Model-Based Software and Data Integration (Communications in Computer and Information Science, 8)

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Communications in Computer and Information Science

8

Ralf-Detlef Kutsche Nikola Milanovic (Eds.)

Model-Based Software and Data Integration First International Workshop, MBSDI 2008 Berlin, Germany, April 1-3, 2008 Proceedings

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Volume Editors Ralf-Detlef Kutsche Nikola Milanovic Technische Universität Berlin Fakultät IV - Elektrotechnik und Informatik Computergestützte Informationssysteme CIS Einsteinufer 17, 10587 Berlin, Germany E-mail: {rkutsche, nmilanov}@cs.tu-berlin.de

Library of Congress Control Number: 2008923752 CR Subject Classification (1998): H.3, H.2, H.4, C.2.4, I.2, J.1 ISSN ISBN-10 ISBN-13

1865-0929 3-540-78998-7 Springer Berlin Heidelberg New York 978-3-540-78998-7 Springer Berlin Heidelberg New York

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2008 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 12251551 06/3180 543210

Preface

The First International Workshop on Model-Based Software and Data Integration (MBSDI 2008), was our first event of this kind in a forthcoming series of activities at TU Berlin, where a scientific discussion and exchange forum was provided for both academic and industrial researchers. We aimed at researchers, engineers and practitioners who focus on advanced, model-based solutions in the area of software and information integration and interoperability. As with every beginning, the resonance on our calls in today’s overflooding of workshops was somewhat unpredictable, and we did not really know how many paper submissions to expect. We were nicely surprised, considering the rather short lead time to organize the meeting and the very specialized and focused topic. After the rigorous review process, where each paper received at least four reviews, we were able to accept nine regular papers and, additionally, we asked for extended abstracts from our invited speakers. The selected papers mirror the main aspect and the mission of the workshop: to promote research in the field of model-based software engineering, essentially focusing on methodologies for data and software (component) integration. Integration of data from heterogeneous distributed sources, and at the same time integration of software components and systems, in order to achieve full interoperability is one of the major challenges and research areas in the software industry today. It is also the major IT cost-driving factor. During the past few years, the relevance of “model based” approaches to these extremely time-and money-consuming integration tasks has come into the special focus of software engineering methods. OMG’s keyword model-driven architecture (MDA) has brought model-based approaches into the wide observation of the software industry and science. On the other hand, a strong community centered around the service-oriented architecture (SOA) paradigm and service science in general has covered significant grounds in defining interoperability standards in the area of communication, discovery, description and binding, as well as business process modeling and processing. The two communities have remained largely isolated, resulting in MDA concepts not being broadly applied to system integration. Our workshop addressed this issue. The selection of papers tried to introduce a strict model-based design, verification, development and evolution methodologies to system integration concepts, such as SOA. Through our three thematic paper sessions – Data Integration; Software Architectures, Services and Migration; and Model-Based and Semantic Approaches – we tried to offer a roadmap and the vision of a new methodology. We had a distinguished keynote speaker, Bran Selic from IBM Rational, whose work has extensively contributed to the very definition of model-driven development methods and tools, as well as to the definition of unified modeling

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Preface

language (UML). He presented a talk “Key Technical and Cultural Challenges for Model-Based Software Engineering,” in which he identified short- and longterm research problems that have to be resolved to facilitate faster adoption of model-based software engineering methods. We also had three prominent invited speakers, Miroslaw Malek (Humboldt University Berlin), who shared his views on the art of creating and integrating models, Stefan Tai (University of Karlsruhe), who proposed service science as an interdisciplinary approach for service modeling and Volker Markl (IBM Almaden Research Center), who presented the data mashup project. The workshop, in the context of a German regional initiative of collaborative development of methodologies and tools for “Model-Based Software and Data Integration,” a joint effort of software SMEs and science under the acronym BIZYCLE, was a part of the Berlin Software Integration Week 2008. Besides MBSDI 2008, it featured a one-day industrial forum, where problems were discussed and solutions proposed and demonstrated in the area of software interoperability and integration. This forum addressed several industrial sectors, such as production and logistics, health, facility management and publishing. The Berlin Software Integration Week 2008 presented a unique opportunity for knowledge and technology transfer between industrial practitioners and academic researchers. We would wholeheartedly like to thank to all the people that made MBSDI 2008 possible, first of all, our Program Committee members, for their guidance and diligent review process which enabled us to select an exciting program. We would also like to thank our industrial partners from the BIZYCLE consortium, for their support in understanding integration problems in different industrial contexts. The event would not have been possible without the support and the grant given by the Federal Ministry of Education and Research (BMBF), and its subordinated project management agency PTJ. Finally, our thanks go to the local organizers, members of the CIS group at the Technical University of Berlin (especially Mario Cartsburg and Timo Baum) and Katja Baumheier of Baumheier Eventmanagement GbR. We hope that the attendees enjoyed the final scientific program and the industry symposium, got interesting insights from the presentations, got involved in the discussions, struck up new friendships and got inspired to contribute to MBSDI 2009! April 2008

Ralf-Detlef Kutsche Nikola Milanovic

Organization

MBSDI 2008 was organized by the Berlin University of Technology, Institute for Software Engineering and Theoretical Computer Science, research group Computation and Information Structures (CIS), in cooperation with the BIZYCLE consortium industrial partners and German Federal Ministry of Education and Research. Our Program Committee was formed by 26 members, many of them from Germany, but many of them from universities and research institutions all over the world. Thanks to all of them for their engagement and their critical reviewing work.

Program Committee Co-chair Co-chair

Ralf-Detlef Kutsche (TU Berlin, Germany) Nikola Milanovic (TU Berlin, Germany)

Referees

Roberto Baldoni (University of Rome, Italy) Andreas Billig (University of Joenkoeping, Sweden) Susanne Busse (TU Berlin, Germany) Tru Hoang Cao (HCMUT, Vietnam) Fabio Casati (University of Trento, Italy) Stefan Conrad (University of Duesseldorf, Germany) Bich-Thuy T. Dong (HCMUNS, Vietnam) Michael Goedicke (University of Duisburg-Essen, Germany) Martin Grosse-Rhode (Fraunhofer ISST, Germany) Oliver Guenther (HU Berlin, Germany) Willi Hasselbring (University of Oldenburg, Germany) Maritta Heisel (University of Duisburg-Essen Germany) Arno Jacobsen (University of Toronto, Canada) Andreas Leicher (Carmeq GmbH, Germany) Michael Loewe (FHDW, Germany) Aad van Moorsel (University of Newcastle, UK) Felix Naumann (HPI, Germany) Andreas Polze (HPI, Germany) Ralf Reussner (University of Karlsruhe, Germany) Kurt Sandkuhl (University of Joenkoeping, Sweden) Alexander Smirnov (SPIIRAS, Russia) Stefan Tai (IBM Yorktown Heigths, USA) Gerrit Tamm (HU Berlin, Germany) Bernhard Thalheim (University of Kiel, Germany) Gregor Wolf (Klopotek AG, Germany) Katinka Wolter (HU Berlin, Germany) Uwe Zdun (TU Wien, Austria)

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Organization

BIZYCLE “Entrepreneurial Regions” Context of MBSDI 2008 The workshop series MBSDI in its first edition in 2008 was born out of the project context of the Innovation Initiative “Entrepreneurial Regions” set up by the German Federal Ministry of Education and Research (BMBF). This initiative, particularly its part program “Innovative Regional Growth Cores,” stands for innovation-oriented regional alliances which develop the region’s identified core competencies to clusters on a high level and with strict market orientation. BMBF has systematically developed a series of such programs for the New German Länder since 1999. BIZYCLE, the “Evolution-Oriented Technology Platform for the Integration of Enterprise Management Software” is one of the “Innovative Regional Growth Cores,” which was started in February 2007 as a joint activity of six industrial partners, SMEs in Berlin, and CIS/ TU Berlin as the academic partner of this consortium. After the first project year, it seemed appropriate to establish a long-term academic and industrial collaboration initiative in the form of a scientific conference combined with an industrial forum in the context of our focus area: Model-Based Software and Data Integration. We created the “Berlin Software Integration Week 2008” as our cover for “Model-Based Software and Data Integration 2008” and “BIZYCLE Industrial Forum 2008.” Looking forward to establishing a long-term perspective in this challenging area of research and industrial engineering, we had – besides the regularly reviewed program of MBSDI 2008 – prominent invited international speakers from academia for this part of the software integration week, as well as engaged and active entrepreneurs from our region Berlin-Brandenburg and from outside, for the BIZYCLE Industrial Forum 2008.

Sponsoring Institutions MBSDI 2008 and the BIZYCLE Industrial Forum were partially supported under grant number 03WKBB1B by the German Federal Ministry of Education and Research (BMBF).

Table of Contents

Invited Papers The Art of Creating Models and Models Integration . . . . . . . . . . . . . . . . . Miroslaw Malek

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Modeling Services – An Inter-disciplinary Perspective . . . . . . . . . . . . . . . . . Stefan Tai and Steffen Lamparter

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Data Mashups for Situational Applications . . . . . . . . . . . . . . . . . . . . . . . . . . Volker Markl, Mehmet Altinel, David Simmen, and Ashutosh Singh

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Data Integration Combining Effectiveness and Efficiency for Schema Matching Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alsayed Algergawy, Eike Schallehn, and Gunter Saake

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Model-Driven Development of Complex and Data-Intensive Integration Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matthias B¨ ohm, Dirk Habich, Wolfgang Lehner, and Uwe Wloka

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Towards a Metrics Suite for Object-Relational Mappings . . . . . . . . . . . . . . Stefan Holder, Jim Buchan, and Stephen G. MacDonell

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Software Architectures, Services and Migration View-Based Integration of Process-Driven SOA Models at Various Abstraction Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huy Tran, Uwe Zdun, and Schahram Dustdar

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Model-Driven Development of Composite Applications . . . . . . . . . . . . . . . . Susanne Patig

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Towards Identification of Migration Increments to Enable Smooth Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Niels Streekmann and Wilhelm Hasselbring

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Model-Based and Semantic Approaches Service-Based Architecture for Ontology-Driven Information Integration in Dynamic Logistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Smirnov, T. Levashova, N. Shilov, and A. Kashevnik

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State of the Art on Topic Map Building Approaches . . . . . . . . . . . . . . . . . . Nebrasse Ellouze, Mohamed Ben Ahmed, and Elisabeth Métais

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Construction of Consistent Models in Model-Driven Software Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gabriele Taentzer

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Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The Art of Creating Models and Models Integration Miroslaw Malek Humboldt-Universität zu Berlin Unter den Linden 6, 10099 Berlin

The sciences do not try to explain, they hardly even try to interpret, they mainly make models. By a model is meant a mathematical construct which, with the addition of certain verbal interpretations, describes observed phenomena. The justification of such a mathematical construct is solely and precisely that it is expected to work. John von Neumann (1903 - 1957)

1 Introduction The art of abstracting from physical or virtual objects and behaviors for the development of system models is critical to a variety of applications ranging from business processes to computers and communication. As computer and communication systems begin to pervade all walks of lives the question of design, performance and dependability evaluation of such systems proves to be increasingly important. Today's challenge is to develop models that can, not only give a qualitative understanding of ever more complex and diverse phenomena and systems, but can also aid in a quantitative assessment of functional and non-functional properties. System modeling should help to understand the functionality and properties of the system and models are used for development and communication with other developers and users [1]. Different models present the system from different perspectives • • • •

Structural perspective describing the system organization or data structure Behavioral perspective showing the behavior of the system Hierarchical perspective depicting the system’s hierarchical organization to cope with complexity External perspective reflecting the system’s context or environment.

