denoting the knowledge base for the regulator; • constructing the membership function; • minimizing the model error, e.g. absolute average error (algebraic difference between comparative value, obtained on the basis of the model, and the result of the measurement of the measured size in relation to the number of measurements). In some work [59] account has been taken of application of the following tuning processes: fuzzy neuron networks, searches, clusterisation, unfuzzy neuron networks and heuristic methods. Unfuzzy neuron network methods are based on transforming the fuzzy model into a fuzzy neuron network [73] and using measurement data in the processes of learning the network with the aim of tuning it. Search methods depend on using organised and unorganised forms to tune the model [63]. Clusterization methods depend on grouping the results of measurements in clusters and subordinating their centre of gravity to the apexes of the membership function. Unorganised forms are trial and error methods. An example of organised methods could be genetic algorithms [631. Unfuzzy neuron networks, like heuristic methods, are rarely used in model tuning processes. Other steering laws can be described with the help of ordinary differential equations and partial equations. These are continuous or discrete dynamic arrangements of concentrated or diffuse parameters, stationary or non-stationary [39]. The presented survey ofalgorithms ofsteering fuzzy regulators shows the possibilities of implementing them both in social and in technical systems after earlier definition of the steered object and steering regulator. The selection of a dynamic steering regulator and the methods of describing it are dependent, however, on the type of input and output trajectories of the system. In the author's earlier work [51] possibilities of employing fuzzy regulators in building management models were presented. 3. EXAMPLE OF BUILDING A FUZZY SPM MODEL

In this chapter it is accepted that fuzzy models are constructed using knowledge of SPM and ofenterprise modelling. In the first case this knowledge is obtained from managers, while in the second from specialists in the field of system modelling. In the first place knowledge concerning SPM is presented. The applied formal methods of describing it and the possibilities of using it in building a fuzzy model are discussed. On the basis

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Cezary Orlowski

of the collected knowledge of SPM and the solutions from the area of modelling that are capable of application, a concept of the structure of the model is presented. It has been assumed that the strategic approach for the modeller was the selection of methods of formalising the knowledge possessed by the manager with regard to the necessity of describing the complex socio-technical system that is SPM. Problems connected with the selection of formal methods appeared quickly in the course of the initial documentation monitored by the instigator ofthe enterprise, when it turned out that accuracy of description of often unique management processes, if their number is considerable, loses its meaning and does not lead to a full description ofthe system. The known statement of Zadeha [76], co-creator of the theory offuzzy sets, hence suggests itself "if the complexity of the system increases, then our ability to formulate accurate and also meaningful views of its behaviour decreases, until we reach a threshold value, beyond which precision and meaning become almost mutually exclusive features". Making use of complex mathematical apparatus gives the possibility of precise description of repeatable processes mainly for technical systems (mechanical, electric), whereas in the case of social systems a departure from such precise description is suggested [46], with the use of approaches based on fuzzy logic [48]. Therefore also in the case of modelling socio-technical systems such as SPM, the apparatus of fuzzy set theory may be used both in processes of formalizing knowledge and in adaptation of the model [68]. Then the construction of the fuzzy model of SPM (fig. 9) can be treated as a process of continuous modelling with the help of fuzzy algorithms. The idea behind this concept is to distinguish two areas: managing the enterprise and modelling it. The manager within the framework of the structure of the experiment, understood here as a set of management processes on the level of the project team in the course of implementing the information enterprise, provides the modeller with information. This knowledge concerns: the structure of the model created (number of input and output variables and the fuzzy sets characterising each of the variables, the form and number of the rules) and its parameters (the apexes of the membership functions) (fig. 9). The enterprise modeller transforms data obtained from the manager on the linguistic level and applies the apparatus of fuzzy set theory. The application of modelling on the linguistic level results from the effectiveness of transforming data obtained from experts [45]. The data concerning the parameters of the model are supplied by the modeller in the process of tuning. Next, the processes of adapting the parameters and structure of the model are carried out, steered by the manager, who while directly influencing (broken line) the modeller also affects the structure and parameters of the model, according to the criteria of experimental and model correspondence [48]. 3.1. The concept of model construction

Keeping in mind the accepted assumptions on the necessity of building a fuzzy model by exploiting the knowledge of the manager of the enterprise, the possibilities of making use in constructing the model of the knowledge of the enterprise manager (expert knowledge, methods and models) and of the modeller of the enterprise (basics of modelling and simulation, theory of fuzzy sets and regulators) have been presented.

Methods of building knowledge-based systems applied in software project management 229

1-

-

-

_.-

[

J

Software project management

Structure of the experiment

I Modelling theory

I

I

tructure of themodel

DATA :

>

J

Parameters

of the model ~-'"

Structural modelling on the linguistic level

Tuning model parameters

~.~

"'-~.

. . ....

-"

~

.

- . ~, ~ Adaptation?

;';-l Parameters l-

'- ,

~"'"

-._~

; Structure s

Area of management

I

Area of modelling

I

Figure 9. Continuous modelling as an open concept of system approach in building a fuzzy model of SPM with the help of fuzzy algorithms.

The enterprise manager's knowledge concentrates on recognising problems, describing experiences and methods and models applied. We have indicated the problems of SPM when the manager is inexperienced, or when the methods and models are applied to managing teams and processes that concentrate mainly on assessing teams and processes, on economic assessment, on assessment of time and project resources as well as knowledge. Appropriate examples have been adduced in support [29]. In the cases of the modeller of SPM, knowledge is collected concerning foundations of modelling and simulation processes, fuzzy set and regulator theory essential in defining the concepts and processing appearing in the course of modelling. The manager's and modeller's knowledge enables the processes of modelling SPM to be conducted with the use of an open concept of system approach in modelling SPM. This concept is presented in fig. 9, taking account of: • preparation of data concerning the structure and parameters of the model; • structural modelling on the linguistic level; • tuning the parameters; • adapting the parameters and structure.

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Data concerning the structure of the model

On the basis of the manager's knowledge it is accepted that SPM will be implemented on the level ofthe project team SPMz in four areas: management ofknowledge, project processes, infrastructure and supporting technologies. It is also accepted that SPMz will be analysed according to the phases of the enterprise, while the scope of activity of the manager will concern planning the selection, organization and monitoring of application ofinformation and management methods and tools (MNliZ). With regard to the dynamic changes in MNliZ the use of concepts of variable states in describing them is planned and an expert assessment of them according to the practices applied in SPMz is assumed. Three areas of exploitation of MNliZ are defined (for each field), as shown on a linear scale in fig. 10. These scales will be constructed for the previously given four areas of management both for methods and for tools of information technology and management. Two layers have been marked on each scale.

I

ta yer I

Scalar values for M liZ

[%1

METHODS

,

0

ta yerU

Range of MNliZ

i

so

I

,.

TOOLS

100

BBB

Figure 10. Method of creating a linear expert scale to define management states.

Layer I is described by three identified values: scalar states and methods of information and management and scalar states of information and management tools. They are calculated as a weighed value both for methods and for tools of information and management on a scale from 5% to 100% for methods and from 0 to 100% for tools. It is accepted that the choice of a scale for methods incorporating a 5% value results from the fact that it is impossible not to manage an enterprise (0%). It is accepted that the values of the co-factors appearing in calculating scalar states of methods and tools of management depend on: • in the case of managing infrastructure and knowledge-on the number of team members who apply MNliZ in relation to the number of team members at a given stage (ks-composition co-factor);

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• in the case of managing processes and supporting technologies-on the number of implemented processes in which MNliZ is applied in relation to the overall number of processes implemented at a given stage (kp---process co-factor). On the basis of the sum of scalar states of information and management methods and scalar states of information and management tools a generalised scalar state of management of knowledge, infrastructure, project processes and supportive technologies is defined. This is a sum that takes into account the influence ofmethods and tools, represented as weights (a 1, a2, f31, f32, Xl, X2, 8 1 , 82 ) , It is accepted that in calculating generalised scalar states of management weights are incorporated in which the values are established on the basis of expert assessment. Layer II covers the fields of the MNliZ used (which are obtained from experts on the basis of the best practice in managing information enterprises) that have an influence on its planning, implementation, monitoring and decision making.

Example Method of using the scale to define thegeneralised scalar state

of knowled};e management

If the manager ofan enterprise obtains knowledge by means of direct talks with experts and formalises them with the help of diagrams or a rule description, the application of this method has considerable influence on planning the obtainment of knowledge, on the control of its obtainment and the taking of decisions concerning the obtainment of knowledge. For this reason also, the idea of the previously given concept is to separate the area of knowledge management, in which for the given scopes of information and management methods used a scalar state of information and management methods is defined, as well as a scalar state of information and management tools. For the method of direct talks with experts the scalar state of information and management methods is defined at 5%, formalisation of knowledge with the help of diagrams at 50% and rule description at 100%. The scalar state of information and management tools is similarly defined. Correct values are connected with the use of the co-factor kp resulting from the number ofteam members employing the given methods and tools in relation to all the members of the team. Next, the generalised scalar state ofknowledge management, being the sum of both values with the weights taken into account, is calculated. The use in the work ofproject teams ofnew or modified MNliZ involves employing financial resources defined further as resources. The manager, in employing new or modified versions of existing MNliZ, pays attention to the current generalised scalar states of management and plans resources and the implementation time for a stage of the enterprise in relation to the schedule and budget. Next, after introducing MNliZ, he assesses the resources set aside and analyses the task implementation time with the use of MNliZ. He also takes into account both the generalised scalar states of management and the planned and real resources as well as the time of implementing the stage of the enterprise. Analysis of the relationship between the exploited MNIiZ and the generalised scalar states of management as well as resources and time set aside for implementing the stages of the enterprise raises a question about the reversibility of

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Cezary Orlowski

the function, for which the arguments are the generalised scalar state of management and the values: real resources and implementation time. This function is reversible for the arguments: generalised scalar states of management and the values: resources, implementation time. On the basis of the values of resources and time one cannot however define the scalar states of information and management methods and the scalar states of information and management tools. In constructing the concept of the model, solutions from the field of management were ignored, such as "hand steering" the composition of the team. Equally irrelevant are so far identified phases of the enterprise (definition and specification of demands, construction of the model and its implementation), because the manager initially chooses MNIiZ for all phases of the project and conducts team training (if necessary). It is also accepted that the manager is a specialist in the field of information science and can select, exploit and assess information solutions applied in enterprise management; he is not simply responsible for selecting the team and supervising their work. Data concerning theparameters

of the model

A concept of a self-tuning fuzzy model of permanent rule structure has been accepted, making use of expert knowledge SPMz . The team manager collects knowledge in the knowledge base for the experiments, recording them in the form of rules whose structure corresponds to the rules of the fuzzy model. It is accepted that the experimental knowledge base is classified with regard to the type of enterprise (e.g., successful, unsuccessful). An expert in enterprise management, the co-ordinator of the enterprise or team leader conducts this classification. Successful enterprises are defined as completed in the given time, with the agreed resources and implemented aim. By using this kind of solution we avoid "averaging", that is creating a useless model. The rules recorded in the experimental knowledge base will be grouped according to the phases of the enterprise. Such ordering influences the method of identifying clusters and creates the conditions for calculating the co-ordinates of the centres of gravity of the clusters, identified later as co-ordinates of the apexes of membership functions. Adding new rules to the experimental knowledge base will effect a change of position of the centres of gravity of the clusters, and in consequence, of the apexes of the membership function. In the concept of the model the possibility of its selflearning is assumed, accepting the idea of selecting changes of position of the apexes of the membership function with the addition of new rules, earlier classified with regard to degree of certainty (defined as the ratio of the degree of membership of the variables to the rule analysed). The concept of introducing new rules to the experimental knowledge base is presented in fig. 11. In the conception of the model's construction it is also assumed that in the processes of tuning the model, knowledge will be exploited that covers management of project teams implementing information enterprises through international consortiums. The sources of knowledge will be: documentation of the enterprise's implementation and the knowledge of the co-ordinators and leaders of the project teams making up these consortiums.