Good models: speak to imagination, are easy to visualize and remember. A beautiful example is a dining philosophers problem in which the problem of concurrency can easily be understood. Good models address the problem at hand and provide useful, preferably quantifiable insight. Leading role and popularity of physics is to a large extent due to models that general public can grasp such as atomic model. Computer scientists, especially software engineers claim to be able to solve almost all specified problems. On the other hand physicists deal with physical world and model the world around us. Software engineers create artifacts and try to solve most problems while neglecting limits and frequently lacking understanding. Computer hardware is mainly R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 1–7, 2008. © Springer-Verlag Berlin Heidelberg 2008

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designed by professionals while software is written by a broad spectrum of population ranging from experts to dilettantes. This state of affairs poses a number of challenges. In this paper we give first a historical perspective how we have got to the state that we are in at present and point out major problems and challenges with the state-of-the-art models in general as well as their integration by using a specific example of a model for failure prediction.

2 Historical Perspective Modeling was around since the beginning of times. The first traceable abstractions of reality were numbers and this process dates back to the beginning of mankind [2]. Astronomy and architecture were next areas where models were used since about 4,000 BC. Mathematical models have spread over Babylon, Egypt and India over 4,000 years ago. With Thales of Miletus (circa 600 BC) the geometry became a useful tool in reflecting and analyzing reality and since then many branches of mathematics and corresponding models have flourished and proved their use. But it was not until Euler in 18th century when graph theory was formalized and gave rise to a plethora of mathematical models reflecting communication patterns and networks, which gave impetus to research in a large number of areas ranging from psychology to electrical engineering. The breakthrough in using models in computers came with Boolean algebra which resulted in modeling computer hardware by logic gates such as AND, OR and NOT. The logic gate model of hardware enjoyed an incredible popularity since then and forms till today the basis of computer design. The beauty and power of gate model is simply astounding. It abstracts complicated circuits into gates, allows to form any mathematical function which can be tested and verified for logical correctness. This is a model-driven architecture par excellence. But logical correctness is only part of the story. Such a model does not reflect time and associated with it hazards and races, temperature, quality, reliability and many other non-functional properties of integrated circuits. At the beginning of the 20th century, Andrey Markov, has developed a theory of stochastic processes that enjoys enormous popularity to date in modeling of a number of stochastic events in computer and communication systems. Next breakthrough in model of computing came with flow process charts which were originally used to model business processes (Frank Gilbreth, 1921) and then used by programmers in late fifties to reflect a logic flow of a computer program in a form of a flowchart. Flowchart depicts realization of an underlying algorithm and depending on its level, the model may just sketch the way to achieve a given goal or give a detailed implementation (execution) of a program realizing a given algorithm. It mainly focuses on basic functionality while non-functional properties are largely neglected. A refined form of a flowchart is a Nassi-Schneidermann Diagram [3], which supports Dijkstra’s structured programming concept. Since its inception in the sixties, the proposal by Carl Adam Petri for system modeling has enjoyed tremendous interest (called Petri nets theory today) and has resulted in a number of applications.

The Art of Creating Models and Models Integration

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In the eighties the dominant development was pattern design [4] that promoted the reuse of certain solutions in software development process and with objects [5] and components [6] the software reuse became a reality. Also, representation of finite state automata, called statecharts was proposed by David Harel [7]. With further development of computer languages, this formed the basis for textual models of algorithms leading to the Unified Modeling Language (UML) in the 1990’s which allowed to semiformally synthetize and encode models. It also gave rise to, so called Model Driven Architecture (MDA), which promotes models that can be automated in part (unlike CASE - Computer-Aided Software Engineering where full automation is the goal) to accelerate and improve software development process. The MDA strives for separation of concerns and distinguishes four levels of models: • • • •

Computation Independent Model (CIM) – description and specification Platform Independent Model (PIM) - a model of the business process or service Platform Specific Model (PSM) - platform-dependent model of architecture or service Code Model, Platform Implementation

This is already a remarkable progress but, unfortunately, due to complexity of systems we would like to model, a comprehensive description of most systems is not feasible but frequently also not necessary as in the MDA we want to focus on goals and properties that we, the developers/users, are interested in and not just the entire system. We have to be realistic and recognize that reflecting the reality fully may not be feasible. When Niels Bohr was searching for a complete description of the world (nature) around us believing that everything can be deduced by logic, after many years of research he concluded that there is mutually exclusive but at the same time mutually complementary world of illogic. He called the phenomenon complementarity and he even designed a coat-of-arms with inscription contraria sunt complementa and the yin-yang symbol reflecting his principle of complementarity on which the fundamental laws of physics are based.

3 Problems and Challenges An important part of work of a software/hardware engineer is ability to translate a real world phenomenon into his or her own language. This art, as it is evolving into a science, results in a success, partial success or a failure of the undertaking at hand. In our activities, we should always remember that a model is usually a simplified version of reality so stating that “the system works” should only apply to a model as reality may turn out to be different. Good models reflect reality very closely, the bad ones behave differently or even opposite to the goals of a real system. It is highly desirable to be able to measure the distance between a model and a reality in order to evaluate its goodness. There are a couple of problems with this task: first, we can do it only with respect to a limited number of variables (limited by the model) and second, we have to have ability to measure the reality (a real system) without influencing its behavior. Ability to determine distance would help us to assess model’s relevance in achieving the desirable goal. Models can vary in

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their level of formalism, complexity (level of detail) and quality in meeting the intended goals. These characteristics largely depend on the basic function of the model and the complexity of modeling goal. The blessing and the curse with artifacts such as software is that so called “reality” does not even exist during the system development. Once the software is developed then we can run it on a particular platform and measure. The fundamental problems with modeling of computer systems are: 1.

2.

3.

4. 5.

6. 7.

Creation of artifacts, systems that do not exist in nature, so it is difficult to assess quantitatively their quality even when they are created as there is no reference point. The only way out are comparative analysis (e.g., who has developed bigger or faster engine, algorithm, etc.). Unconstrained design space and unconstrained objectives (software engineers can promise to do the impossible, e.g., “exceed the speed of light,” hardware engineers typically cannot, they are constrained by physics). Complexity of systems and their behavior is frequently prohibitive and despite methods like abstraction (top-down design, hierarchical design), partitioning and sequencing poses an ever-growing challenge. Demand for an ever-growing number of features (e.g., scalability, adaptivity or a real-time behavior). Conflicting requirements (e.g., a system should be fault tolerant and secure could be interpreted as a file replication and distribution for fault tolerance and keeping a single copy at one location for security – an apparent contradiction). Dynamicity of systems caused by varying configurations, patches updates, upgrades/downgrades requires highly flexible and dynamic models. Composability and integration (e.g., ability to combine various service models into a business process; models integration of structure, behavior and both functional and non-functional properties; integration of software, hardware, interoperability/infrastructure and personal with respect to a given property).

Our knowledge of reality is structured by our model and the way we have abstracted the reality. In case of non-existing objects we have to assume what reality should look like and concentrate on the goals and scope we want to achieve with particular model while escaping the question of realizability. This can be relatively easily accomplished at the CIM level. It has to become increasingly concrete and realistic once we approach the implementation phase and this process of refinement can be painstakingly difficult. Models may have different focus such as explaining phenomena (frequently used in physics), knowledge transfer, prediction (reliability or failure prediction), decision making and, finally, in specification, design and implementation process.

4 Example – Models for Failure Prediction Non-functional properties such as availability or security play ever increasing role in system development in addition to functionality of a system or service. The purpose of this example is to show how system properties can be modeled and pose a challenge of incorporating such model in service-oriented architecture (SOA) framework. We have developed best practice guide backed by methodology and models [8], [9], [10] for availability enhancement using failure prediction and recovery methods.


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This best practice guide [8] is based on the experience we have gained when investigating these topics: a.

b.

c.

complexity reduction, showing that selecting the most predictive subset of variables contributes more to model quality than selecting a particular linear or nonlinear modeling technique information gain of using numerical vs. categorical data: finding that including log file data into the modeling process may have negative impact on model quality due to increased processing requirements, data-based empirical modeling of complex software systems, cross benchmarking of linear and nonlinear modeling techniques, finding nonlinear approaches seems to be consistently superior than linear approaches, however, not always significantly.

A typical way to analyze the impact of faults and the fault tolerance of a system is to develop fault models and failure modes and then to evaluate them. In order to model and estimate the dependability of SOA it is important to know which faults and errors the system has to tolerate to assure correct operation [11]. Then, we develop methods which can detect these faults or system’s “misbehavior” and then are able to predict failures. In combination with recovery schemes system’s dependability can be enhanced.. A number of modeling techniques have been applied to failure prediction in software systems: probability models, linear and nonlinear statistical models, expert system-based models and Hidden Markov Models. f) closing the control loop

a) System Observation

b) Variable Selection / Complexity Reduction

c) Model Estimation

d) Model Application

e) Reaction / Actuation

sensitivity analysis

offline system adaptation

forec asting

online reac tion sc hemes

ARMA / AR forw ard selec tion time series (numeric al)

m ultivariate linear bac kw ard elimination

log files (c ategoric al) probabilistic w rapper

unsiversal basis func tions (UBF) radial basis func tions (RBF)

system experts support vec tor mac hines (SVM) ...

Fig. 1. Building blocks for modeling and forecasting performance variables as well as critical events in complex software systems either during runtime or during off-line testing. System observations (a) include numerical time series data and/or categorical log files. The variable selection process (b) is frequently handled implicitly by system expert's ad-hoc theories or gut feeling, rigorous procedures are applied infrequently. In recent studies attention has been focused on the model estimation process (c). Univariate and multivariate linear regression techniques have been at the center of attention. Some nonlinear regression techniques such as universal basis functions or support vector machines have been applied as well. While forecasting has received a substantial amount of attention, sensitivity analysis (d) of system models has been largely marginalized. Closing the control loop (f) is still in its infancy. Choosing the right reaction scheme (e) as a function of quality of service and cost is nontrivial [8].

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Reliability block diagrams, fault trees, Hidden Markov/Markov/semi-Markov chains, stochastic Petri nets and their combinations have been used for reliability and availability modeling. Such probability models can sometimes be solved in closed-form but will commonly be solved numerically and sometimes by discrete-event simulation. The key difficulty with such models is the parameterization and validation. For online failure prediction we have developed two models: 1) Universal Basis Function (UBF) based on function approximation using selected variables such as kernel memory fillup or the number of semaphores per second as fault symptoms [9], and 2) Hidden Semi-Markov Model (HSMM) in which error logs in space and time domain are analyzed using pattern recognition methods [10]. The challenge is how to integrate such non-functional properties models into the design and development process. Should we pose a question at every level: what can go wrong and how such a problem can be avoided? This type of process requires deep understanding of the task at hand and that is why it can be only partially automated. The challenge of building, for example, secure systems can be even more demanding as there is only a binary answer to the question of security. The security community at present is left with mainly qualitative assessment of security by analyzing specific threats and evaluating whether a given system is protected from them or not.

5 Conclusions A number of challenges have been outlined in this paper but some of them are more pressing than others. The problems of composability/integration, while preserving certain properties, requires the utmost attention and so it is with the question of taming complexity. The issues of handling of the two biggest tyrants on earth: the chance and the time1 continue to attract and fascinate researchers and engineers but the difficulty of integrating them with other models remains. The intricacy of chance requires ability to cope with unpredictability (faults and failures) and the main problem with time is that time cannot be stopped (or even slowed down) posing another eternal challenge. Finally, the art of creating models and integrating them will continue to evolve into a science.

References 1. Sommerville, I.: Software Engineering, 8th edn. Pearson Education, London (2006) 2. Schichl, H.: Models and the History of Modeling. In: Kallrath, J. (ed.) Modeling Languages in Mathematical Optimization, Kluwer, Boston (2006) 3. Nassi, I., Shneiderman, B.: Flowchart Techniques for Structured Programming. In: SIGPLAN Notices, August 8, 1973 (1973) 4. Christopher, A., Ishikawa, S., Silverstein, M., Jacobson, M., Fiksdahl-King, I., Angel, S.: A Pattern Language: Towns, Buildings, Construction. Oxford University Press, New York (1977) 5. Cox, B.J., Novobilski, A.J.: Object-Oriented Programming: An Evolutionary Approach, 2nd edn. Addison-Wesley, Reading (1991) 1

Two biggest tyrants on Earth are: the chance and the time (Die zwei größten Tyrannen der Erde: der Zufall und die Zeit) Johann Gottfried von Herder (1744-1803)


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6. Szyperski, C.: Component Software: Beyond Object-Oriented Programming, 2nd edn. Addison-Wesley Professional, Boston (2002) 7. Harel, D.: Statecharts: A Visual Formalism for Complex Systems. Science of Computer Programming, vol. 8, North Holland, Amsterdam (1987) 8. Hoffmann, G.A., Trivedi, K.S., Malek, M.: A Best Practice Guide to Resource Forecasting for Computing Systems. IEEE Transactions on Reliability 56(4) (2007) 9. Hoffmann, G.A., Malek, M.: Call Availability Prediction in a Telecommunication System: A Data Driven Empirical Approach. In: IEEE Symposium on Reliable Distributed Systems (SRDS 2006), Leeds, United Kingdom (2006) 10. Salfner, F., Malek, M.: Using Hidden Semi-Markov Models for Effective Online Failure Pre-diction. In: IEEE Proceedings of the 26th Symposium on Reliable Distributed Systems (SRDS 2007), Beijing, China (2007) 11. Brüning, S., Weißleder, S., Malek, M.: A Fault Taxonomy for Service-Oriented Architecture. In: Proceedings of High Assurance Systems Engineering Symposium, Dallas, Texas (2007)

Modeling Services – An Inter-disciplinary Perspective Stefan Tai and Steffen Lamparter Karlsruhe Institute of Technology (KIT), Universität Karlsruhe (TH) Karlsruhe Service Research Institute 76128 Karlsruhe, Germany [email protected], [email protected] www.ksri.uni-karlsruhe.de

1 Introduction Service engineering is receiving increasing attention in both the service economics and service computing communities. This trend is due to two observations: 1. From an economics viewpoint, services today are contributing the majority of jobs, GDP, and productivity growth in Europe and in other countries worldwide. This includes all activities by service sector firms, services associated with physical goods production, as well as services of the public sector. 2. From an ICT viewpoint, the evolution of the Internet enables the provision of software-as-a-service on the Web, and is thus changing the way distributed computing systems are being architected. Software systems are designed as service-oriented computing architectures consisting of loosely-coupled software components and data resources that are accessible using standard Web technology. The notion of “service” used in both communities is different; however, they are not independent but have a strong impact on each other. From an economics viewpoint services are increasingly ICT-enabled. Therefore, new ways of business process management, organization and value co-creation emerge for both the service provider and the service consumer. From an ICT viewpoint the engineering and use of computing services requires careful consideration of the business context, including business requirements and opportunities, business transformation, and social, organizational and regulatory policies. In this extended abstract, we explore the question of modeling services – business services and computing services. We argue for an inter-disciplinary approach to modeling and engineering services, and discuss major challenges for model-driven service engineering.

2 Definition of Services We provide the following definitions for the purposes of our discussion. A (Business) Service is a market-driven activity that co-creates a (business) value for both the service consumer and the service provider. A Web (computing) Service is a special type of service that can be accessed and delivered over the Internet. R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 8–11, 2008. © Springer-Verlag Berlin Heidelberg 2008

Modeling Services – An Inter-disciplinary Perspective

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The Service Lifecycle comprises the phases of service inception and strategy, service design, service realization, service deployment, service operation and use, and service evaluation and continuous improvement. Service Engineering describes the activities in support of the entire lifecycle of a service, with the objective to establish, sustain, and grow the service in a market (from the provider’s viewpoint), and to effectively use the service (from a consumer’s viewpoint). Service Modeling describes the engineering activities in all phases of the services lifecycle to create abstractions to reason about services.

3 Service Modeling Using the above definitions, three complementary views on service modeling can be distinguished: 1. Modeling software as (Web) services 2. Modeling (business) services as software (and thus, as Web services) 3. Modeling (business) services that use software Model-driven software engineering, and in particular the OMG’s Model-driven Architecture (MDA) has focused on modeling software and – to some limited extent – modeling software as Web services (1). In this context, MDA suggests that a software component (at first, designed using a platform-independent model, PIM) can become a Web service by making its interfaces publicly available via Web interface language and protocol standards (using a platform-specific model, PSM). This simplistic approach applies primarily to software design in forward engineering of services from a provider’s viewpoint. There are many common and important scenarios, however, which are far more complex. Consider, for example, the case of modeling a software application that aims to dynamically select (at runtime) a service from a set of available services. The selection may need to incorporate functional and non-functional criteria, some of which are only available at runtime. The service may further be provided by a services marketplace which acts as an intermediary, and contracts must be established between the consumer and the marketplace prior to using the service. To conceptualize the problem and to design a software solution, platform-independent and platform-specific information, as well as operational runtime information, must be considered. A simplistic PIM-to-PSM transformational approach is not appropriate and sufficient. Model-driven software engineering also stops short for modeling business services-assoftware (2) and for modeling business services that use software (3). MDA suggest business process modeling with a subsequent PIM and PSM software design, using model transformation and code generation. However, we question the transformational aspect and argue again for an inter-disciplinary approach, where a set of appropriate business and ICT models are used in parallel. Model transformation and code generation tend to introduce artificial orderings and dependencies, and the code generated is often of rather poor quality and thus applicable to short-lived applications at best.

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4 Example: Cloud Computing Services We illustrate our discussion of service modeling for the case of cloud computing services. In support of the trend stated in the beginning of this paper, we can see a change in the middleware (software and data integration) market towards services. The traditional, heavy-weight middleware stack is being replaced by a more lightweight stack as middleware functionality is moved into “the cloud” – a network of remote servers. Several Web-based middleware services are emerging; examples are message queuing services, data storage and backup services, and the (scalable) provision of entire computing resources and infrastructure as services, such as Amazon’s Elastic Compute Cloud (EC2). Common to all these middleware services is that they are business services and Web services compliant to our definition. Cloud computing services like the EC2 allow the service provider to better utilize available compute resources by means of virtualization and by selling partial infrastructure use as a service to multiple consumers. From the viewpoint of the consumers, and small and medium-sized companies in particular, (scalable) compute infrastructure and data centers are now accessible without the need to purchase and maintain them. Programming specifics, such as the protocols required to reserve compute capacity and the formats required for data exchange are based on standard Web services programming models, but must be carefully considered to reason about applicability and profitability of the cloud compute service. Modeling cloud computing services from a provider or a consumer viewpoint must address all relevant challenges. Technical provider challenges include the need for a sophisticated resource and network management for service provisioning, and to ensure availability, reliability, and security. Economic challenges range from competitive operation and consumer pricing models to business insight generation based on monitoring and interpreting service usage patterns. Additional (often key) problems lie in understanding and solving physical constraints, such as server storage space and electricity needs. For the consumer, a major challenge lies in understanding the business implications of outsourcing middleware and the business transformation needed. Organizational and possibly governmental and other regulatory policies must be considered. Further, the application programming model for using middleware services in the cloud is different than for using a local middleware, and varies depending on the type of middleware functionality that is provided as a service. Model-driven service engineering for cloud computing consequently requires appropriate business and ICT models in support of the above challenges. Technical and economic questions go hand-in-hand; economic models stemming from market theory, for example, and software models are insufficient in isolation, but must be combined. The state-of-the-art in model-driven software engineering focuses on software design, but not on business service design – what we need are methods and tools for model-driven service engineering.

5 Summary and Outlook Emerging services such as cloud computing services introduce new and complex economic and technical challenges. These are fundamentally changing the way that businesses can operate and the way distributed computing systems are designed. Service

Modeling Services – An Inter-disciplinary Perspective

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engineering requires modeling software-as-services as well as modeling services-assoftware. Traditional methods of model-driven software engineering have limitations in this context, and new methods and tools in support of the entire service lifecycle are needed. In addition, the Internet has evolved into a people collaboration platform with continuous on-line access, and is changing the way people interact with each other. Wikis and digital communities are examples of Web 2.0 technologies that have a significant impact on how we exchange and work with all kinds of data – and services. We believe that these developments will grow further and into the services market. Service evaluations (ratings, recommendation), and community-driven support services for business and Web services are likely to emerge and contribute to the value of a service. Without careful consideration of all relevant economic, technical, and social collaboration challenges, services such as cloud computing services and their adoption in markets cannot succeed. Modeling such services thus places new requirements on the engineering methodology and requires focusing on the challenges that really matter.

Data Mashups for Situational Applications Volker Markl, Mehmet Altinel, David Simmen, and Ashutosh Singh IBM Almaden Research Center, San Jose, CA, USA {marklv,maltinel,simmen}@us.ibm.com, [email protected]

Abstract. Situational applications require business users to create combine, and catalog data feeds and other enterprise data sources. Damia is a lightweight enterprise data integration engine inspired by the Web 2.0 mashup phenomenon. It consists of (1) a browser-based user-interface that allows for the specification of data mashups as data flow graphs using a set of Damia operators specified by programming-by-example principles, (2) a server with an execution engine, as well as (3) APIs for searching, debugging, executing and managing mashups. Damia provides a base data model and primitive operators based on the XQuery Infoset. A feed abstraction built on that model enables combining, filtering and transforming data feeds. This paper presents an overview of the Damia system as well as a research vision for data-intensive situational applications. A first version of Damia realizing some of the concepts described in this paper is available as a webserivce [17] and for download as part of IBM’s Mashup Starter Kit [18].