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ADDING NEW RULES TO THE EXPER IMENTAL KNOWLEDG E BAS E

CALCULATL G Til E DEG REEOF CERTAINTY OF RULES

CALCULATIN G CEN TRE OF GRAVITY OFCl USTERS

MO DIFYING MEMBERSHIP FUNCTION

Figure 11. Procedures of introducing new rules to the experimental knowledge base.

Structural modelling on the linguistic level

A four-stage construction of the fuzzy model has been proposed (fig. 12). The first is analysis of the real system. It covers management of the project consortium consisting of several or more project teams. In this chapter, this structure of the experiment is called a hierarchical model. This concept is introduced in the desire to obtain a hierarchy of management levels for the project consortium and project teams. Next, a model is obtained, referring to management on the level of the project team SPMz. According to the theory of modelling [168], this corresponds to the basic model. In the conception of this chapter, creation of a fuzzy model has been assumed (according to the modelling theory-an integrated model), formalizing management processes with the help of fuzzy rules. Tuning the model parameters

We have proposed a concept of tuning the model that embraces construction of the membership function according to the phases of the enterprise for input, state and output variables. The membership functions will be tuned for input and output variables, while for the state variables, permanent membership functions are proposed (the apex-parameters of the functions and their form will be established). The construction of the membership function will be conducted with the application of data from implemented information projects, with the use of clusterizing methods. Adaptingtheparameters

It is planned to conduct adaptation processes on two steering levels: direction of the work of the project team SPMz (the object of steering) and adaptation of the model SPMz-RFM (Software Project Management-Rule Fuzzy Model) on the level of the steering regulator. On the first level adaptation will depend on the selection of "better" solutions of higher scalar value of generalised management states, while on

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Cezary O rlowski

FUZlYMODEL

Figure 12. Stages of construction of the fuzzy model.

the second , on qualification of the outp ut variables by the team leader according to the quality ofteam management. It is necessary to emph asis that there is a great variety of meth ods of implem entation and levels of quality in managing th e project teams implementing information projects, which leads to an over "averaged" , and therefore useless model. For this reason in the concept of adaptation on the level of the steering regulator, it is recommended that pre-selection of whole enterprises be carr ied out by an expert SPA1.z according to quality levels in team management , e.g., on "successful" and " unsuccessful" projects. T his will influence the classification of input and output variables to appropriate models and the position of the apexes of the membership function. The method of modelling and adapting the mo del presented in this work SPMz-RFM: (1) allows various (established by the expert) types of model to be obtained; (2) creates conditions for the enterprise manager to use the proper mod el appropriate to his knowledge on the level of managing the team actually implem enting the inform ation project. Adapting the structure

It is accepted that the model obtained will be a compl ete model. In connection with this, adaptation of the model structure is not assumed.

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3.2. Construction of the model

The proposed model relies on knowledge-based solutions and the theories of dynamic systems and fuzzy sets [39]. A detailed design of fuzzy models takes advantage of the experience ofmanaging two environmental projects. Data from these applications have been utilised in tuning the fuzzy model using knowledge-based rules and membership functions. Knowledge from a third environmental project has been applied to verify the system, by means of self-tuning mechanisms. Within this chapter, the symbol SPMT means generating a set of solutions, consisting of IT methods and tools, for a given project Team and SPM p generating a set of solutions, consisting ofIT methods and tools, for a whole consortium. The results of this work for Software-Project Management of Teams based on the Fuzzy-Rule Mechanism will be referred to as the SPMT-RFM system. 3.2. 1. Fuzzy Models of Knowledge- Based System for Software Project Management

The starting point for a models creation procedure has been an evaluation of a real system from the experimental perspective. Firstly, appropriate formal models have been developed and next, hierarchical and structural models of the project team have been constructed. With the project team in mind, the analytical (dynamic) integrated model has been developed. In order to build a useful model, elements of the fuzzy control theory have been used in the form of fuzzy rules of the Mamdani type. The result is a matrix and vector model with a fuzzy sub-system. The completeness and consistency of the fuzzy-model rules have been verified. Dynamic state variables have been introduced to define (temporarily) fuzzy values of the states of management. 3.2.2. Hierarchical model

The hierarchical model presents the hierarchical structure of management: whole project consortium and teams. The structure of the hierarchical model is given in fig. 13. It has the following levels of management (assigned to the respective management functions) : • Project co-ordinator: decisions made after comparing the scheduled and budgeted tasks with their actual status; • Project team manager: planning based on evaluation of the IT methods and tools used in the process; • Project team manager: decisions to change the IT methods and tools; • Project team manager: introduction and follow-up on the use of proposed solutions and their evaluation. Preliminary forecasts are made to support the decision-making system at the level of individual project teams, built using the model SPMz-RFM, the opposite ofthe project team management system SPMz. The decision-making support system generates actual increases in resources and time for further evaluation by the team manager. He decides

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how to use the time and resources and introduces his own technology innovations (seen as changes to the methods and IT tools used) and lets the team use them. As the work progresses, the increases suggested by the manager continue to be modified to reflect any changes. The changes result in actual increases in the time allocated for project tasks and resources for completing them; those are measured in stages using a measurement system for the individual teams. 3.2.3. Structural model

Let us now concentrate on modelling a system that is a single project team. Unlike the above hierarchical model (which we use to describe the developments within a whole system ofsoftware project management) obtained in the course of the process of model formalisation and description, the team management model performs a purely analytical function. It plays a key role in our synthesis process of the decision support system, which is meant for SPMz-RFM. The structural model that describes project management at the project team level, focuses on data preparation procedures and preliminary data processing for a dynamic subsystem, yielding data for the hierarchical system of SPM p on its 4th level (concerning SPMz). The whole project management is synthetic. This means that particular SPMz decision support systems are used to assist the general management processes at its team (4th ) level with a view to fulfilling a superior task of optimizing the management of the entire project. The structural team management model reveals a (module-based) structure of the formal (analytical) model of SPMz-RFM. As shown in fig. 14, it consists offour main areas: input data, preliminary processing of input data, dynamic SPMz model and output data. The model has references to the 3rd and 4th level of management in the hierarchical model (fig. 13).

Figure 14. The structural model of the SPMz-RFM (invert to SPMz).

3.2. 4. Integrated model

In the processes of integrated model design, simplification procedures have been used that eliminated descriptive variables (e.g. consortium level management) and grouped some elements (IT methods and tools according to the states of management and resources according to a project phase).

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The discrete time analytical integrated model shown in fig. 15 (compare fig. 14) describes the input variables (forecasted increases in the money and time), formal team-management sub-models (dynamic and static), state variables (knowledge, infrastructure, supporting technologies, and project processes), output variables (referring to actual increases in resources: project money and time).

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By introducing formal symbols of the data (variables) and operators (discrete functions), the model of SPMz-RFM (the fourth level of the hierarchical model given in Fig. 13) can be described as follows. The preliminary input data include technological innovations (the preliminary forecasted increases in the IT methods (t.mt) and tools (t. T,)), as well as the preliminary forecasted increases in the money (t. 5t) and the time (t.[,). Note that resource increases originate from the changes in the IT methods and tools generated in the 3rd level of the system (Fig. 13). The project phase plays the role of a decision variable 1, which reprograms the DSS used by project managers (Fig. 13). Preliminary processing also performs an analysis of resource increases, respectively these increases are derived from the project resources needed to implement or modify the IT methods and tools. The conversion of the input variables is done by using the function RIjJ-forecasting of preliminary increases resources vector t.g,. The variables of the changes of the methods and tools are aggregated in one vector of technological innovations t. vt , which is next converted

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to the vector of the previous increases of the management states (~Xt-l) using the function of the state of management changes R rr. Within the dynamic sub-model, the forecasted increases resources gt and the new management states x, are aggregated using a function R R allowing the determination ofthe actual increases: in money (~s t) and time (~c t) that prove to be necessary for the project implementation. A function R x , called a state transition function, shows the transitions of the management states during the project. As presented in fig. 14, resulting from suitable decomposition procedures, the integrated model ofthe project management at the team level relies on the above described variables, including the static and dynamic states, as well as on the characteristics of the static R\(J, R rr, RR and dynamic R x functions. As a result of our analysis, we propose to treat the model of SPMz-RFM as an integrated vector-matrix entity. Principally, the structure of this model includes the static and dynamic parts (sub-models). As shown in fig. 16, within the dynamic part containing two state-space S-S mechanisms, we have a classicallinear state-space sub-system (one of the S-S mechanisms) and a fuzzyrule F-R sub-system, including a F-R mechanism (static function) and a dynamic S-S mechanism.

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Thus, in general, this matrix-vector model of SPM[RFM covers two areas distinguished in the structural model (fig. 14, as well as fig. 15) as the static (pre-processing)

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and dynamic parts. The black border in fig. 16 separates the static and dynamic submodels, while the dotted line divides the dynamic part into the state-space and fuzzyrule sub-systems. 3.2.5. Tuning of the Fuzzy Model

The developed model of SPMz-RFM, which has been briefly described above, has all but one ofits elements established. Nevertheless, it is the fuzzy-rule (F-R) mechanism, included in the F-R dynamic sub-system of the full dynamic integrated model of SPMz-RFM, that needs a parameter tuning procedure. Thus the process ofoptimization (converting only the fuzzy-rule mechanism) should have two stages: • development of the rule descriptions for an experiment (using data from software projects performed in reality); the set of these descriptions will be referred to as the experimental knowledge base; • design of membership functions based on the experimental knowledge base. Two IT environmental projects were utilised to supply data for our knowledge base. Information on how these projects were managed has been acquired by using inductive methods of "machine learning". The particular sources of this knowledge were the following: • a documentation of the considered IT projects including descriptions of work packages; • an expert evaluation of the effects of project realisation and completion, as well as the project reports (including own materials, website publications, notes ofco-ordinators, etc.). At first, the number of rules for a FIRST PROJECT was defined as: n

=k x I =4 x

12

= 48

(12)

and in the case of a SECOND PROJECT the number was: n

= k x I = 10 x

12

= 120

(13)

where:

k - is the number of teams. 1- means the number of reports. 3.2.6. Adaptation of the model to the needs of newprojects

3.2.6.1. THE SPM-RFM MODEL AS A SUPPORT FOR SOFTWARE PROJECT MANAGEMENT. The project managers of selected teams decided that a THIRD PROJECT is of the same type (a similar subject, and the type of management) as the two previously considered projects. Therefore the previously tuned SPMz-RFM model (the fuzzy-rule

Method s of building knowledge-based systems applied in software project management 241

mechanism and the experimental knowledge base) could have been used as a support in the management of the THIRD PROJECT (obviously, in terms of the team manageme nt) as a decisions support system S PMz- R FM for evaluating the respective flow of design pro cesses. These pro cesses included: the preparation of initial data (indicato rs and data for the simulation models of pollutant emission and ambient concentration), model reliability tests and mod el integ ration. By considering the S PMz- R FM, the team managers have made their operating decisions on the forecasted project resources (precisely speaking, they could modi fy their decisions by look ing into the system's suggestions). D~fi ll il1g

the membership[unction

The changes in the cluster gravity centres have an effect on the membership functions. As a result the peaks of the memb ership funct ions have shifted. A modified model SPMz-RFM + THIR D _PROJECT has thus been designed (tu ned) by the use ofthe data from the THIRD PROJEC T, after it has been com pleted. As an analysis of the obt ained outcomes showed, the two decision-support systems (i.e. the SPMz-RFM model based on the originally designed fuzzy mechani sm and the SPMz- R FM + THIRD_PROJEC T model augmented by the knowledge of the new data) proved to be effectively similar (fig. 17). The indicated deviation s (in resources) are placed in the beginning, main and final phases of the project.