1 Introduction A mashup is a web application that combines content from two or more applications to create a new application [9]. Situational applications are enterprise web applications built on-the-fly to solve a specific business problem [1]. They are often developed without involvement of the IT department and operate outside of its control. They combine data from a variety of enterprise sources such as SAP or Office applications, back-end databases, and content management systems. Any distinction between mashups and situational applications will become progressively blurred as situational applications augment enterprise data with data outside the firewall. In effect, situational applications are enterprise mashups. Enterprise mashups present a "data problem", as they need to access, filter, join, and aggregate data from multiple sources; however, these data machinations are typically done in the application mixed with business and presentation logic. Damia implements an enterprise-oriented data mashup platform on which such applications can be built quickly, by enabling a clean separation between the data machination logic and the business logic, allowing for data independence as a basis for monitoring, scaling, and evolving situational applications. Microsoft’s Popfly and Google’s Mashup Editor do not easily allow the separation of the presentation and the data access layer of a mashup. To our knowledge, the only other similar service is Yahoo Pipes [12] as well as the Mashmaker [13] and Mashup Feeds [14] research projects]. R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 12–18, 2008. © Springer-Verlag Berlin Heidelberg 2008

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Damia goes beyond these efforts in several ways: 1. Damia has a principled data model of tuples of sequences of XML, which is more general than for instance Yahoo Pipes’ feed model. 2. Damia’s focus on enterprise data allows for ingestion of a larger set of data sources such as Notes, Excel, XML, as well as data from emerging information marketplaces like StrikeIron [10]. 3. Damia’s data model allows for generic joins of web data sources. 4. Damia’s entity model allows for dynamic entity resolution, thus enabling semantic joins between feeds with different representations of the same entity.

2 Damia Architecture This section gives an overview of the Damia web application and execution engine.

Fig. 1. Damia Architecture

The Damia platform is a web application that allows for searching, debugging, compiling, executing and managing data mashups. 2.1 The Damia Web Application Situational applications are typically created by departmental users with little programming knowledge; consequently visualization of data mashup operations is critical for any mashup platform. Considering this key aspect, we developed a browser-based user interface that allows Damia users to perform major operations easily and intuitively. The GUI provides facilities for composing new data mashups, searching data sources or existing mashups, and managing stored mashups. The mashup composition follows programming-by-example model which makes the development process more natural and less error prone. Figure 2 shows a snapshot of the Damia Mashup Editor which was implemented with the Dojo toolkit [4]. It communicates with the server through a set of REST API interfaces, as illustrated in Figure 1 The GUI allows users to drag and drop boxes, representing Damia operators, onto a palette and to connect them with edges representing

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Fig. 2. Damia Mashup Editor

the flow of data between those operators. Users can use a "preview" feature to see the result of the dataflow at any point in the process. The Damia GUI interacts with a metadata repository in order to suggest options for creating "touch points" for joins, grouping, and other operations that require that data be first put in a standard representation. Once the data flow is completed, the result is serialized as an XML document and delivered to the server for further processing. 2.2 The Damia Execution Engine Figure 3 provides a high-level view of Damia execution engine. The Damia engine contains four layers: (1) At the core, Damia relies on the PHP runtime environment [8] as its runtime. PHP was an attractive choice for our runtime engine as it has abundant features for accessing web data and for processing XML data. (2) Primitive Damia operators1 are Higher Value Functionalitie

Primitive Operators

Dynamic entity resolution Lineage tracking

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construct Augment-Feed Sort

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Fig. 3. Damia Features

1

Due to space limitations we show a partial operator list including only the most commonly used ones.


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implemented as PHP classes. They can be wired to each other to function as pipelined (pull-based) or streaming (push-based) fashion. The primitive operators are designed with the goal to enable advanced user with the almost full power of XQuery in a data flow fashion. (3) Feed Operators provide a feed-friendly abstraction on top of the primitive operators. (4) Higher-value features are implemented as extensions modules within the engine. 2.3 Damia Operators and Data Model The Damia operators produce and consume tuples of sequences. A sequence is an ordered set of items wherein an item can be an atomic value or an XML node. The Damia data model provides a closed data model which allows new data mashups to be composed of existing ones. In general, there are three classes of Damia operators: ingestion, augmentation, and publication. Ingestion wrappers are sources in the Damia data flow graph, which translate data sources into XML documents or feeds to be further consumed by Damia Augmentation Operators. Standard ingestion wrappers supported by Damia exist for REST and SOAP Web Services, Excel Spreadsheets, Lotus Notes databases, as well as screen scraping for HTML pages. The overall system of ingestion operators is extensible, as any data source can be ingested into Damia by providing simply a SOAP or REST wrapper. In the terms of the Damia data model, an ingestion operator takes a data object and translates it into a tuple of sequences of XML data. Augmentation operators are internal nodes of the data flow graph. Damia is extensible in that user defined operators can be written in either PHP, called as webservices, or written using Damia’s primitives, and can be plugged into the engine. Publication operators are the sinks of the data flow graph. They transform the result of the mashup into common output formats like JSON, HTML, and various XML formats (e.g. Atom, RSS). Damia publication operators can be extended to produce other formats. 2.4 Damia Metadata Services The metadata services of Damia handle the storage and retrieval of data feeds created by the Damia community. In addition to publishing data feeds created via Damia data flows, a user can publish resources like Excel Spreadsheets or XML documents to the Damia system, to make them consumable by mashups. It is possible to share published resources with others or to keep them private. The Damia mashup repository stores both the graphical mashup specification serialized as XML as well as the PHP code that the mashup specification was compiled into. Users execute stored mashups by calling a REST URL provided by the Damia system.

3 Important Features of a Mashup System This section summarizes advanced features offered of an enterprise data mashup system and gives insights on how the Damia research team at IBM plans to address them.

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Ingestion of "Enterprise Data": A common characteristic of mashup applications available on the Web today is that they consume URL addressable resources. However, this doesn’t apply for enterprise data sources as they are exposed in many different forms such as office documents, email, and pure databases. Therefore, the Damia system must provide tools and mechanisms to tap into non-URL addressable sources, and make them available in the server. We developed a set of predefined wrappers for this purpose. Furthermore, we consider utilizing existing page-scraping tools (e.g. Kapow [5] or Lixto [6]) to turn web pages (inside and outside of enterprise domains) into ready-to-use data sources in Damia server. Another unique characteristic of creating en)terprise mashups is that a multitude of authentication models are in use in enterprise systems. Although we utilize an LDAPbased common sign-on system in the current prototype, this is actually an open research issue, and we are investigating new techniques to embrace this heterogeneity. Dynamic Entity Resolution: By definition, the quality of mashup applications is directly related to how they bring together data from multiple sources. Therefore, the level of sophistication in matching different entities is a key distinguishing feature for any data mashup platform. This standardization problem is usually addressed in two dimensions: (1) identification of semantically known entities, and (2) resolving differences in representations. We observe that more and more standardization services are becoming available on the Internet for most commonly used data types2. A similar trend is also visible within enterprise systems with widespread adaptation of master data management products. Therefore, we designed dynamic entity resolution module to exploit growing number of standardization services with little effort. During the mashup design, we show applicable standardization services to users and expect them to choose the right ones for their mashup3. At runtime, the system can find out possible touch points between entities and can generate the right set of transformation functions to perform the join. For the cases where the Damia server cannot detect the entities, there are facilities for users to introduce the entities into the system and select/register suitable standardization services for entity matching. We are investigating how to exploit existing tools and platforms (e.g. ClearForest [2], UIMA [11]) for detecting known entities in input sources. Once the entity matching is finished, these steps are remembered to help future users when they try to use same sources in their mashups. Such a “folksonomy-based” approach is aimed at taking the advantage of collective power of Damia community to deliver a promising framework for large-scale data integration. Streaming: RSS and Atom feeds are a norm in the Internet. A huge amount of information today is available through feed interfaces. Hence, we designed the Damia system to be able to understand and consume feeds effortlessly. Feeds are inherently “push-based”, i.e. their content is dynamically updated at the source without any notification. To be able to cope with this streaming aspect, the Damia system includes mechanisms to detect and process the changes, and to deliver notifications to its applications. In enterprise domains, there are many useful and critical mashup scenarios 2

Geocoding service for address types is the prime example. Many Internet companies including Yahoo and Google provide this service for free. 3 Programming-by-example model enables us to perform this task.


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where this feature is extremely valuable, particularly for reporting and dashboarding applications. Search: Mashup applications are typically created in large numbers since they are only useful for a specific situation concerning a small number of users. Hence, a common problem is how to find a right mashup application or a data source when it is needed. An effective search mechanism for mashups requires understanding not only the properties of input sources but also how they are processed in data flow. We performed an initial investigation on this problem, and developed a preliminary search mechanism. We believe this is an important research problem, and we are working on enhancing the current solution. Lineage: Mashups on the Web usually do not provide any metrics on data quality as this issue is not considered central. However, this is not always true for enterprise mashups applications, as sometimes important business decisions may be made based on their outcome. In such cases, when an enterprise data mashup is composed with other mashups, it becomes very critical to be able to provide lineage information for its users to asses its data quality. Again, this is an active research area, and we only implemented basic mechanisms to address this issue. Uncertainty: It is not always the case that all the operations return exact results in mashups. There may be situations where uncertain results are inevitable: (1) Data sources may return probabilistic (or ranked) data. Typical example is when a search engine result is fed into the mashup. (2) Entity matching may not yield exact results in some cases. Hence, the result of joins may become probabilistic. In such cases, when an uncertainty is introduced in the system, it has to be understood and modeled in the data flow. Like the lineage problem, this aspect is mostly ignored in Web mashups, but it may be crucial in some class of enterprise mashups. We implemented a set of operators (join, sort, aggregate, etc.) which can deal with probabilistic results in the data flow. We are actively exploring new probabilistic models, improving the existing operators and adding new ones.

4 Conclusions Proliferation Web 2.0 technologies and ever increasing number of available Web data services gave rise to phenomenal growth of mashup applications on the Internet. We anticipate that enterprise systems will greatly benefit from this new trend by means of enterprise-oriented mashup development platforms. We are laying the foundations of such a platform in the Damia project [17, 18]. In this paper, we presented our initial Damia prototype, which includes a mashuporiented data flow engine enriched with novel, enterprise-specific advanced functionalities. Most notable features include a folksonomy-based standardization service, a intuitive GUI front-end, a hosted, scalable mashup server architecture, unique ingestion facilities and utilities for mashup search and management. Going forward, we aim to use this prototype as a playground to explore many interesting research directions outlined in this paper.

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Acknowledgements We thank the CTO office of IBM Information Management as well as the Mashup Hub and Damia teams from IBM Research, IBM Emerging Technologies, and IBM Software Group for their help on making our vision on Damia happen.