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~o

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The maximization over all possible transformations max, can be effectively computed by using a similarity measure sim., that is invariant with reference to translationrotation-scaling. Similarity of color and texture will be added as a weighted sum later on. In this way, a basic shape B can be used as a query to retrieve all images from f.. which are in Bel. Therefore, our approach generalizes the more usual approaches for single-shape retrieval, such as Blobworld [12]. Composite shape descriptions are interpreted as sets ofimages that contain all components ofthe composite shape. Components can be anywhere in the image, as long as they are in the described arrangement relative to each other. Let C be a composite shape description (iI, B 1 ) n ... n (in, Bn ) . In exact matching, the interpretation is the intersection of the sets interpreting each component of the shape: (2)

Figure 2 shows the semantics of the proposed language. Observe that we require all shape components of C to be transformed into image regions using the same transformation r. This preserves the arrangement of the shape components relative to each other-given by each ii-while allowing C el to include every image containing a group of regions in the right arrangement, wholly displaced by r. To clarify this formula, consider Figure 3: the shape C is composed by two basic shapes B 1 and Bz, suitably arranged by the transformations il and iz. Suppose now that

Knowledge based systems technology and applications in image retrieval 357

o

c

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I

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IJ

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II contains the image I. Then, lEe'" because there exists the transformation r , which globally brings C into I, that is, rOrj brings the rectangle B 1 into a rectangle recognized in I, and rOr2 brings the circle B2 into a circle recognized in I, both arranged according to C. Note that I could contain also other shapes, not included in C.

Definition 1 {Recognition] A shape description C is recognized in an image I if for every interpretation (:J, ll) such that I E ll, it is i «c». An interpretation (:J, ll) satisfies a composite shape description C if there exists an image I E II such that C is recognized in I. A composite shape description is satisfiable if there exists an interpretation sati~fying it. Observe that shape descriptions could be unsatisfiable: if two components define overlapping regions, no image can be segmented in a way that satisfies both components. Of course, if composite shape descriptions are built using a graphical tool, unsatisfiability can be easily avoided, so we assume that descriptions are always satisfiable. Anyway, unsatisfiable shape descriptions could be easily detected, from their syntactic form, since unsatisfiability can only arise because of overlapping regions (see Proposition 4). Observe also that our set-based semantics implies the intuitive interpretation of conjunction "n"-one could easily prove that n is commutative and idempotent. For approximate matching, we modify definition (2), following the fuzzy interpretation of n as minimum, and existential as maximum: (3)

Our interpretation of composite shape descriptions strictly requires the presence of all components. In fact, the measure by which an image I belongs to the interpretation of

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a composite shape description C::i is dominated by the least similar shape component (the one with the minimum similarity). Hence, if a basic shape component is very dissimilar from every region in I, this brings near to 0 also the measure of C::i (I). This is more strict than, e.g., Gudivada & Raghavan's or El-Kwae & Kabuka's approaches, in which a non-appearing component can decrease the similarity value of C::i (I), but I can be still above a threshold. Although this requirement may seem a strict one, it captures the way details are used to refine a query: the "dominant" shapes are used first, and, if the retrieved set is still too large, the user adds details to restrict the results. In this refinement process, it should not happen that other images that match only some new details, "pop up" enlarging the set of results that the user was trying to restrict. We formalize this refinement process through the following definition.

Proposition 1 [Downward refinement] Let C beacomposite shape description, andlet D be a refinement' oj C, thatis D ~ en (r', B '). For every interpretation ~, if shapes are interpreted as in (2), then D::i ~ cr. if shapes are interpreted as in (3), then Jor every image I it holds ~(I) ::::: C::i(I). Proo]. For (2), the claim follows from the fact that D::i considers an intersection of ::i , ,cthe same components as the one of C , plus the set ((rOr ), B )'-5. For (3), the claim analogously follows from the fact that D::i (I) computes a minimum over a superset of the values considered for C::i (I). The above property makes our language fully compositional. Namely, let C be a composite shape description; we can consider the meaning of C-when used as a query-as the set of images that can be potentially retrieved using C. At least, this will be the meaning perceived by an end user of a system. Downward refinement ensures that the meaning of C can be obtained by starting with one component, and then progressively adding other components in any order. We remark that for other frameworks cited above [31, 25] this property does not hold. We illustrate the problem in Figure 3. Starting with shape description C, we may retrieve (among many others) the two images 11, Iz, for which both C::i(I 1) and C::i(I z) are above a threshold t, while another image 13 is not in the set because C::i (1 3 ) < t. In order to be more selective, we try adding details, and we obtain the shape description D. Using 0, we may still retrieve Iz, and discard 11. However, 13 now partially matches the new details ofD. If Downward refinement holds, D::i (13) ::::: C::i (13) < t, and 13 cannot "pop up". In contrast, if downward refinement does not hold (asin [31]) it can be D::i (13) > t > C::i (13) because matched details in 0 raise the similarity sum weighted over all components. In this case, the meaning of a sketch cannot be defined in terms of its components. Downward refinement is a property linking syntax to semantics. Thanks to the extensional semantics, it can be extended to an even more meaningful semantic relation, namely, subsumption. We borrow this definition from Description Logics [23], and its fuzzy extensions [60, 56].

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DO c

D

Figure 4. Downward refinement: the thin arrows denote non-zero similarity in approximate recognition. The thick arrow denotes a refinement [21].

Definition 2 [SubsumptionJ A description C subsumes a description D iffor every interpretation ;;s, IY ~ C::J. if (3) is used, C subsumes D if for every interpretation ;;s and image I E ~, it is IY(I) :::: C::J(I). Subsumption takes into account the fact that a description might contain a syntactic variant of another, without both the user and the system explicitly knowing this fact. The notion of subsumption extends downward refinement. It enables also a hierarchy ofshape descriptions, in which a description D is below another C ifD is subsumed by C. When C and D are used as queries, the subsumption hierarchy makes easy to detect query containment. Containment can be used to speed up retrieval: all images retrieved using D as a query can be immediately retrieved also when C is used as a query,

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1

Figure 5. An example of subsumption hierarchy of shapes (thick arrows), and images in which the shapes can be recognized (thin arrows) [18].

without recomputing similarities. While query containment is important in standard databases [58], it becomes even more important in an image retrieval setting, since the recognition of specific features in an image can be computationally demanding. Figure 5 illustrates an example of subsumption hierarchy of basic and composite shapes (thick arrows denote a subsumption between shapes), and two images in which shapes can be recognized (thin arrows). Although we did not consider a background, it could be added to our framework as a special basic component (c , t , ,background) with the property that a region b satisfies the background simply if their colors and textures match, with no check on the edge contours. Also, more than one background could be added; in that case background regions should not overlap, and the matching of background regions

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should be considered after the regions of all the basic shapes recognized are subtracted to the background regions. 5. REASONING AND RETRIEVAL

We envisage several reasoning services that can be carried out in a logic for image retrieval: 1. shape recognition: Given an image I and a shape description D, decide if D is recognized in I. 2. image retrieval: given a database of images and a shape description D, retrieve all images in which D can be recognized. 3. image classification: given an image I and a collection of descriptions D 1 , ... , Do, find which descriptions can be recognized in I. In practice, I is classified by finding the most specific descriptions (with reference to subsumption) it satisfies. Observe that classification is a way of "preprocessing" recognition. 4. description subsumption (and classification): given a (new) description D and a collection of descriptions D 1 , ... , Do, decide whether D subsumes (or is subsumed by) each D j , for i = 1, ... , n. While services 1-2 are standard in an image retrieval system, services 3-4 are less obvious, and we briefly discuss them below. The process of image retrieval is quite expensive, and systems usually perform offline processing of data, amortizing its cost over several queries to be answered on-line. As an example, all document retrieval systems for the web, both for images and text, use spiders to crawl the web and extract some relevant features (e.g., color distributions and textures in images, keywords in texts), that are used to classify documents. Then, the answering process uses such classified, extracted features of documentsand not the original data. Our approach can adapt this setting to composite shapes, too. In our approach, a new image inserted in the database is immediately segmented and classified in accordance with the basic shapes that compose it, and the composite descriptions it satisfies (Service 3). Also a query undergoes the same classification, with reference to the queries already answered (Service 4). The more basic shapes are present, the faster will the system answer new queries based on these shapes. More formally, given a query (shape description) D, if there exists a collection of descriptions D 1 , ... , Do and all images in the database were already classified with reference to D 1 , .•• , Do, then it may suffice to classify D with reference to D 1 , •.. , Do to find (most of) the images satisfying D. This is the usual way in which classification in Description Logics-which amounts to a semantic indexing-can help query answering [42]. For example, to answer the query asking for images containing an arch, a system may classify arch and find that it subsumes threePortalsGate (see Figure 5). Then, the system can include in the answer all images in which ancient Roman gates can be recognized, without recomputing whether these images contain an arch or not.

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The problem of computing subsumption between descriptions is reduced to recognition in the next section, and then an algorithm for exact recognition is given. Then, we extend the algorithm to realistic approximate recognition, reconsidering color and texture. 5.1. Exact reasoning on images and descriptions

Theorem 2 [Recognition as mapping] Let C = (T1, B1) n ... n (Tn, Bn) be a composite shape description, and let I be an image, segmented into regions {r1,"" rm } . Then Cis recognized in I iff there exists a traniformation T and an injective mapping j: {1, ... , n} ---+ {1, ... , m} such thatfor i = 1, ... , n it is

Proo]. C is recognized in I iff

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= T(Tl(e(B;)))]

and since regions in I are {r1' ... , rm } this is equivalent to

Making explicit the disjunction over j and conjunctions over i, we can arrange this conjunctive formula as a matrix:

:3T

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(e(rl)

= T(Tj(e(B 1 ) ) )

(e(rl)

= T(Tn(e(B

v v

:

n)) )

v

v

~]

(4)

Now we note two properties in the above matrix of equalities: 1. For a given transformation, at most one region among r1, ... , rm ean be equal to

each component. This means that in each row, at most one disjunct can be true for a given T. 2. For a given transformation, a region can match at most one component. This means that in each column, at most one equality can be true for a given T.

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We observe that these properties do not imply that regions have all different shapes, since the equality of contours depends on any translation, rotation, and scaling. We use equality to represent true overlap, and not just equal shape. Properties 1-2 imply that the above formula is true iff there is an injective function mapping each component to one region it matches with. To ease the comparison with the formulae above we use the same symbol j as a mapping j: {1, ... , n} ~ {1, ... , m}. Hence, Formula (4) can be rewritten into the claim: (5)

Hence, even if in the previous section the semantics of a composite shape was derived from the semantics of its components, in computing whether an image contains a composite shape one can focus on groups of regions, one group rj(I), ... , rj(n) for each possible mapping j. Observe that j injective implies m ::::: n, as one would expect. The above proposition leaves open which one between r or j must be chosen first. In fact, in what follows we show that the optimal choice for exact recognition is to mix decisions about j and T. When approximate recognition will be considered, however, exchanging quantifiers is not harmless. In fact, it can change the order in which approximations are made. We return to this issue in the next section, when we discuss how one can devise algorithms for approximate recognition. Subsumption in this simple logic for shape descriptions relies on the composition of contours of basic shapes. Intuitively, to actually decide if D is subsumed by C, we check if the sketch associated with D-seen as an image-would be retrieved using C as a query. From a logical perspective, the existentially quantified regions in the semantics of shape descriptions are skolemized with their prototypical contours. Definition 3 [Prototypical imageJ Let B be a basic shape. Its prototypical image is I (B) = {e(B)}. Let C = (TI, B I ) n n (r;, B n ) be a composite shape description. Its prototypical , r, (e(B n )) } . image is I (C) = {TI (e(B I ) ) ,

In practice, from a composite shape description one builds its prototypical image just applying the stated transformations to its components (and color/texture fillings, if present). Recall that we envisage this prototypical image to be built directly by the user, with the help of a drawing tool, with basic shapes and colors as palette items. The system will just keep track of the transformations corresponding to the user's actions, and use them in building the (internal) shape descriptions stored with the previous syntax. The feature that makes our proposal different from other query-bysketch retrieval systems, is precisely that our sketches have also a logical meaning. So, properties about description/sketches can be proved, containment between query sketches can be stated in a formal way, and algorithms for containment checking can be proved correct with reference to the semantics. Prototypical images have some important properties. The first is that they satisfythe shape description they exemplify-v-as intuition would suggest.

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Proposition 3 For every composite shape description D, ifD issatiifrable then the interpretation (::S,{I(D)}) satisjies D. Proof From Theorem 2, using an identical transformation forj.

T

and the identity mapping

A shape description 0 is satisfiableif there are no overlapping regions in 1(0). Since this is obvious when 0 is specified by a drawing tool, we just give the following proposition for sake of completeness.

Proposition 4 A shape description D is satiifrable iffitsprototypical image I(D) contains no overlapping regions. We now turn to subsumption. Observe that if B I and B2 are basic shapes, either they are equivalent (each one subsumes the other) or neither of the two subsumes the other. If we adopt for the segmented regions an invariant representation, deciding equivalence between basic shapes, or recognizing whether a basic shape appears in an image, is just a call to an algorithm computing the similarity between shapes. This is what usual image recognizers do-allowing for some tolerance in the matching of the shapes. Therefore, our framework extends the retrieval of shapes made of a single component, for which effective systems are already available. We now consider composite shape descriptions, and prove the main property of prototypical images, namely, the fact that subsumption between shape descriptions can be decided by checking if the subsumer can be recognized in the sketch of the subsumee.

Theorem 5 A composite shape description C subsumes a description D if and only if C is recognized in theprototypical image I(D). Proof Let C = (Tj, BI) n ... n (Tn, Bn ), and let 0 = (aI, AI) n ... n (am, Am). Recall that 1(0) is defined by 1(0) = {aj(e(A j)), ... , am(e(Am))}. To ease the reading, we sketch the idea of the proof in Figure 6. If. Suppose C is recognized in 1(0), that is, 1(0) E C"' for every interpretation (::S, 6.) such that 1(0) E 6.. Then, from Theorem 2 there exists a transformation i and a suitable injective functionj from {1, ... , n} into {1, ... , m} such that

Since 1(0) is the prototypical image of 0, we can substitute each region with the basic shape of 0 it comes from: (6)

Knowledge based systems technology and applications in image retrieval 365

(prototypical image of) C

prototypical image I (D) image J Figure 6. Schematic of the If-proof of Theorem 5 [21].

Now suppose that D is recognized in an image] = {SI' ... , sp}, with] E ~. We prove that also C is recognized in J. In fact, if D is recognized in ] then there exists a transformation and another injective mapping q from {l, ... , m} into [L, ... , p} selecting from] regions {Sq(l), ... , Sq(m)} such that

a

(7)

Now composing q andj-that is, selecting the regions of] satisfying those components of D which are used to recognize C-one obtains e(Sq(j(k)))

= fI °OJ(k)(e(Aj(k)))

for k

= 1, ... , n

(8)

Then, substituting equals for equals from (6), one finally gets

which proves that C too is recognized in], using a Of as transformation of its components, and q(j (.)) as injective mapping from {l , ... , n} into [l , ... , p}. Since] is a generic image, it follows that D:J ~ C!."l. Since (~, ~) is generic too, C subsumes D.

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Only if. The reverse direction is easier: suppose C subsumes D. By definition, this amounts to D~ S; C~ for every collection of images ~. For every ~ that contains I(D), then I(D) E D~ for Proposition l. Therefore, I(D) E C~, that is, C is recognized in I(D). This property allows us to compute subsumption as recognition, so we concentrate on complex shape recognition, using Theorem 2. Our concern is how to decide whether there exists a transformation r and a matching j having the properties stated in Theorem 2. It turns out that for exact recognition, a quadratic upper bound can be attained for the possible transformations to try.

Theorem 6 Let C = (rl, BI ) n ... n (r;, Bn ) be a composite shape description, and let I be an image, segmented into regions {rl, ... , r m}. Then, there are at most m(m - 1) exact matches between the n basic shapes and the m regions. Moreover, each possible match can be verified by checking the matching of n pairs of contours. Proof. A transformation r matching exactly basic components to regions is also an exact match for their centroids. Hence we concentrate on centroids. Each correspondence between a centroid of a basic component and a centroid of a region yields two constraints for r. Now r is a rigid motion with scaling, hence it has four degrees of freedom (two degrees for translations, one for rotation, and one for uniform scaling). Hence, if an exact match r exists between the centroids of the basic components and the centroids of some of the regions, then r is completely determined by the transformation of any two centroids of the basic shapes into two centroids of the regIOns. Fixing any pair of basic components B 1 , B2, let PI, P2 denote their centroids. Also, let rj(l), rj(2) be the regions that correspond to B I, B2, and let Vj(I), Vj(2), denote their centroids. There is only one transformation r solving the point equations (each one mapping a point into another) r(rl(Pl)) { r(rz(pz))

= Vj(l) = vJ(Z)

Hence, there are only m(m - 1) such transformations. For the second claim, once a r matching the centroids is found, one checks that the edge contours ofbasic components and regions coincide, i.e., that r(rl (e(B I))) = e(rj(l)), r(r2(e(B 2))) = e(rj(2)), and for k = 3, ... , n that r(rk(e(B k)) coincides with the contour of some region e(rj(k))' Recalling Formula (5) in the proof of Theorem 2, we can eliminate the outer quantifier in (5) using a computed r, and conclude that C is recognized in I iff N

3j : {I .. n} ---+ {I .. mj .« e(rJ(j)) 1=1

= r(rj(e(B

j) ) )

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Observe that, to prune the above search, once a r has been found as above, one can check for k = 3, ... , n that r (fdcentr(B k))) coincides with a centroid of some region rj, before checking contours. Based on Theorem 6, we can devise the following algorithm:

Algorithm Recognize (C,I); input a composite shape description C = (fl, Bj ) n ... n (f n, B n), and an image I, segmented into regions rl, ... , rm output True if C is recognized in I, False otherwise begin (1) compute the centroids Vj, , Vm of rj , ... , rm (2) compute the centroids Pt. , Pn of the components of C (3) for i, h E {I, ... , m} with i < h do compute the transformation r such that f(Pl) = Vi and f(P2) = Vh; iffor every k E {I, ... , n} f(fk(e(B k))) coincides (for some j) with a region rj in I then return True endfor return False end The correctness of Recognize (C, I) follows directly from Theorems 2 and 6. Regarding the time complexity, step (1) requires to compute centroids of segmented regions. Several methods for computing centroids are well known in the literature [37]. Hence, we abstract from this detail, and assume there exists a function f(N h, N v ) that bounds the complexity of computing one centroid, where Nh, N, are the horizontal and vertical dimensions of I (number of pixels). We report in the Appendix how we compute centroids, and concentrate on the complexity in terms of n, m, and f(N h, Ny).

Theorem 7 Let C = (fl, B1 ) n ... n (fn , Bn ) be a composite shape description, and let I be an image with Ni; x N; pixels, segmented into regions {r 1, ... , r m }. Moreover, letf (Nh, N) be afunction bounding the complexity of computing the centroid of one region. Then C can be recognized in I in time O(m· f(Nh, N v ) + n + m 2·n· Nh· Nv ) ' Proof From the assumptions, Step (1) can be performed in time O(m·f(Nh, N v ) ) . Instead, Step (2) can be accomplished by extracting the n translation vectors from the transformations fl, ... , Tn of the components of C. Therefore, it requires O(n) time. Finally, the innermost check in Step (3)-checking whether a transformed basic shape and a region coincide-can be performed in O( N, . Ny), using a suitable marking of pixels in I with the region they belong to. Hence, we obtain the claim.

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Since subsumption between two shape descriptions C and D can be reduced to recognizing C in I(D), the same upper bound holds for checking subsumption between composite shape descriptions, with the simplification that also Step (1) can be accomplished without any further feature-level image processing. 5.2. Approximate recognition

The algorithm proposed in the previous section assumes an exact recognition. Since the target of retrieval are real images, approximate recognition is needed. We start by reconsidering the proof of Theorem 2, and in particular the matrix of equalities (4). Using the semantics for approximate recognition (3), the expanded formula for evaluating C':l (I) becomes now the following:

I

max{slm(e(rl)' T(Tl(e(Bl)))),

max mill r

:

maxjsimfctr- ), T(Tn(e(B n))),

:1

Now Properties 1-2 stated for exact recognition can be reformulated as hypotheses about sim, as follows. For a given transformation, we assume that at most one region among rl, ... , rm is maximally similar to each component. This assumption can be justified by supposing its negation: if there are two regions both maximally similar to a component, then this maximal value should be a very low one, lowering the overall value because of the external minimization. This means that in maximizing each row, we can assume that the maximal value is given by one index among 1, ... , m. For a given transformation, we assume that a region can yield a maximal similarity for at most one component. Again, the rationale of this assumption is that when a region yields a maximal similarity with two components in two different rows, this value can be only a low one, which propagates along the overall minimum. This means that in minimizing the maxima from all rows, we can consider a different region in each row. We remark that also in the approximate case these assumptions do not imply that regions have all different shapes, since sim is a similarity measure which is 1 only for true overlap, not just for equal shapes with different pose. The assumptions just state that sim should be a function "near" to plain equality. The above assumptions imply that we can focus on injective mappings from {1 .. n} into {1 .. m} also for the approximate recognition, yielding the formula max. r

n

max

J{Ln}-->{1..m)

min (sim(e(rJ(j)), T(Tj(e(Bj))))} 1=1

The choices of rand j for the two maxima are independent, hence we can consider groups of regions first: max

j:{Ln}-->{Lm}

n

max min (sim(e(rj(j)), T(Tj(e(Bj))))} r

1=1

(9)

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Differently from the exact recognition, the choice of an injective mapping j does not directly lead to a transformation r , since now r depends on how the similarity of transformed shapes is computed, that is, the choice of r depends on sim. In giving a definition of sim, we reconsider the other image features (color, texture) that were skipped in the theoretical part to ease the presentation of semantics. This will introduce weighted sums in the similarity measure, where weights are set by the user according to the importance of the features in the recognition. Let sim(r, (c, t, r , B)) be a similarity measure that takes a region r (with its color c(r) and texture t(r)) and a component (c, t, r, B) into the range [0, 1] of real numbers (where 1 is perfect matching). We note that color and texture similarities do not depend on transformations, hence their introduction does not change Assumptions 1-2 above. Accordingly, Formula (9) becomes max

j:{l ..n}-->{1..m}

max mill (sim(rj(i)' (c, t, (r °ri)' Bi ) )} r

i=l

(10)