References [1] Jhingran, A.: Enterprise Information Mashups: Integrating Information, Simply. In: VLDB 2006, pp. 3–4 (2006) [2] Clearforest Inc., http://www.clearforest.com/ [3] DB2 pureXML™ technology, http://www-306.ibm.com/software/data/db2/xml/ [4] Dojo, the Javascript toolkit, http://dojotoolkit.org/ [5] Kapow Technologies, http://www.kapowtech.com/ [6] Lixto Software Gmbh, http://www.lixto.com/ [7] PEAR - PHP Extension and Application Repository, http://pear.php.net/ [8] PHP: Hypertext Preprocessor, http://www.php.net/ [9] Programmable Web, http://www.programmableweb.com/ [10] Strikeiron Inc., http://www.strikeiron.com/ [11] Unstructured Information Management Architecture (UIMA), IBM Research, http://www.research.ibm.com/UIMA/ [12] Yahoo Pipes, http://pipes.yahoo.com/pipes/ [13] Ennals, R., Garofalakis, M.N.: MashMaker: mashups for the masses. In: SIGMOD Conference 2007, pp. 1116–1118 (2007) [14] Tatemura, J., Sawires, A., Po, O., Chen, S., Candan, K.S., Agrawal, D., Goveas, M.: Mashup Feeds: continuous queries over web services. In: SIGMOD Conference 2007, pp. 1128–1130 (2007) [15] Maximilien, M.: Web Services on Rails: Using Ruby and Rails for Web Services Development and Mashups. IEEE SCC (2006) [16] Wong, J., Hong, J.I.: Making mashups with marmite: towards end-user programming for the web. In: CHI 2007, pp. 1435–1444 (2007) [17] IBM Damia Service on IBM Alphaservices, http://services.alphaworks.ibm.com/damia [18] IBM Mashup Starter Kit on IBM Alphaworks, http://www.alphaworks.ibm.com/tech/ibmmsk

Combining Effectiveness and Efficiency for Schema Matching Evaluation Alsayed Algergawy, Eike Schallehn, and Gunter Saake Department of Computer Science, Otto-von-Guericke University, 39106 Magdeburg, Germany {alshahat,eike,saake}@iti.cs.uni-mgdeburg.de

Abstract. Schema matching plays a central role in many applications that require interoperability among heterogeneous data sources. A good evaluation for different capabilities of schema matching systems has become vital as the complexity of such systems arises. The capabilities of matching systems incorporate different (possibly conflicting) aspects among them match quality and match efficiency. The analysis of efficiency of a schema matching system, if it is done, tends to be done in a way separate from the analysis of effectiveness. In this paper, we present the trade-off between schema matching effectiveness and efficiency as a multi-objective optimization problem. This representation enables us to obtain a combined measure as a compromise between them. We combine both performance aspects in a weighted-average function to determine the cost-effectiveness of a schema matching system. We apply our proposed approach to evaluate two currently existing mainstream schema matching systems namely COMA++ and BTreeMatch. Experimental results showed that, by carefully utilizing both small-scale and large-scale schemas, it is necessary to take the response time of the matching process into account especially in large-scale schemas. Keywords: Schema matching, Schema matching performance, Effectiveness, Efficiency, Cost-effectiveness.

1

Introduction

Schema matching is the task of identifying semantic correspondences between the elements of two or more schemas and plays a central role in many data application scenarios [6,13,10]: in data integration, to identify and characterize interschema relationships between multiple (heterogeneous) schemas; in E-business, to help exchange messages between different XML formats; in semantic query processing, to map user-specified concepts in the query to schema elements; in the semantic Web, to establish semantic correspondences between concepts of different websites ontologies; and in data migration, to migrate legacy data from multiple sources into a new one [7]. To identify a solution for a particular match problem, it is important to understand which of the proposed techniques performs best. The performance R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 19–30, 2008. Springer-Verlag Berlin Heidelberg 2008

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of a schema matching system comprises mainly two equally important factors; namely; effectiveness and efficiency [15,8]. The effectiveness is concerned with the accuracy and the correctness of the match result while the efficiency is concerned with resources consumption (time, memory,...) by the match system. Most of existing system evaluations focus on analyzing the effectiveness of a schema matching system [2,16]. The analysis of the efficiency of a schema matching system, if it is done, tends to be done in a way separate from the analysis of effectiveness [8]. In other words, effectiveness and efficiency are usually considered two very different dimensions, and the trade-off between these two dimensions has not been investigated in the schema matching community. Many real-world problems such as the schema matching problem, involve multiple measures of performance, which should be optimized simultaneously. Optimal performance according to one objective, if such an optimum exists, often implies unacceptably low performance in one or more of the other objective dimensions, creating the need for a compromise to be reached. In the schema matching problem, the performance of a matching system involves multiple aspects among them effectiveness and efficiency. Optimizing one aspect for example effectiveness will affect the other aspects such as efficiency. Hence, we need a compromise between them, and we could consider the trade-off between effectiveness and efficiency matching result as a multi-objective problem. In practice, multi-objective problems have to be re-formulated as a single objective problem. To this end, in this paper, we propose a method for computing the costeffectiveness of a schema matching system. Such a method is intended to be used in a combined evaluation of schema matching systems. This evaluation concentrates on the cost-effectiveness of schema matching approaches, i.e. the trade-off between effectiveness and efficiency. The motivation behind this is that suppose we want to compare two schema matching systems to solve a specific matching problem. If we have a schema matching problem P, and we have two matching systems A and B. The system A is more effective than system B, while the system B is more efficient than system A. The arising question here is which system will be used to solve the given problem? So far most existing matching systems [3,10,12] evaluate their performance according only to effectiveness issues, hence they all choose the system A (more effective). This paper introduces a combined approach to evaluate cost-effectiveness of matching systems based on the multi-objective optimization problem (MOOP). We apply the proposed approach to evaluate and compare two well-known systems, namely COMA++ [3] and BTreeMatch [9]. The rest of this paper is organized as follows: the next section introduces an overview of schema matching. In the following section, we present schema matching performance focusing on effectiveness and efficiency evaluations. Section 4 presents a combined measure for schema matching performance, costeffectiveness measure. In section 5, experiments and results are discussed. Section 6 gives concluding remarks and our proposed future work.

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Schema Matching: An Overview

In this section, we present the main definitions used in this paper. Definition 1. (Schema) A schema is a description of the structure and the content of a model and consists of a set of related elements such as tables, columns, classes, or XML elements and attributes. By schema structure and schema content, we mean its schema-based properties and its instance-based properties, respectively. Definition 2. (Match) Match is a function that takes two or more schemas as input and produces a mapping as output Definition 3. (Mapping) A mapping is a set of mapping elements specifying the correspondence of schemas’ elements together. Each mapping element is 5-tuple < ID, S.si , T.tj , R, semrel > where: ID is an identifier for the mapping element, si is an element of the first schema, tj is an element of the second one, and R indicates the similarity value between 0 and 1. The value of 0 means strong dissimilarity while the value of 1 means strong similarity. semrel is the semantic relationship between two (or more) elements such as equivalence, synonyms, etc. To identify the correspondences among schemas’ elements, various matching algorithms have been proposed and numerous schema matching systems have been developed. The current matching algorithms can be classified by either the information they exploit or their methodologies. According to the information they exploit the matchers can be: [13] individual matchers which exploits only one type of element properties in a single algorithm, or combining matchers that can be one of two types: hybrid matchers (integrate multiple criteria [10]) and composite matchers (combine results of independently executed matchers [5,3]). The exploited information by a matcher is called element properties. These properties can be classified as atomic or structural properties; schema-based or instancebased properties; and auxiliary properties. According to the methodologies of the matching algorithm, they can be classified as either rule-based or learner-based [6,15]. Table 1 summarizes the advantages and disadvantages of both systems. Table 1. Comparison of Rule-base and learner-based Systems Criteria Rule-based Learner-based exploited information schema-based instance-based nature of schema elements more static more dynamic schema size small size large size training phase not needed needed response time less time more time

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Schema Matching Performance

In order to motivate the importance of trading-off performance aspects during schema matching evaluation, we present the schema matching problem as follows: consider two schemas S and T having n and m elements respectively, we could distinguish between two types of matching, simple and complex – Simple matching: for each element s of S, find the most semantically similar element t of T. This problem is referred to as one-to-one matching. – Complex matching: for each element s (or a set of elements) of S, find the most semantically similar set of elements t1 , t2 , ..., tk of T. The solution to the above problems is not unique, and this is due to inherent difficulties in schema matching. In [17], they introduce the concepts of matching state and matching space. A matching state represents a possible matching result with complexity of n m and the matching space is the all possible matching states with order of 2n×m . For example, consider n = 5 and m = 4, then to identify a suitable match result for a given schema matching problem, a schema matching system should search in a matching space of 25×4 = 1048576 (very large matching space although very small schemas’ sizes). Therefore, the schema matching problem is an optimization problem which searches for the best solution (matching state) among vast available solutions (matching space). However, the problem becomes not only identifying the best solution but also obtaining this solution in a reasonable time. Unfortunately, most previous evaluations ignore the time response of the match system and often assume that matching is an off-line process. Hence, in order to cope with large scale schemas, we should take the processing time into account. In the following subsection, we consider both performance aspects; effectiveness and efficiency. To obtain a better overview about the current state of the art in evaluating schema matching approaches, good reviews could be found in [2,16,8]. 3.1

Performance Evaluation

To unify the performance evaluation of schema matching systems, the following criteria should be taken into account – Input : what kind of input information has been used (schema-based, instancebased, auxiliary information)? – Output : what information has been included in the mappings? i.e. the type of output, output formats, and the complexity of mappings, – Performance measures: what metrics have been chosen to quantify the match result?; and – Effort : what kind of manual effort has been measured? To assess the manual effort, one should consider both pre-match effort required before an automatic matcher can run (such as training of learner-based matchers, specifying auxiliary information) as well as post-match effort to add the false negatives and to remove the false positives from the final match result.


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Effectiveness Measures: First, the match task should be manually solved to get the real mappings Rm . Then, the matching system solves the same problem to obtain automatic mappings Am . After identifying both real and automatic mappings it is possible to define some terms that will be used in computing match effectiveness. False negatives A =Rm - Am : are the needed matches but not identified by the system; True positives B =Rm ∩ Am : are the correct matches and identified correctly by the system; False positives C =Am - Rm : are the false matches but identified by the system; and True negatives D: are the false matches and correctly discarded by the system. Precision and Recall: Based on real and automatic mappings, two measures can be computed. The two measures are precision and recall, which originate from the information retrieval (IR) field [14]. Precision P can be computed from |B| |B| and the recall R is computed R = |B|+|A| . However, neither precision P = |B|+|C| nor recall alone can accurately assess the match quality. Hence, it is necessary to consider a trade-off between them. There are several methods to handle such a trade-off, one of them is to combine both measures. The most used combined measures are: – F-Measure: is the weighted harmonic mean of precision and recall, the traditional F-measure or balanced F-score is: F =

P ∗R 2 ∗ |B| = 2∗ (|B| + |A|) + (|B| + |C|) P +R

(1)

– Overall : is developed specifically in the schema matching context and embodies the idea to quantify the effort needed to add false negatives and removing false positives. It is introduced in the Similarity Flooding (SF) system [12] and is given by |A| + |C| 1 OV = 1 − = R ∗ (2 − ) (2) |A| + |B| P To determine which one is used as a measure for the effectiveness of a schema matching system, we compare the two combined measures. Figure 2 as well as equations (1) and (2) represent a good way for this comparison. Since the overall measure is more sensitive to precision than recall. Therefore, in this paper, we consider the overall (OV ) measure as an indicator for schema matching effectiveness. Efficiency Evaluation: Efficiency is mainly contained in two properties: speed (the time it takes for an operation to complete), and space (the memory or nonvolatile storage used up by the construct). In order to obtain a good efficiency measure for schema matching systems, we should consider the following factors: – A first factor to consider when we evaluate the schema matching efficiency is the critical phase of a matching process. A matching process consists of many phases and each phase contains multiple steps.