This formula suggests that from all the groups of regions in an image that might resemble the components, we should select the groups that present the higher similarity. In artificially constructed examples in which all shapes in I and C resemble each other, this may generate an exponential number of groups to be tested. However, we can assume that in realistic images the similarity between shapes is selective enough to yield only a very small number of possible groups to try. We recall that in Gudivadas approach [30] an even stricter assumption is made, namely, each basic component in C does not appear twice, and each region in I matches at most one component in C. Hence our approach extends Gudivada's one, also for this aspect-besides the fact that we consider shape, scale, rotation, color and texture of each component. In spite of the assumptions made, finding an algorithm for computing the "best" r in Formula (10) proved a difficult task. The problem is that there is a continuous spectrum of r to be searched, and that the best T may not be unique. We observed that when only single points are to be matched-instead of regions and components-our problem simplifies to Point Pattern Matching in Computational Geometry. However, even recent results in that research area are not complete, and cannot be directly applied to our problem. [11] solve the nearly-exact point matching with efficient randomized methods, but without scaling. They also observe that best match is a more difficult problem than nearly-exact match. Also [16] propose a method for best match of shapes, but they analyze only rigid motions without scaling. Therefore, we adopt some heuristics to evaluate the above formula. First of all, we decompose sim (r, (c, t, r , B)) as a sum of six weighted contributions. Three contributions are independent of the pose: color, texture and shape. The values ofcolor and texture similarity are denoted by simcolor(c(r), c) and simtexture(t(r), t), respectively. Similarity of the shapes (rotation-translation-scale invariant) is denoted by simshape(e(r), e(B)). For each feature, and each pair (region, component) we compute a similarity measure as explained in the Appendix. Then, we assign to all similarities of a feature-say, color-the worst similarity in the group. This yields a pessimistic estimate

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ofFormula (10); however, for such estimate the Downward Refinement prop erty hold s (see next Theorem 8). The other three contri butions depend on the pose, and try to evaluate how the pose of each region in the selected group is similar to the pose specified by the corresponding compone nt in the sketch. In parti cular, simscale(e(r), r (e(B)) represent s how similar in scale are th e region and the transformed component, while simrotation(e(r), r (e(B)) denotes how e(r) and r (e(B)) are similarly (or not) rotated wi th referen ce to the arr angement of the other compone nts. Finally, simspatial(e(r), r(e(B)) denotes a measure of how coi ncident arc the cent roids of th e region and the transformed compone nt. In summary, we get the followin g form for the overall similarity bet ween a region and a compon ent: sim(r,(c, t, r , E))

= simspatial(e(r), r (e(B)) . IX + Sim,hape(e(r), c(B)) . f3 + simcolor(c(r), c) · y + simroution(e(r), r (e(B)) . 8 + simscal e(e(r), r (e(B)) . I] + simrexture(t(r), t) . E:

wh ere co efficients a , fJ, y, 8, 1], e weight th e relevance each feature has in the overall similarity computation . Obviou sly, we impose ct + fJ + y + 8 + 1] + e 1, and all coefficients are greater or equ al to O. Because of the difficulties in computing the best r , we do not compute a maximum over all po ssible r s. Instead, we evaluate whether there can be a rigid transform atio n w ith scaling from '[1 (e(B ))), .. . , rn(e(Bn)) int o rj(I), . . . , rj(n), through similarities simspatial, simscaJe, and simrotatioll ' There is a transformation iff all th ese sim ilari ties are 1. If not , th e lower th e similarities are, th e less "rigid" the transformation should be to match co mpo ne nts and region s. Hence, instead of Formula (10) we evaluate the following simpler formula:

=

n

. max min {sim(rj(i)' (c, t, Ti , B J:{l..n}-> {l..rn} ,=1

l) ) }

(11)

int erpreting pose sim ilarities in a different way. We now describ e in detail how we estimate pose similarities. Let C = (CI' tl, '[I, B 1 ) n .. . n (c.,, tn, rn , B n), and let j be an injective functi on from {1 . . n } into {I .. m}, th at matche s compone nts with regions {rj(I), ... , rj(n)} respectively. 5.2. 1. Spatialsimilarity

For a given compone nt-say, co mponent I-we compute all angles under which the other compone nts are seen from 1. Formally, let ctil h be th e counter-c lockwiseorient ed angle with vertex in th e centroid of component 1, and formed by the lines linking this centroid wi th the centroids of compone nt i and h . T here are n (n - 1)/ 2 such angles. T hen, we compute the correspo ndent angles for region rj(I), namely, angles fJ j (j)j (l )j (h ) wi th vertex in th e cent roid of rj(J), forme d by the lines linking this centroid

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0/

Querylq

Image1d

0/

Querylq

Imagel«

Querylq

Imageld

Figure 7. Representation of angles uscd for computing spatial similarity of component 1 and region rj(l).

with the centroids of regions rj(i) and rj(h) respectively. A pictorial representation of the angles is given in Figure 7. Then we let the difference ~spatial (e(rj(l)), Tj (e(B1)) be the maximal absolute difference between correspondent angles:

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We compute an analogous measure for components 2, ... , n, and then we select the maximum of such differences: (12)

where the argumentj highlights the fact that this measure depends on the mappingj. Finally, we transform this maximal difference-for which perfect matching yields 0into a minimal similarity-perfect matching yields 1-with the help of the function described in the Appendix. This minimal similarity is then assigned to every simspatial (e(rj(i)), ri(e(B i)), for i = 1, ... , n. Intuitively, our estimate measures the difference in the arrangement of centroids between the composite shape and the group of regions. If there exists a transformation bringing components into regions exactly, every difference is 0, and so simspatial raises to 1 for every component. The more an arrangement is scattered with reference to the other arrangement, the higher its maximum difference. The reason why we use the maximum of all differences as similarity for each pair component-region will be clear when we prove later that this measure obeys Downward Refinement property. 5.2.2. Rotation similarity

For every basic shape one can imagine a unit vector with origin in its centroid and oriented horizontally on the right (as seen on the palette). When the shape is used a~ a component-say, component 1-also this vector is rotated according to rl. Let h denote such a rotated vector. For i = 2, ... , n let Yjll; the counte:;-clockwise-oriented angle with vertex in the centroid ofcomponent 1, and formed by h and the line linking the centroid of component 1 with the centroid of component i. For region rj(l), the analogous u of h can be constructed by finding the rotation phase for which cross-correlation attains a maximum value (see Appendix). Then, for i = 2, ... , n let Dj(i)J(I)" be the angles with vertex in the centroid ofrj(l), and formed by and the line linking the centroid of rj(l) with the centroid of rj(i). Figure 8 clarifies the angles we are computing. Then we let the difference Llrotation (e(rj(l)), rl (e(BI ) ) be the maximal absolute difference between correspondent angles:

u

If there is more than one orientation of rj(l) for which cross-correlation yields a maximum-e.g., a square has four such orientations-then we compute the above maximal difference for all such orientations, and take the best difference (the minimal one). We repeat the process for components 2 to n, and we select the maximum of such differences: (13)

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373

Image I

Figure 8. Representation of angles used for computing rotation similarity of component I and region rj(l).

It

j

R,

Figure 9. Sizes and distances for scale similarity computation of component I and region rj(l).

Finally, as for spatial similarity, we transform ~rotation[j] into a minimal similarity with the help of . This minimal similarity is then assigned to every simrotation(e(rj(i)), Tj(e(B i)), for i = 1, ... , n. Observe that also these differences drop to 0 when there is a perfect match, hence the similarity raises to 1. The more a region has to be rotated with reference to the other regions to match a component, the higher the rotational differences. Again, the fact that we use the worst difference to compute all rotational similarities will be exploited in the proof of Downward Refinement. 5.2.3. Scale similarity

We concentrate again on component 1 to ease the presentation. Let mj be the size of component 1, computed as the mean distance between its centroid and points on the contour. Moreover, for i = 2, ... , n, let d li be the distance between the centroid of component 1 and the centroid of component i. In the image, let Mj(l) be the size of region rj(i), and let Dj(l)j(i) be the distance between centroids of regions j (1) and j (i). Figure 9 pictures the quantities we are computing.

374

Di Sciascia et al.

We define the difference in scale between e(rj(I)) and A

'-'scale

_ max e rJ(I) ,

120

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!

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~ 1 ..11d1 ~ .I 'I ~ I

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1·1

q

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!:!~

: 120

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Res-paRse Time Isec.1

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Mod elling tech niqu es in integrat ed op erations and inform ation systems 95

--------------------------~ Capt ured networ k traffic Model descrip tion data

T raffic link

CO MN ET summary

reports

summary reports

i) ARENA 3.0 '

ii ) CO MN ET Ill '

Figure 12. Flow chart for the int egrated mo del based on ARENA 3.0 and COM NET Ill.

variables should be identified. However, in this particular investigation, we do not include those resources (such as the design department for production scheduling, etc.) that are also linked and are inte grated into the system but operate in a relatively slow manner and thu s do not significantly affect the performance of on-line production. O ther assumptions for the established integrated peBA model are summarised below : • The produ ction operates cont inuously and co nstantly witho ut breakdo wn du e to physical failures of machin es and devices on the assembly lines. • The number of available machin es or devices and th eir capacities at th e assembly lines to perform both the assembly and changeo ver activities are fixed and kn own .

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• Each work station consists of one assembly robot (i.e., SMT machin e) with input and output buffers. T here are no buffers between workst ations . Each product is assembled at least once in th e assembly schedule, wh ich was determined previously. • For communic ation networks, assume that all communicating devices (or machines) are able to communicate wit h each other properly, and each device has a suitable int erface that allows connec tion to the communication networks w itho ut physical failures. • This study does not evaluate issues co ncern ing production costs, such as costs arising from assembly, labour, changeove r, and miscellaneous thin gs. Based on the above assumptions for building an integrated model some key requirement s and steps are summarised as follows. 1. Define all the relevant equipment (or workstations) in th e real-system to be modelled, including number ofmachines and devices or workstations, number of part types and number of operations for each part, capacity of each machin e, buffers and thei r capacities at each machine, and mach inin g sequence. 2. Analyse the operati onal fun ctions of the system in order to form all approp riate input data (i.e., so- called experime ntal files) for ARENA mod els, such as physical and logical sequencing and tim ing parameters to represent the operational function for individu al items of equ ipm ent . T he timing parameters includ e loadin g/unloading times for each machin e (and/or each loadin g/ un loading station), materi al handling time s for each assembly robot , machining time s for each operation, arr ival times for each type of part , and other parameters such as batch size, conveyors' (and/ or AGVs') spee d and travelling distances. These data resourc es heavily depend on different systems to be investigated. 3. Determine how to schedule the information proc essing fun ction related to information events. The timing of information events is deri ved from the statistical analysis of th e operational simulation output (statistical results) provided by ARENA simulation m odels. 4. Analyse and extract the output of statistical data from ARENA simulations and interpret it into statistical distribution functions (SDF s) as inpu t variables with th e particular parameter values, which are required for COMNET models. T he SDF files for C O MN ET models are mainly related to the type of distribution for representing the information flow activities betw een two communication devices via the com mu nication network. M oreover, th e mo st important consid eration in choosing th e specific probability distribution with one or mo re parame ter values for the specific rando m communication beh aviour is the degree of closeness to whi ch it resembles th e real information events. O bviou sly, th e output statistical data from ARENA simulatio n mod els that represent the rand om operational aspects (or processes) will certai nly affect the probability distribution chosen for C O M N ET modelling. Moreover, the selection of the distribu tion 's parameter values is one of the mo st critical procedures, because ifit is not accurate, simulation wo rk using C O M N ET will not represent the real-system's behaviour.