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Precision

Fig. 1. F-Measure and Overall against Precision and Recall

– A second factor is its automatabity. Human effort is very expensive; matching approaches that require excessive human interaction are impractical. – A third factor that impacts schema matching efficiency is the type of methodology used to compute similarity values. Recent schema matching systems [16,8] introduce the time measure as a criterion of performance evaluation. In this paper, we take the time measure (T ) as an indicator for the schema matching efficiency. To sum up, With the emergence of applications that need fast matching systems, such as incremental schema matching systems [1], and the applications that match large schemas [4], the need to improve schema matching efficiency increases. For instance, consider a crisis management information system, it is not sufficient to provide its users with the best possible mappings, it is also necessary to obtain these mappings within a reasonable time.

4

Combining Effectiveness and Efficiency

From the above criteria, we could conclude that the trade-off between effectiveness and efficiency of a schema matching system is considered as a multi-objective optimization problem (MOOP). In this section, we present a definition for the MOOP and the approaches used to solve the problem [18,11]. In the following definitions we will assume minimization (without loss of generality). Definition 4. (Multi-objective Optimization Problem) An MOOP is defined as ”Find x that minimizes F (X) = (f1 (x)), f2 (x), ..., fK (x))T s.t. x ∈ S and x = (x1 , x2 , ..., xn )T where f1 (x), f2 (x), ..., fk (x) are the k-objective functions, (x1 , x2 , ..., xn ) are the n optimization parameters, and S ∈ Rn is the solution. In our approach, we have two objective functions, overall as a measure of effectiveness and time as a measure of efficiency. Therefore, we could rewrite the multi-objective function as: CE = (f1 (OV )), f2 (T )), where CE is the costeffectiveness which to be maximized here. In a multi-objective problem, the optimum solution consists of a set of solutions, rather than a single solution as


25

in global optimization. This optimal set is known as the Pareto Optimal set and is defined as follows: P := {x ∈ S| ∃x ∈ S F (x ) F (x)}. Pareto optimal solutions are known as the non-dominated or efficient solutions. There are many methods available to tackle multi-objective optimization problems. Among them, we choose priori articulation of preference information. This means that before the actual optimization is conducted the different objectives are somehow aggregated to one single figure of merit. This can be done in many ways, we choose weighted-sum approaches. Weighted-Sum Approaches: The most easy and perhaps most widely used method is the weighted-sum approach. The kobjective function is formulated as a weighted function as given min(ormax) i=1 wi × fi (x) s.t. x ∈ S and wi ∈ R |wi > 0, wi = 1. By choosing different weightings for the different objectives, the preference of the application domain is taken into account. As the objective functions are generally of different magnitudes and units, they should have to be normalized first. 4.1

The Cost-Effectiveness of Schema Matching

Consider we have two schema matching systems A and B to solve the same matching problem. Let OVA and TA represent the overall and time measures of the system A respectively, while OVB and TB denote the same measures for the system B. To analyze the cost-effectiveness of a schema matching system, we make use of the MOOP and its methods to solve it, namely the weighted-sum approach. Here, we have two objectives, namely effectiveness (measured by overall OV ) and efficiency (measured by response time T ). Obviously, we can not directly add up an overall value to a response time value, since the resulting sum would be meaningless, due to the difference of dimensional units. The overall value of a schema matching system is normalized value, i.e. its range is between 0 and 1, while the processing time is measured in seconds. Therefore, before summing (e.g. weighted average) the two quantities, we should normalize the processing time. To normalize the response time, for instance, the response time of the slower system (here TA ) is normalized to the value 1, while the response time of the faster system (TB ) can be normalized to a value in the range [0,1] by dividing B TB and TA , i.e. T TA . We name the objective function of a schema matching system the costeffectiveness (CE) and should be maximized. The cost-effectiveness is given by CE =

2 i=1

wi × fi (x) = w1 × OVn + w2 ×

1 Tn

(3)

where w1 is the weighting for the overall objective and denoted by (wov ) and w2 is the weighting for the time objective and denoted by (wt ). In the case of comparing two schema matching systems, we have the following normalized quantities

26


OVAn , OVBn , TAn and TBn where OVAn = OVA , OVBn = OVB , TAn =1, and B TBn = T TA . We now endeavor to come up with a single formula involving two quantities, namely normalized overall OVn and normalized response time Tn , where each of these quantity associated with a numerical weight to indicate its importance in the evaluation of the overall performance and to enrich the flexibility of the method. We write the equations that describe the cost-effectiveness (CE )for each system as follows: CEA = wovA ∗ OVAn + wtA ∗

1 TAn

CEB = wovB ∗ OVBn + wtB ∗

1

(4)

(5) TBn where wov and wt are the numerical weights for the overall and time response quantities respectively. If we let the time weights equal to zero, i.e. wt =0, then the cost-effective becomes the same normal evaluation considering only the effectiveness aspects (wov =1). The most cost-effectiveness schema matching system is the system having the larger CE as measured by the above formulas. Equations 4 and 5 present a simple but a valuable method to combine the effectiveness and the efficiency of a schema matching system. Moreover, this method is based on and supported by a proven and verified method; the multi-objective optimization problem. Although the method is effective, it still has an inherent problem. It would be difficult to determine good values for the numerical weights, since the relative importance of overall and time response is highly domain-dependent and, of course, very subjective. For example, when we are dealing with small-scale schemas, the overall measure is more dominant than the response time. Hence, we may select wov =0.8 and wt =0.2. For the critically-time systems, the response time may have the same importance as the overall measure, then we may choose wov = wt =0.5. To accommodate this problem, we need an optimization technique which enables us to determine the optimal (or close to optimal) numerical weights. In this paper, we set these values manually in the selected case studies. Automatic determination of numerical weight values is left for future work.

5

Experimental Evaluation

We evaluate our approach by comparing two recently well-known schema matching systems, namely COMA++ and BTreeMatch. We have obtained both systems from its open source distribution1 . All the experiments were performed using the XBenchMatch tool developed in [8]. However, our proposed approach can be applied easily to other schema matching systems. The problem is that it is hard to find available matching prototypes to test. We first briefly describe the two evaluated systems according to performance criteria describe above in the 1

http://dbs.uni-leipzig.de/Research/coma.html http://www.lirmm.fr/duchatea/XBenchMatch


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paper. Then we describe used data sets. We then present the results for applying the proposed approach to evaluate different schema matching systems. 5.1

Evaluated Systems

Motivated by the fact that the two matching systems COMA++ and BTreeMatch are available through its open distribution, we use them to validate our approach. The two systems share some features and differ in others. The shared features include they are schema-based approach; they utilize rule-based algorithms; they accept XML schemas as input and produce element-level mappings (one-to-one); they need pre-match effort e.g. tuning match parameters and defining match strategy; they evaluate matching effectiveness using precision, recall, and F-measure. The two systems differ in the following points: COMA++ exploits an external dictionary as auxiliary information; it uses a rich library of matchers including simple, hybrid, fragment, and context matchers; it did not consider matching efficiency in its evaluation. On the other hand, BTreeMatch does not utilize any auxiliary information sources; it uses a hybrid matcher based on Btree index; it deals with largescale schemas measuring time response as the measure for matching efficiency. 5.2

Data Set

We used the same data sets described in [8]. To make this paper self-contained, we summarize the properties of the data sets in Table 2. The first one describes a person, the second is related to business order, the third one represents university courses, and the last one comes from the biology domain. These data sets are tested on the COMA++ and BTreeMatch systems to determine the cost-effectiveness and compare between them. Table 2. Data set details from [8] Person University Order Biology No. nodes(S1 / S2 ) 11/10 18/18 20/844 719/80 Avg No. nodes 11 18 432 400 Max. depth (S1 / S2 ) 4/4 5/3 3/3 7/3 No. mappings 5 15 10 57

5.3

Experimental Results

In this section we show the experimental results of applying our approach to COMA++ and BTreeMatch using the XBenchMatch tool developed in [8]. Small-scale Schemas: The cost-effectiveness of test matchers using smallscale schemas such as university and person schemas can be computed by the following equations: CECOMA++s = wOV ∗ OVCOMA++ + wt ∗

1 TCOMA++n

(6)

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CEBT Ms = wOV ∗ OVBT M + wt ∗

1 TBT Mn

(7)

where OVCOMA++ =0.8, OVBT M =0.3, TCOMA++ =0.9s, TBT M =0.6s and wOV =0.8 (for small-scale schemas), and wt =0.2, then CECOMAs =0.64 +

0.2 1 =0.84,

and CEBT Ms =0.24 +

0.2 0.6 0.9

=0.5

Large-scale Schemas: The cost-effectiveness of test matchers using smallscale schemas such as the biology schema can be computed by the following equations: CECOMA++l = wOV ∗ OVCOMA++ + wt ∗ CEBT Ml = wOV ∗ OVBT M + wt ∗

1 TCOMA++n 1

(8) (9)

TBT Mn

where OVCOMA++ =0.4, OVBT M =0.8, TCOMA++ =4s, TBT M =2s and wOV =0.6 (for large-scale schemas), and wt =0.4, then CECOMAs =0.24 + 5.4

0.4 1 =0.64,

and CEBT Ms =0.48 +

0.4 2 4

=1.28

Discussion

The experiment section shows that schema matching prototypes are best suited for certain situations. For example, see Table 3, the cost-effectiveness of COMA++ is well accepted for small-scale schemas while it is not accepted for BTreeMatch. However, for large-scale schemas, the cost-effectiveness increases for BTreeMatch and decreases for COMA++. Table 3. Summary of results Evaluated System COMA++ BTreeMatch

OV T CE small-scale large-scale small-scale large-scale small-scale large-scale 0.8 0.4 0.9s 4s 0.84 0.64 0.3 0.8 0.6s 2s 0.5 1.28

We study the relationship between cost-effectiveness and both performance aspects (overall and response time). Figures 2 illustrates this relationship, where the squared line represents the overall only, the dashed line represents the response time and the solid line represents both. Figure 2(a) is drawn for the small-scale case (i.e.wOV =.8 and wt =.2 ) while Fig. 2(b) is drawn for the largescale schemas (wOV =.5 and wt =.5 ). In the case of small-scale schemas, the cost-effectiveness is more biased to overall measure than the response time of the system, while in the case of large-scale schemas, the cost-effectiveness is biased by both performance aspects.

Combining Effectiveness and Efficiency for Schema Matching Evaluation both response time overall

3

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cost−effectiveness

cost−effectiveness

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2 1.5 1 0.5 0 1

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0

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overall

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Fig. 2. Performance Aspects with Cost-Effectiveness

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Summary and Future Work

In this paper, we presented an approach, where both effectiveness and efficiency are taken into account. We introduced the trade-off between aspects of schema matching performance as a multi-objective optimization problem. Then, we make use of the weighted-sum approach as priori articulation of preference information. The cost-effectiveness is taken as a measure for the overall performance. This measure combines (weighted sum) the two performance aspects in a single formula. The formula contains overall measure as an indicator for effectiveness and normalized response time as an indicator for efficiency. To enrich the flexibility of the method, each quantity is associated with a numerical weight to indicate its importance in the evaluation of the overall performance. In this paper, we set the numerical weights manually depending on our experience. We applied our proposed method to fairly evaluate and compare two wellknown schema matching systems (COMA++ and BTreeMatch). We have discussed the effect of schema size on match performance. For small-scale schemas, match performance is more affected by match effectiveness, while in large-scale schemas two performance aspects have equal effect. Our proposed approach can be integrated with the recent schema matching benchmarks. Moreover, our ongoing work is to build a unified evaluation process in order to decide on schema matching performance. The impact of numerical values of weights and identifying their optimal values automatically is one of our future work.

References 1. Bernstein, P.A., Melnik, S., Churchill, J.E.: Incremental schema matching. In: VLDB, Korea (2006) 2. Do, H.H., Melnik, S., Rahm, E.: Comparison of schema matching evaluations. In: the 2nd Int. Workshop on Web Databases (2002) 3. Do, H.H., Rahm, E.: COMA- a system for flexible combination of schema matching approaches. In: VLDB, pp. 610–621 (2002)

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4. Do, H.-H., Rahm, E.: Matching large schemas: Approaches and evaluation. Information Systems 32(6), 857–885 (2007) 5. Doan, A., Domingos, P., Halevy, A.: Reconciling schemas of disparate data sources: A machine-learning approach. SIGMOD, 509–520 (2001) 6. Doan, A., Halevy, A.: Semantic integration research in the database community: A brief survey. AAAI AI Magazine 25(1), 83–94 (2005) 7. Drumm, C., Schmitt, M., Do, H.-H., Rahm, E.: Quickmig - automatic schema matching for data migration projects. In: Proc. ACM CIKM 2007, Portugal (2007) 8. Duchateau, F., Bellahsene, Z., Hunt, E.: Xbenchmatch: a benchmark for XML schema matching tools. In: VLDB 2007, Austria, pp. 1318–1321 (2007) 9. Duchateau, F., Bellahsene, Z., Roche, M.: An indexing structure for automatic schema matching. In: SMDB Workshop, Turkey (2007) 10. Madhavan, J., Bernstein, P.A., Rahm, E.: Generic schema matching with cupid. In: VLDB, Italy, pp. 49–58 (2001) 11. Marler, R., arora, J.: Survey of multi-objective optimization methods for engineering. Struct. Multidisc Optim. 26, 369–395 (2004) 12. Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In: ICDE 2002 (2002) 13. Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB Journal 10(4), 334–350 (2001) 14. Rijsbergen, C.J.: Information Retrieval, 2nd edn., London (1979) 15. Smiljanic, M.: XML Schema Matching Balancing Efficiency and Effectiveness by means of Clustering. PhD thesis, Twente University (2006) 16. Yatskevich, M.: Prelimanary evaluation of schema matching systems. Technical Report #DIT-03-028, Tornoto University (2003) 17. Zhang, Z., Che, H., Shi, P., Sun, Y., Gu, J.: Formulation schema matching problem for combinatorial optimization problem. IBIS 1(1), 33–60 (2006) 18. Zitzler, E., Thiele, L.: Multiobjective evolutionaty algorithms: A comparative case study and the strength pareto approach. IEEE Tran. on EC 3, 257–271 (1999)

Model-Driven Development of Complex and Data-Intensive Integration Processes Matthias Böhm1 , Dirk Habich2 , Wolfgang Lehner2, and Uwe Wloka1 1

2

Dresden University of Applied Sciences, Database Group [email protected], [email protected] Dresden University of Technology, Database Technology Group [email protected], [email protected]

Abstract. Due to the changing scope of data management from centrally stored data towards the management of distributed and heterogeneous systems, the integration takes place on different levels. The lack of standards for information integration as well as application integration resulted in a large number of different integration models and proprietary solutions. With the aim of a high degree of portability and the reduction of development efforts, the model-driven development—following the Model-Driven Architecture (MDA)—is advantageous in this context as well. Hence, in the GCIP project (Generation of Complex Integration Processes), we focus on the model-driven generation and optimization of integration tasks using a process-based approach. In this paper, we contribute detailed generation aspects and finally discuss open issues and further challenges. Keywords: Model-Driven Architecture, Integration Processes, GCIP, Federated DBMS, Enterprise Application Integration, Extraction Transformation Loading.

1 Introduction The scope of data management continuously changes from centrally stored data to the integration of distributed and heterogeneous systems. In fact, integration is realized on different levels of abstraction: we distinguish between information integration (function integration and data integration), application integration, process integration, and partially also GUI integration. Due to missing standards for information integration and application integration, numerous different integration systems with overlapping functionality exist. In conclusion, only a low degree of portability of the integration task specification can be reached. However, portability is strongly needed, in particular, in the context of extending existing integration projects. Typically, in an enterprise IT-infrastructure, there are multiple integration systems. Assume that two relational databases are currently integrated with a federated DBMS for reasons of performance. If this integration process should be extended with the aim of integrating an SAP R/3 system, functional restrictions make it necessary to transfer the integration process to the existing EAI server, with the lowest possible development efforts. Aside from the main problem of portability, there are three more major problems. First, extensive efforts are R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 31–42, 2008. c Springer-Verlag Berlin Heidelberg 2008

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needed in order to specify integration tasks. This includes, for example, mapping specifications between heterogeneous data schemas. Although there are approaches to minimize these efforts, like automated schema matching or data integration with uncertainty (e.g., dataspaces), these approaches are not applicable in real-world enterprise scenarios, due to insufficient exactness. Second, very little work exists on the optimization of integration tasks. However, this is crucial, e.g., in the context of application integration, where complete business processes depend on it. Third, the decision about the optimal integration system (w.r.t. performance, functionality and development effort) is made based on subjective experience rather than on objective integration system properties. Hence, our GCIP project (Generation of Complex Integration Processes) addresses the mentioned problems using a model-driven approach to generate and optimize integration processes. Basically, integration processes are modeled as platform-independent models (PIM) using graphical process description notations like UML and BPMN. Those are transformed into an abstract platform-specific model (A-PSM). Based on this central representation, different platform-specific models (PSM) can be generated with the help of specific platform models. Currently, we provide platform models for federated DBMS, EAI servers and ETL tools. Finally, each PSM can be transformed into multiple declarative process descriptions (DPD), which represents the code layer. The contribution of this paper mainly includes three issues. First, after having surveyed related work in this research area in Section 2, we show how integration processes can be modeled in a platform-independent manner in Section 3. Second, in Section 4, we discuss generation aspects including the A-PSM, PSM and DPD specifications. Third, in Section 5, we enumerate open issues and research challenges correlated to our approach. Finally, we conclude the paper in Section 6 and give an overview of our future work on the optimization of integration processes.

2 Related Work A lot of work on MDA techniques already exists [1,2]. Further, sophisticated tools and frameworks for model-driven software engineering have been developed. Therefore, we survey related work in three steps. First, we point out major MDA techniques. Second, we show that for application development as well as for data modeling, suitable MDA support exists. Third, we illustrate the lack of model-driven techniques and approaches in the context of integration processes. The core concepts of a model-driven architecture (MDA)—specified by the Object Management Group (OMG)—are MOF [3], standardized meta models like UML [4], and model-model transformation techniques. In accordance to [5,6], it could be stated that QVT (Query/View/Transformations) and TGG (Triple Graph Grammars) are currently the most promising techniques for such model-model transformations. Due to the bidirectional mapping and the possibility of incremental model changes, TGG in particular is seen as the most suitable solution for model transformations. TGG comprises three graphs: the left-side graph (representing the first model), the right-side graph (representing the second model) and finally, a correspondence graph between the two models. Based on the given correspondence graph, a TGG rule interpreter is able to process the graph transformations for both directions.

Model-Driven Development of Complex and Data-Intensive Integration Processes

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Further, the MDA paradigm is widely used in the area of database applications for database creation. First, the model-driven data modeling and the generation of normalized database schemas should be mentioned. This approach is well known and many different systems and tools exist. Second, there is the generation of full database applications, including the data schema as well as data layer code, business logic layer code, and even user interface code. In this area, CASE tools in particular have to be named. However, there are still research items in this application area. For example, the logical database optimization is not realized adequately. The GignoMDA project [7,8] addresses this research item by allowing hint specification. Third, it can be stated that in the application area of data warehouse schema creation, MDA is also used increasingly. In accordance to the MDA paradigm, the CWM (Common Warehouse Metamodel) [9] should be mentioned. However, in contrast to normalized data modeling, there is not as much MDA support for data warehouse modeling. Within the context of integration processes, there is insufficient support for modeldriven development. Basically, two different groups of related work should be distinguished in this area. First, there is the modeling of workflow processes. With WSBPEL [10], there is a promising standard and several extension proposals. However, this language has deficits concerning the modeling of data-intensive integration processes; plus, it is a platform-specific model. In accordance to the MDA paradigm, the existing process description languages can be classified into three layers: (1) graphical representation layer, (2) description layer and (3) execution layer. The graphical notations of UML 2.0 and BPMN 2.0 [11] are included in (1). Further, in (2), only WS-CDL [12] should be mentioned. Finally, (3) comprises WSBPEL, XPDL [13] and ebBP [14]. With this logical stratification in mind, the model-driven development of workflow processes is possible. In contrast to this, there are only few contributions on generic integration process generation. One of these is the RADES approach [15], which tries to give an abstract view on EAI solutions using technology-independent and multi-vendor-capable model-driven engineering methodologies. However, this approach is very specific to EAI solutions. Although some work on ETL process modeling [16,17,18] and ETL model transformation [19,20,21] exists, most data integration research addresses schema matching automation [22,23] or static meta data management [24] rather than the model-driven generation of integration tasks for different target integration systems. Thus, we are not aware of any solution for the generic generation of data-intensive integration processes. However, we are convinced that such a solution is required as a logical consequence of the historical evolution in this area. Finally, we want to refer to the Message Transformation Model (MTM), a conceptual model for data-centric integration processes in the area of message-based application integration, which was introduced in short with [25]. This model is used as our abstract platformspecific model and thus, as the core of the whole generation process.