Modelling techniques in integrated operations and information systems 97

5. Ensure that the logical sequence and interaction of all components and the interrelationship between the operational function and the information processing function in the system are precisely defined, thus, a complete simulation model can be implemented. Once the generic model has been verified and validated, it can be run to represent the actual (physical and logical) operations ofthe real-world system without re-building the system model for the different system's investigation scenarios, and it is also easy to add or remove components to investigate the effect of system alterations. The aim is to utilise the simulation-generated data to observe the impact on both systems' function and to assess the entire system's performance by making inferences with the system model. From this, an optimal system specification can be drawn up. 4.1.1. Operational system

The process of assembling electronic components provides a typical flexible manufacturing system, which involves complex items (part types) being produced in limited quantities. For example, there may be short-term variations in size, quantity and frequency of the lots to the system. This stochastic variation is termed 'flexibility' [25]. This type of assembly system is also a time-critical application.

• ARENA model of the PCBA system A flexible manufacturing system from the printed circuit board assembly sector represents a stochastic manufacturing scenario extremely well. Figure 13 shows a layout of the PCBA system, its graphically animated simulation model was constructed using ARENA 3.0. The system is composed of pallets, load/unload machines, pick and place machines, shifting-in devices, shifting-out devices, fixing devices, sensors, bar-code readers, stoppers, assembly robots (or so-called SMT placement machines), cell controllers, carriers and flexible conveyor systems, which are routed and controlled by Pl.C's throughout the system. To summarise the operational sequence: unprocessed components (printed circuit boards) are held in pallets for transport into the 'Enter System' and then loaded onto the loop conveyor by the loading machine at station M1. The assembled PCB's are unloaded at station M2 with a high priority given to exiting the PCBA system. Unfinished components enter another cycle until all assembly operations are complete. The sequence of operations at stations is arbitrary. The entire operation is controlled by one of the cell controllers, which interact with others at the relevant workplace locations to accomplish the individual activities. The detailed system description and its parameters are explained below: 1. The arriving unprocessed palletised PCB enters the buffer area, where a sensor senses the arrival of each palletised PCB. The sensor sends a message to notify the cell controller waiting for the decision for access to a gravity slide, which feeds the loop conveyor containing the palletised PCB's queuing for assembly. If not available, the

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98

Enter system

Carrier

o

r.. ··· . ·.. ·.. . ·. . ··... ·. ·..··........ i Fina l assembly area

,-

o

- I

I-

Accumulating conveyo rs

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;'············..·········..········..········r····_····Loop conveyor

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o

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-

-

EI II Carrier

1

t

r-..---

, ,

Exit

Suhassemhly area

.i

Finished I lousing

Type 2 H / Modul e

Figure 13. The layout of the PCBA system.

sensor activates a stopper to halt the palletised PCB and stop it moving into the slide. O nly two palletised PCB 's are allowed on the slide at anyone time to avoid damage to the circuit boards. T he time it takes to traverse the slide follows a normal distribution with a mean of 3 minutes and a standard deviation of 1 minut e. 2. O nce a palletised PCB reaches the end of the slide, it must wait for a space o n the no n-acc umulating loop con veyor that is controlled by a PLC. T he loop conveyor, whi ch has a length of 18 meters, has space for 30 circuit boards waiting for assembly. When an open space becom es available at the end of the slide, the cell controller will inform the PLC to stop the loop conveyor, and the arriving palletised PC B is loaded onto the loop conveyor at station MI . This loading pro cess requires an operation time that follows a triangular distribution with a minimum of 0.2, mo de of 0.3, and maximum of0.5 minutes, wh en the loading operation is compl eted, the loop conveyor is re-ac tivated by the PLC. 3. T he palletised PCB s then travel on the loop conveyor at a speed of 9 met ers per minute unt il they reach their required assembly lines: final assembly area for type 1 parts and the sub-assembly area for type 2 parts. Bar- cod e readers scan bar- cod e labels to identify the status of each arrivi ng PCB . PCB types arc notified to the cell controller to update their status. Th e queue in front of each assembly operation has room for two circuit boards. If the queue is full, th e palletised PCB continues around th e loop conveyor until it can enter the queue. If space is available in the queue, the

Modelling techniques in integrated operations and information systems 99

SIn (x), STop (x), SEns (x), BCR (x)

Main conveyor belt ( I)

Branch conveyor belt 2

WK (x) SOut (x), STop (x), SEns (x), BCR (x)

Robot (x)

Figure 14. Schematic layout of the peBA final assembly area.

palletised PCB is automatically diverted off the loop conveyor into the appropriate assembly system. The diversion of palletised PCBs from the loop conveyor does not cause the conveyor to stop. 4. The entire processing times at the sub-assembly area and final assembly area conform to a normal distribution with a mean of 6 minutes and 7 minutes, and a standard deviation of 1 minute and 1.5 minutes, respectively. Once the assembly operation has finished, it exits the assembly operation and enters an accumulating roller conveyor; each one is 3 meters long, and parts travel at a speed of 8 meters per minute. However, if the accumulating roller conveyor is full, the assembled parts are not permitted to leave the assembly operation, thereby blocking its operation. The bar-code readers will scan finished parts at the end of the roller conveyors and send a message to the cell controller to update their status and request transportation by one of two available carriers. The carrier (AGV) moves to the end ofthe roller conveyor; picks up the processed parts, and leaves towards its destination, which is selected according to the shortest-distance rule [28]. For an individual workstation WK (x) at the final assembly area shown in figure 14, when each palletised PCB is coming in on belt 1, if the PCB is to be processed at station WK (x) where the buffer in front ofWK (x) is not full, it will shift to belt 2x via the shift-in device SIn (x) to start the assembly operations by robots or queue in the buffer area. During the assembly, the pallet is held stationary by the fixing device.

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Other pallets behind it have to wait beh ind the shift-in devi ce until the sh iftin g process is co m pleted. The pallet m oves o n belt 2x to SOut(x) w he re th e palleti sed PCB will be shifte d back to the system co nveyo r, belt 1, with priority ove r th ose com ing from th e left o n Beltl. After definition and validatio n of the system model, eac h sim ulation was run by sim ulating 8 hours of assem bly activity w ith a 30-minute warm-up time. T hi s took appro xima tely 25-30 minutes on a 266 Mz Pc. T he result s fro m initial sim ulatio ns were used to optimise th e syste m m odel to give a co n tinuo us flow o f PCBs. In additio n, all time var iable statistica l info rmation, such as tallied frequencies of the time interval between th e arr ivals o f two palletised PCBs at eac h wo rkplace, was analysed as a reference resource to determine their statistical distr ibution func tions related to communication events. This was used as input data to the COMNET m od el and represen ts the traffi c reso urce required to handle th e peBA co m m u nicatio n traffic. 4. 1.2 . b!{tmllatiofl processing system

Wi thin th e rCBA system, the co m m u nication network (LA N) m ust be able to provide a m ech anism for co m m u n icatio n and sync hro n isatio n among several workstations (ro bo ts, etc.) wo rking together to accom plish assem bly ope ratio ns without system 's failure du e to network problems (such as overloa d et c.).

• COMNET model of P CBA system Figure 15 shows th e establishe d COM NET model for th e PCBA co m m un icatio n syste m th at was de sign ed for th e PCBA system. T here are 93 co m m u n icating dev ices th at are co nnected to a single local area net work (i.e ., LAN). All the se d evices have a suitable interfa ce that allows co nnec tio n to th e lo cal area network w itho ut ph ysical problems. The network co m m u n icatio ns link into : load /unload machines at statio ns M 1 and M2, shifting-ou t devices, shifting- in dev ices, fixing devices, con veyor system s, sensors, bar- code readers , stopper s, cell-cont ro llers, assembly ro bo ts, PLCs and a central co ntrol- level system (PC) . Their fun cti ons have been de scr ibed in section 4.1.1. C O M N ET sim ulation m odels for larg e ne twor ks often divid e the traffic into two typ es: foreg ro u nd traffi c and background traffi c. Foregrou nd traffic represents detailed models of applications and th eir prot ocols , an d backgro und traffi c represents the existin g ut ilisation that compete s with th e foreg ro und traffic. Suc h models require a m ech anism for modellin g back ground or baseline loading of th e network . Often , th is load is know n onl y from a m easur ement of utilis ation on th e link without any informati on as to th e nature o f that traffic, M odelling background traffic w ith me ssage so urces is often impractical becau se th e size of the network requires to o many message so urces to be co nfigure d, and th e m essage sources th emselves require ent ry of m any detailed attr ibute s. It is very co m m o n th at most of the above det ails on background traffic are un know n or th at the o nly informati on known abo u t the traffic is the u tilisation th at is pr esent on th e individu al links in th e netw ork. Ther efor e, th e C O M NET

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observed that the maximum message delay is greatly affected by th e maximum message size th at is transmitted between network devices, the affect is significantly different for the different LAN protocols'. As shown in figures 25 and 26, although the same higher maximum message size results in a higher message delay for the different LAN prot ocols, the magnitude of th e gap between corresponding maximum message delays for different protocols is significantly widened w hen increasing the maximum message size in the network. For instance, for a maximum message size of 50 Kbytes, the maximum message delay for the token bus LAN is 241 ms with a channel utilisation of 35.89%, compared

Modelling techniques in integrated operations and information systems 117

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to a maximum message delay of 1616 ms for the CSMA/CD LAN with a channel utilisation of 36.36%. This is further evidence that at the same network load, the token bus LAN has superior performance to the CSMA/CD LAN for the PCBA communication system. This benefit becomes significant when the LAN is heavily loaded. Furthermore, applying the integrated simulation model of the PCBA system enables designers to make a comparison between the maximum message delay obtained from the COMNET simulation results and minimum tallied machine processing time. From figures 22 and 23 and based on COMNET text-based simulation reports, it shows that at the same maximum message size of 50 Kbytes, the maximum message delay is 4748 ms (89.28% busy) and 927 ms (59.67'/\, busy) for the token bus LAN at 2 mbps (not shown in figure 23) and 3 mbps respectively; and 42701 ms (91.04% busy) and 3776 ms (61,16% busy) for the CSMA/CD LAN at 2 mbps (not shown in figure 23) and 3 mbps respectively. By inspection ofthe minimum machine processing times shown in table 3, it can be seen that a LAN with a transmission rate of over 3 mbps for both the token bus LAN and CSMA/CD LAN will guarantee that maximum message delay will be less than the shortest workstation (machine) processing time. This ensures all facilities have sufficient time to access to the network during the PCB assembly process. Moreover, an analysis based on the simulation results concludes that a CSMA/CD LAN with a transmission rate between 5 mbps and 10 mbps has a maximum message delay from 1499 ms to 233 rns, corresponding to a channel utilisation of 36% and 18% respectively, these delays are relatively small, hence performance is reasonable. For a token bus LAN, a transmission rate between 3 mbps and 5 mbps is fast enough to undertake communication duties, and a transmission rate ofmore than 10 mbps leads to a very small maximum message delay for both LANs. Within this range, the simulation results show no data lost during the transmission across the network. Therefore, it was

118

Q. Wang, C. R. Chatwin, and R. C. D. Young

Table 5 Token passing-based protocols investigated for implementation of the PCBA network Protocol standard: IEEE 802.4 and IEEE 802.5 Maximum message size (50 KB) Data rates (mpbs) I 3 5 10

15 20

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tResults in first 2675 seconds simulation time tt Results in first 2160 seconds simulation time

finally suggested that the PCBA CSMA/CD LAN or the PCBA token passing bus LAN with any transmission rate ranging from 5 mbps to 10 mbps would certainly guarantee the operation without failure of the PCBA manufacturing communication system (this is for a maximum message size of 50 Kbytes in the PCBA network). Since the operation of token bus and token ring is similar [45], the simulation result for token ring LAN shown in table 5 is extremely close to the result for token bus, hence, the discussion relating to token bus also applies to token ring, though the token ring is not physically suitable for the PCBA communication system. 6. DISCUSSION AND CONCLUSION

As outlined and discussed in section 1, for increasingly highly automated computercontrolled manufacturing systems, successful integration of manufacturing devices and automated equipment using existing communication protocols and networks is crucial to achieve the desired, cost effective, co-ordinated functionality required for CIM systems. As a result, the performance ofcommunication networks has become a key factor for successful implementation ofintegrated manufacturing systems, particularly, for time-critical applications. Hence, the design and evaluation of manufacturing systems can no longer ignore the performance of the communication environment or conduct a separate investigation without considering the performance ofthe operational system. Section 5 presented an assessment of the operational system's aspect for the PCBA system to ensure that the system has no fatal bottlenecks and weaknesses in system operations. It addressed four issues presented in sections 5.2.1 and 5.2.2 respectively, which discussed the impact on logical interactions and interrelationships between operations and information processing systems within the PCBA environment and determined the relative performance merits of the three IEEE 802 standard networks in which the token bus LAN performs best when implemented for the PCBA communication system. The outcome also shows that token bus is better suited to process control applications (since they are time-critical applications) than the CSMA/CD protocol network, which is well suited to standard computer network applications, where the network loading rarely exceeds 8-17%.

Modelling techniques in integrated operations and information systems 119

A comprehensive review of the current literature reveals the lack of a feasible and practical modelling and simulation method or means that has the ability to investigate manufacturing systems by taking both aspects into account. In fact, there is no single conceptual modelling method or tool available, which can completely model a manufacturing system and easily describe most of its sub-systems due to the high level of complexity of manufacturing systems. It is generally accepted that traditional planning methods and mathematical/analytical modelling techniques are not appropriate to deal with complex manufacturing systems. Nevertheless, manufacturing system's analysts, designers and their clients have an increasingly important requirement for a 'full' system evaluation (particularly for investigation of a highly integrated time-critical manufacturing systems), which will model the basic manufacturing operations and combine the effect of the communication systems. Therefore, the aim of the research reported herein was to focus on: The development of an integrated method, in which both the operations and information systems within a manufacturing system could be examined concurrently using the currently developed simulation tools and techniques so that the relevant impact on logical interactions and interrelationships between them could be determined. Moreover, this technique should be implemented based on a real system to test thefeasibility of this approach becoming a strategic planning tool for systems analysts and designers to quickly provide a visible preview of the integrated system peiformance at an early stage ill the design process.

The major work of this treatise is to present a methodology that has been developed to examine a manufacturing system by the modelling and simulation of its integrated operational systems and information systems. This approach has been implemented on a relatively complex flexible manufacturing system: a printed circuit board assembly (i.e., PCBA) line; in order to determine its feasibility and capability. The key features of this technique has been demonstrated by analysing and comparing various simulation results (in terms of graphs and tables) that were generated by the established integrated model of the PCBA system using the two powerful simulation-packages that were specially selected for use in this integrated domain. The research has shown that applying this integrated method allows system designers and analysts to comprehensively predict system behaviour in order to obtain an optimal solution that maximises systems performance. The integrated model can allow users to see the impact on logical interactions and interrelationships between operations and information processing systems within a manufacturing environment so that they can make design judgements that satisfy systems' and production requirements. From this, an optimal system specification can be drawn up. The research has shown that this approach contributes a useful basis for developing existing modelling frameworks and a practical means of exploring existing modelling simulation methodologies. The research indicates that in principle, this technique is valuable for analysing a wide range of manufacturing systems (CIM systems, FMSs, process control systems, etc.). Finally, the concept of economic performance control came into being during the 1970s petroleum crisis when industrial circles realised that process control systems

120

Q. Wang, C. R. Chatwin, and R. C. D. Young

that excluded economic variables were not guaranteed to benefit enterprise economic planning. To avoid this difficulty, economic variables must be selected as the ultimate control variables of the control system, and specific costs and market information must be taken as the input that disturbs the control system. However, some of the economic variables are not measurable on-line; therefore, model prediction may be used to generate data for them, but model reliability and system stability are difficult problems. It is wise at present to develop process economic performance display (rather than control) software for industry. This will yield manufacturing profitability with lower economic risk. For example, the economic variable to be displayed for a chemical plant may be instantaneous profit IP: IP = SP - PC

where, SP is selling price; PC is production costs, and most components of PC are measurable on-line. It is widely accepted that in general the economic performance of an enterprise is a function of 8 Ms: 1. Man (Personnel and manpower) 2. Machine (Equipment) 3. Material (including energy) 4. Money (floating capital) 5. Market 6. Method 7. Moment (time) 8. Message (information). Obviously, the objective of the enterprise is to maximise profit. Therefore, a good system model should optimise response to the above variable. A first step to take is to make available not only technical data, but also instantaneous information on the economic performance of the enterprise concerned, without which decision making is often misguided. P.S. This work may match the following subject areas:

• New computer technology for enhanced factory modelling and visualisation • Integration of design with manufacturing planning • Process modelling in an integrated design and manufacturing environment • Optimisation techniques for factory design • Advances in discrete event simulation • Enterprise resource planning Keywords:

Manufacturing systems, computer networks, modelling and simulation, integration, FMS, CIM.

Modelling techniques in integrated operations and information systems 121

REFERENCES

[1] Groover M. P.,2000. Automation, production systems, andcomputer integrated manufacturing (Prentice-Hall, Inc.). [2] Wong W. M. R., 1993. Modelling andsimulation ofthecommunicatio11 protocols usedin typical CIM equipment. Bradford University. [3J Mansharamani R., 1997. An overview of discrete eventsimulation methodologies andimplementation. Sadhana, Vo1.22, Part 5,611-627. [4] McCarthy I., Frizelle G., Efstathiou j., 1998. Manuf{,cturing complexity network meeting, University of Oxford. EPSRC engineering and physical science research council. [5] Chou Y. C, 1999. Configuration desion of complex intecrated manufacturing systems. lnternational [ournalof Advanced Mallllfacturing Technology, 15:907-913. [6] AL-Ahmari A. M. A., Ridway K., 1999. An integrated modelling method to support manufacturing system analysis and desion. Computers in Industry, 38 (1999), 225~238. [71 O'Kane j. E, Spencekley j. R., Taylor R., 2000. Simulation as an essential tool loradvanced manuiacturino technology problems. Journal of Materials Processing Technology, 107 (2000), 412-424. [8] Kim C H., Weston R., 2001. Development of an integrated methodology for enterprise engineering. InternationalJournal of Computer Integrated Manufacturing, 14 (5), 473-488. [9] Balduzzi E, Giua A., Seatzu C, 2001. Modelling and simulation of manufacturing systems with first-order hybrid Petri nets. International Journal of Production Research, 39 (2),255-282. (10] Cunha P. E, Dionisio J., 2002. An architecture to support the manufacturing system desion and planning. Proceedings of the 1st CIRP(UK) Seminar on Digital Enterprise Technology, Durham, UK, 129134. [11J Bernard A., Perry N., 2002. Fundamental concepts of product Itechnology Iprocess inionnationai inteoration for process modelling andprocess planning. Proceedings of the 1st CIRP(UK) Seminar on Digital Enterprise Technology, Durham, UK, 237-240. [12] Cantamessa M., Fichera S., 2002. Process and production planning ill manuiacturino enterprise networks. Proceedings of the 1st CIRP(UK) Seminar on Digital Enterprise Technology, Durham, UK, 187190. [13] Higginbottom G. N., 1998. Performance evaluation ofcommunication networks (Norwood: Artech House, Inc.). [14] Mitchell E H., 1991. CIM systems (Prientice-hall Ltd.). [15] Colquhoun G., Baines R., Crossley R., 1993. A state of the art review of IDEFO. International Journal of Computer Integrated Manufacturing, 6 (1993), 252-264. [16] Doumeingts G., Vallespir B., 1995. Methodologies for designing CIM system: a survey. Computers in Industry, 25 (1995), 263-280. [17] AL-Ahmari A. M. A., Ridway K., 1997. Computerised methodoloyiesior modelling computer integrated manufacturing systems. Proceedings of 32nd International MATADOR conference, Manchester, 111116. [18] Chryssolouris G., Anifantis N., Karagianis S., 1998. An approach to thedynamic modelling of manufacturing systems. International Journal of Production Research, 38 (90), 475-483. [19] Baines T. S., Harrison 0. K., 1999. An opportunity for system dynamics in manufacturing system modelling. Production Planning & Control, 10 (6), 542-552. [20] Perera T., Liyanage K., 2000. Methodologyfor rapid identification andcollection of input data in thesimulation of manufacturing systems. Simulation Practice and Theory, 646-656. [21] Borenstein D., 2000. Implementation of all object-oriented tool for the simulation of manufacturing systems and its application to study the effects ofjlexibility. International Journal of Production Research, 38 (9) 2125~2142.

[22] Wang Q., Chatwin C R. et aI., 2002. Modelling and simulation of integrated operations and information systems in manufacturing CA' rating awarded), The International Journal of Advanced Manufacturing Technology, Vol. 19, pp. 142-150. [23] Wang Q., Chatwin C R. et al. Comparative dynamic performance of token passing and CSMAICD LANs for a.flexible manuiacturino system, The InternationalJournal of Computer Integrated Manufacturing, in press. [24] Wang Q., Geha A., Chatwin C R. et aI., 2002. Computer enhanced network design for time-critical integrated manufacturing plants, 1stCIRP (UK) International Seminar on 'Digital Enterprise Technology' (DET02), Proceedings of the 1stCIRP(UK) Seminar on Digital Enterprise Technology, Durham, UK, pp. 251-254.

122 Q. Wang, C R . Chatwin, and R . C D. Young

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Gastaldi M ., Levialdi N. , 1996. DYllamic analysis of tbe peif CA Col l e c t Da t a 199 7/ 7/1 1 998 /1 / 1< / a r gume n t : t i meEnd > As s oc i a t i on Tr a n s a c t i on Java http s://iskmlab.im.ncnu .edu.tw/DataClooect.jsp f i pa - r d f O

2.3.3. The agent conversatioll policy

In most agent- based systems, communication between agents takes through the use of messages. A message consists of a packet of information, usually sent asynchronously.

Applying intelligent agen t-based suppor t systems in agile business processes 89

The message type is represent ed by a verb corresponding to some kind ofillocutionary act (e.g., request, inform). Agents in the context of conversations exchange messages. A conversation is a sequence of message betwe en two agents, taking place over a period of time that may be arbitrarily long, yet is bounded by certain termination conditions for any given occur rence. Co nversations may give rise to other conversations as appropriate. Each message is part of an extensible prot ocol-consisting of both message names and conversation policies (CPs) (also called pattern or rules) common to the agents participating in the conversation. The con tent porti on of a message encapsulates any semantic or procedural elements independent of the conversation policy itself [53]. Based on the notion of patterns, a conversation schema (schema, for short) -based meth od for specifying C Ps is present ed. A conversation schema is defined as a pattern of conversation interactions specifying CPs centered on one or more conversation topics. Topics are extracted from application dom ain at the domain analysis stage. A goal-directed schema is a schema in whi ch the pattern of interaction is directed towards achieving a specified goal of participating agent(s) [54]. According to [54], there are four advantages in using scheme-based conversation policy: (1) It ensures consistency and effectiveness of the agent conversation by considering sub- task constraints. (2) It reduces communication transaction by incorporating with CM(s) (Co nversation Managers) that can qui ckly determine for the participating agents what to do instead of resortin g to lengthy reasonin g by them . (3) It decreases the complexity of impleme ntation by construc ting C M(s) that separate the description of commo n agents' functionality from that of communication and synchronization, to ensure local and global coherency. (4) It enhances the reusability of software components. Domain-independent and domain-specific conversation knowledge are organized and formulated into hierarchies of conversation schema classes using objec t-oriented methodologies. For these reasons, a schema-based approach is used to specify conversation policies in the agent-based system. T he five steps of the schema-based conversation pro cesses are presented in Figure 11 based on th e work of Lin and N orr ie [54]: Step 1. Define the conversation topics. Step 2. Define the conversation schemata Step 3. Use Coloured Petri Nets (CPNs) to check if there is deadlock or livelock. If deadlock occurs, ident ify it. Step 4. C reate "If-then" rules based on C PNs. Step S. Generate j ava thread classes based on the " If-then" rules

Step 1: The main target of the first step is to identify conversation topics. A conversation typically focuses on one or more "topics" each associated with task-r elated information. A topic can be describ ed by a set of variables, which have values to be agreed upon by the agents involved and have constraints to be satisfied by other agents

90

Chun-Che Huang

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or users. Conversation topics, denoted by TP, can be described by TP = (TP _ID, ARGUMENTS), where TP_ID is the identification of a conversation topics and ARGUMENT lists all arguments of the topics. Table 1 illustrates the TP of the agent-based system.

Step 2: The second step identifies the schema. Figure 10 and 12 present the process and schema, respectively. In Figure 12, "Name" represents the schema's name and "Task" describes the purpose of this conversation. In this example, the purpose of this conversation is to analyze the data from various data sources. The "Agent-type" corresponds to the agents involved in this conversation. In the example, three agents, AA, CA and fA involve in the conversation. The "Status" describes the status, which occurs in this conversation. Step 3: After defining the schema, it is described by CPNs (Coloured Petri Nets). The CPNs provide a framework for the design, specification, validation, and verification of agentbased systems [55]. The advantages of this approach is that it not only represents the information being exchanged among agents, but also describes their internal states, thus describing the conversation process in much more detail [53]. In this section, CPNs

Applying intelligent agent-based support systems in agile business processes

91

Table 1 The TP in the agent-based system Topics

Description

Arguments

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The process of analyzing data.

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Figure 12. The schema corresponding to the process of data analysis.

are used to check if there are deadlock or livelock in the agent-based system. The sub-schemata of schema are represented as transitions of CPN. Its states are described by places with tokens holding structured messages. Relation Flows are represented as preconditions and post-conditions in the forms of arc expressions. The following steps present the construction process of schemata [20]: Step 3.1. Identify agent types, attributes and state variables of the schema according to the topics. Step 3.2. For every agent type, add the transitions for communicative acts or subschemata, and represent the actions performed by the same agent, which are aligned horizontally. Step 3.3. Add the places and flow expressions between the transitions and connect them. Step 3.4. Add the information exchange represented by collective state places that occur among the agents for the topics. Step 3.5. Establish an external interface. Figure 13 illustrates the CPN, colors, and variables used in the "Analysis.Data" schema. The derived CPN representation of schemata allows the verification for logical consistency, completeness, and absence of deadlock and livelock. The simulation technique can be used for verification [56].

92 Chun-Che Huang

transmon: T I={Request_AA} T2={TimeouCAA} n ={RECEIVE MESSAGE } T4={SendMessages } T5={Timcout_CA} T6={Replt IA} Type (color set) definition for "Analysis_Data" Schema Color ltem=Strung; Color Analysis_Method= List item; Color Information=String; Color Sourcc_Data= String; ColoragenU D= String; Color Bc~Timc= Year] Month] Day Color End_Time= Year] Month] Day Color Ycar= Int; Color Month= Int; Color Day= Int; agt= agent; Figure 13. The ePN, colors, and variables.

Step 4: After verification, each conversation schema is converted to a set of rules. Each 'place(s)-> transition' of individual agents participating in the conversation in a CPN corresponds to the "condition" part of a rule. Every 'transition -> place(s)' in a CPN corresponds to the "action" part of a rule [54]. Figure 14 is an example ofrule creation, where the rules initiate agents CA and fA to process their jobs while agent AA is ready. Step 5: A set ofjava classes can be implemented based on these rules created in Step 4. When a set ofjava classes are created, the Conversation Manager (CM) can be formed based on these classes. A conversation manager, in a traditional agent communication approach, is "pointto-point", "multi-cast" or "broadcast" manager. Each of them communicates directly with each other. Lin and Norrie [54] proposed an agent conversation architecture called"conversation manager." In this architecture, a group of agents work together in a cooperation area. Each agent in a cooperation area routes all its outgoing messages through a local CM (Conversation Manager). All incoming messages are received from CM as well. The architecture is used in the ABKM system.

Applying intelligent agent-based support systems in agile business processes 93

Rule I (for Request in A) If (Color(Ready_A,agt) = AA) then begin setValue(waiting, time) =5; setValue (requisted)=getValue(Ready_A); sendMessage (CA,Analysis_Method,Be~Time.End, Time); end Rule 2 (for Request in B) If (Color (Requested_AB,agt)=AA) then begin setValue(waiting, time) =5; Source_Data=getData (AnalysiaMethod.Beg,Time,End_Time); sendMessage (IA,Analysis_Method,Beg_Time,End_Time,Source_Data); end

Figure 14. The rule creation.

The Components of CM are defined as the following [54]: • IE (Inference Engine): It utilizes a load balancing mechanism, which allows a message to be forwarded to a new conversation. • ANS (Agent Naming Sub-system): It is used to recognize the process from agent class to agent instance, for the registered agent stored in a yellow page (YP). • ASP (Active Schema Pool): It stores all acting schemata. It has a particular size called "threshold," which is set for performance based on certain criteria. • EE (CPN Execution Engine): The schema is executed by EE. • Schemata Library: It consists of a set of schema thread classes, which comprise the template to construct schema instance. • I/O: Message input/output module Figure 15 describes the structure of conversation manager based on Lin and Norrie [54]. In the CM architecture, the incoming messages come from other CM(s). When the I/O module receives messages, it sends them to IE. IE detects the message if the size ofASP reaches its threshold. If it is below the threshold, the CM selects an appropriate schema class from the schema library and creates an instance of the class and adds it to the ASP. The EE analyzes messages, recognizes current situation and states, creates the rules based on schema, and sends an instruction to appropriate agents about the topics. The topics rely on the current state. 3. AGENT-BASED OBJECT-ORIENTED DESIGN PROCESSES

A complex design is often carried out through collaborative works. Multi-agent systems are particularly suitable for supporting collaborative work. To achieve the goals of product development, each service required by a customer is carried out with a design process. Such design process considers five functions: planning, producing, distributing,

94

Chun-Che Huang

IE

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displaying, and acquiring of objects related to the customer service. The design process (DP) requires the following basic features (adapted from [57] and [58]): • The process is essentially of a non-hierarchical control in character. • The process is highly decentralized, with each element of the process operating quasiindependently. • The process is self-managing, in that each element ofthe process will adjust dynamically. • The process is scaleable, in that elements of the process can be added to the network without changing essential characteristics. • The process is ifficient, in that the element of the model is handled in a systematic way in coding or computing. Paramount among the challenges offuture decision-making in the DP is the development and delivery of decision support technologies that are responsive and portable to ever changing and distributive decision-making situations [59]. One promising approach is that of object-oriented design based on agent technology-via the World Wide Web (WWW): In this section, an Object-Oriented (00) approach, Design with Objects (DwO) is used in the agent body that solves the problems of design process. The DwO approach is implemented in the intelligent object-oriented agent (IOOA) package.

Applying intelligent agent-based support systems in agile business processes 95

~~~ ~~ d ;;h d PC

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Figure 16. Overall architecture of an 00 approach via the WWW

Decision-makers or clients use the browsers to input their requirements and run the rOOA package through the World Wide Web (WWW), regardless of what platforms are used (Figure 16). The WWW is potentially useful for remote decision making because it allows the disparate functions that are involved in remote decision making to share data relatively easily. 3.1. An object-oriented approach

Sub-section 3.1.1 describes the concept of the DwO approach. This approach is then briefly presented in Sub-section 3.1.2. This approach can be extended to apply to modular software, which is presented in Sub-sections 3.1.3 and 3.1.4. 3. 1.1. The concept of a design object

The DwO approach views design objects as being objects that represent not only physical entities, such as parts or components, but also non-physical entities, such as the design history or vendor information. Objects can have the following features: Methods, Data, and Inteface. Stipulating the above view of a design object, the relationship between design objects can be established by using the following object operators: Inheritance, Import and Message passing. Regardless of the above, before the DwO becomes more widespread, there are still, as yet, several research issues that must be addressed. The main research issues associated with DwO can be divided into two segments: those associated with the identification oj objects and the design oj objects.

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The area of identification of objects is concerned with identifying the object elements with the granularity of the objects being an important consideration. There are several research issues concerned with the management of objects. The first is concerned with the object taxonomy and with the content of the object library. As the size of an object library is limited by various factors, selecting an appropriate set of objects is a research issue. A second research issue is associated with the organization of the objects to allow for their efficient use. An additional research issue is associated with the user-defined objects. User-defined objects would allow for a more flexible framework but present problems regarding validation and links with other objects. The area of design if objects is concerned with the design of individual object to best satisfy the requirements such as cost, size, reliability, and quality. As indicated above, one main advantage of the DwO approach is that the design of individual object can be carried out in a relatively autonomous manner. The remainder of this section is focused on the area of design with objects (DwO), which is centered on the need to use a combination of standard objects in order to satisfy functional requirements. Such requirements are the "demands" and "wishes" that clarify the design task in the space of needs [60]. They provide an abstraction of the design task from the most general demand (the overall requirement) to more specific demands (sub-requirements). Requirements and functions are domain specific and represent part of the knowledge base of the design system. In theory, it is possible to decompose functions so that the lowest level of the function structure consists exclusively of functions that cannot be sub-divided further, whilst remaining generally applicable. Each function may then be satisfied by one object and each function may correspond to one requirement. 3. 1.2. A design process model formalism for DwO

This section describes a Design with Objects (DwO) approach that can lead to develop a process model for the design process problem. The model aims to show how design processes can be carried out using IOOA framework. The central design process inherent in DwO can be represented as the architecture shown diagrammatically in Figure 17, with five main types of objects involved: namely design models (DS), design objects (DO), design algorithms (DA), requirements and constraints (RC), and the evaluation schema (ES). Three object operators-inherit, import, and messagepassing express the relationship between these objects. The architecture in Figure 17 exhibits how the particular instance of a design model, ds, is obtained from the design algorithm, evaluation schema, requirements, constraints and the design model object. In this model, DwO views the design model (DS) as a central element of design. DS is the representation of an artifact to be designed and represents a model that consists of one or more design objects, do" that can be physical or non-physical objects. From a DwO perspective:

os =

[doj,{z)]

Where z is the location of the item and is a variable of the form of a numeric, symbolic and/or Boolean data type.

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