3 Integration Process Modeling Due to the lack of solutions for generic model-driven integration process generation, the main approach of modeling complex integration processes is introduced in this section. As already mentioned, integration processes can be specified with

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platform-independent models (PIM). Therefore, the PIM has to be derived from the computer-independent model (CIM), which is represented by textual specifications. Further, it is the first formal specification of the integration process and thus the starting point for the automated generation. In order to allow different target platforms, at this level, no technology is chosen. Thus, the platform independence of the model is reached by restricting the modeling to graphical notations like BPMN [11] and UML [4]. The procedure for PIM modeling is equal to the procedure of structured analysis and design (SAD). The single steps—which are introduced in the following—are used in order to allow for different degrees of abstraction during modeling time. Note that it might occur that, different persons will model the different degrees of abstraction. 1. Determination of terminators (external systems): First, all external systems, also known as the terminators of a process description, and their types are determined. This is derived from the overall system architecture. 2. Determination of interactions with the terminators: Second, the type of interaction is specified for each interaction between the integration system and an external system. As a result, this comprises the determination of whether these are read (pull), write (push) or mixed interaction forms. 3. Control flow modeling: Third, the detailed control flow is modeled, including structural aspects (e.g., alternatives), time aspects (e.g., delays and asynchronous flows) as well as signal handling (e.g., errors). 4. Data flow modeling: Fourth, the abstract data flow modeling is applied. This means the general specification of data flow activities like filters, data transformations and the transactional behavior. 5. Detailed model parameterization: Finally, all control flow and data flow activities are parameterized in detail using annotations. Thereby, condition evaluations are specified and the data transformation is described on the schema mapping level. Those five modeling steps result in a single platform-independent model which represents the integration process. It would be possible to separate aspects with different model views (terminator interactions, control and data flow, parameterization and configuration). Due to an increasing complexity, we explicitly do not use multiple models. We want to introduce the example processes P13 and P02 from the DIPBench (Data-Intensive Integration Process Benchmark) specification [26,27]. Obviously, these are not complex integration processes, but we use these process types as running examples throughout the whole paper. Figure 1 shows the PIM P13 and PIM P02, modeled with the help of StarUML, using the supported UML activity diagrams. The process type P13 basically describes the extraction of movement data from a consolidated database and its loading into a global data warehouse. Let us use the introduced procedure for PIM process modeling: First, the two external systems cs1.cdb.DBA and cs1.dwh.DBA are determined (“service“) and identified as RDBMS. Second, the interaction types (“operation“) have to be detected. So, at the beginning of the process, a stored procedure is called on cs1.cdb.DBA in order to realize the data cleansing [28]. Furthermore, two different datasets are queried from the cs1.cdb.DBA. These datasets are finally inserted into cs1.dwh.DBA. Third, the control flow may be determined as a simple sequence of process steps. Fourth, the data flow—specified by


35

Fig. 1. Example PIM P13 and PIM P02

rectangles and dashed arrows—has to be provided. As illustrated, the specific datasets are transfered from the extracting to the loading activities. Fifth, and thus finally, the model is enriched with technical details (in the form of UML annotations) like table names, procedure names and so on. The process type P02 focuses on the reception of Customer master data (xml messages), its translation to relational data, and the contentbased insertion into one of three target systems, based on the extracted sitekey.

4 Integration Process Generation Based on the modeled platform-specific integration processes, our GCIP Framework supports the generation of several platform-specific models. Due to the complexity of this generation, we describe this from various perspectives. 4.1 A-PSM Generation In contrast to other approaches, we use an abstract platform-specific model (A-PSM) between the PIMs and the PSMs. This is reasoned by four facts. First, it reduces the transformation complexity between n PIMs and m PSMs from n · m to n + m. Second, it separates the most general PIM representations from the context-aware A-PSM of integration processes, still independent of any integration system type. Third, it offers the possibility for applying context-aware optimization techniques in a unique (normalized) manner. And fourth, it increases the simplicity of model transformations, using small, well-defined transformation steps. Basically, the Message Transformation Model (MTM) is used as A-PSM. This model represents the starting point of all transformations into and from platform-specific models. Obviously, we are only able to generate integration processes which can be expressed adequately with the MTM. Although the MTM was already introduced in short with [25], its importance drives us to give a short overview of this meta model. The MTM is a conceptual model for data-intensive integration processes and is separated into a conceptual message model and a conceptual process model.

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The conceptual message model was designed with the aim of logical data independence and represents the static aspects of a message transformation. In accordance with the molecule atom data model (MAD), the MTM meta message model can be seen as a molecule type message and thus as a recursive, hierarchical, object-oriented and redundancy-free structure. The molecule type message is composed of two atom types: the header segment and the data segment. Furthermore, there is a unidirectional, recursive self-reference of the atom type data segment, with a 1:CN cardinality, which represents the molecule type data segment. There, the header segment is composed of k name-value pairs, whereas the data segment is a logical table with l attributes and n tuples. The option of nested tables ensures a dynamic and structure-carrying description of all data representations. The conceptual process model—the execution model for the defined messages— addresses the dynamic aspects of a transformation process and was designed with the aim of independence from concrete process description languages. Basically, a graphoriented base model Directed Graph is used. It is limited to the three components: node (generalized process step), transition (edge between two nodes) and the hierarchical process. A single node may have multiple leaving transitions, and during the runtime of one process instance, there may be multiple active leaving transitions but not more than the total number of its leaving transitions. Thus, one transition has exactly one target node. Indeed, multiple transitions could refer to one node. The process is a hierarchical element and contains a start node, several intermediate nodes and an end node. In fact, such a process is also a specialized node, so that the recursive execution with any hierarchy level is possible. A node receives a set of input messages, further executes several processing steps specified by its node type and its parameterization, and finally returns a set of output messages. The actual process model is defined—with the aim of a low degree of redundancy—on top of the base model Directed Graph. Operators are defined as specialized process steps and thus as node types. Basically, these are distinguished into the three categories: interaction-oriented operators (Invoke, Receive and Reply), control-flow-oriented operators (Switch, Fork, Delay and Signal) and data-flow-oriented operators (Assign, Translation, Selection, Projection, Join, Setoperation, Split, Orderby, Groupby, Window, Validate, Savepoint and Action). Definition 1. A process type P is defined with P = (N, S, F ) as a 3-tuple representation of a directed graph, where N = {n1 , . . . , nk } is a set of nodes, S = {s1 , ..., sl } is a set of services, including their specific operations si = {o1 , ..., om }, and F ⊆ (N × (S ∪ N )) is a set of flow relations between nodes or a node and a service. Each node has a specific node type as well as an identifier N ID (unique within the process type) and is either of an atomic or a complex type. Each process type P , with P ⊆ N , is also a node. A process p with P ⇒ p has, a specific state z(p) = {z(n1 ), . . . , z(nk )}. Thus, the process state is an aggregate of the specific single node states z(ni ). Basically, two different event types initiating such integration processes have to be distinguished. The specific event type has a high impact on the process modeling and on the generation of platform-specific models for different target integration systems. The main event types could be distinguished as follows:


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– Message stream: Processes are initiated by incoming messages. According to the area of data streaming, such a stream is an event stream. Processes of this event type have a RECEIVE operator and are able to reply to the invoking client. – External events and scheduled time events: Processes are initiated in dependence on a time-based schedule or by external schedulers. These processes do not have a RECEIVE operator and are not able to reply synchronously to an invoking client. An XML representation of the conceptual MTM was defined. Thus, the external XML msg1/pname = ’sp_runMovementDataCleansing’ representation of the PIM can Service{ cs1.cdb.DBA }; Operation{ CALL }; IN msg1/pname; be transformed into the XML msg2/tblname1 = ’Orders’ representation of the A-PSM msg2/tblname2 = ’Orderline’ using transformation templates. Service{ cs1.cdb.DBA }; Operation{ QUERY }; IN msg2/tblname1; OUT msg3/dataset Figure 2 shows the A-PSM Service{ cs1.cdb.DBA }; Operation{ QUERY }; P13, derived from the PIM IN msg2/tblname2; OUT msg4/dataset P13. Basically, the MTM is msg3/tblname = ’Orders’ msg4/tblname = ’Orderline’ message-based and thus uses Service{ cs1.dwh.DBA }; Operation{ INSERT }; message variables instead of IN msg3/tblname, msg3/dataset; the explicit data flow used in Service{ cs1.dwh.DBA }; Operation{ INSERT }; IN msg4/tblname, msg4/dataset; the PIM. Furthermore, detailed parameters like table names and stored procedure names Fig. 2. Example A-PSM P13 have to be assigned to the input messages. That is why the logical sequence is extended with several ASSIGN operators. In contrast to this, the interaction-oriented operator INVOKE is simply mapped. Finally, note that technical details, like schema mappings and configuration properties, can be annotated on the PIM as well as the A-PSM level, while all following models are generated in a fully automated manner. 4.2 PSM Generation From the unique A-PSM, multiple platform-specific models (PSM), including PSMs for FDBMS, ETL tools and EAI servers, could be generated. For this transformation, the defined specific platform models (PM) are used. Here, a PM represents a meta model for an integration system type, like the type ETL tool. We describe in detail only the PM for FDBMS, including the resulting PSM, while the integration system types ETL tools and EAI servers are only discussed from a high-level perspective. Federated DBMS PSM. The PSM for federated DBMS comprises structural as well as semantic differences to the A-PSM (MTM). In contrast to the MTM, it is rather hierarchically structured and not a graph-based model. Thus, some structured components recursively include other structured components as well as atomic components. Furthermore, the two different process-initiating event types are expressed with two different possibilities for the root component. First, there is the Trigger, which is bound to a queuing table and represents the event type message stream. Second, there is the

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M. Böhm et al. Table 1. Interaction-Oriented Operators

Name Call Insert Delete Update Resource Scan

Description Invoke a persistently stored module (e.g., procedures, functions) Load a dataset into a specified relation Delete a specified dataset Set a specified attribute value Extract a dataset from a specified relation Table 2. Control-Flow-Oriented Operators

Name If Delay Signal Iteration

Description Execute all path children if the path condition is true Interrupt the execution based on a timestamp or for a specified time Terminate the integration process or raise a defined error Loop over all children while the specified condition is true Table 3. Data-Flow-Oriented Operators

Name Assign Translation Selection Projection Join Setoperation Split OrderBy GroupBy Window Validate

Description Value assignment of atomic or complex objects (different query language) Execution of elementary schema translations Choice of tuples in dependence on a specific condition Choice of attributes in dependence on a specific attribute list Compound of multiple datasets depending on conditions and types Use of the set operations union, intersection and difference Decomposition of a large XML document into multiple rows Sorting of a dataset depending on a specified attribute Partitioning of tuples with grouping attributes and aggregate functions Partitioning of tuples for ordering and correlations, without grouping Constraint validation on a dataset depending on a specific condition Table 4. Transaction-Oriented Operators

Name BeginTX CommitTX RollbackTX Savepoint

Description Start a new transactional context End the current transactional context in a successful way End the current transactional context in a failed way Write intermediate results for recovery processing

Procedure, representing the event type external events and scheduled time events, where no data-intensive parameters are specified for such a procedure. Both of these root component types implicitly include the handling of the transactional context. Before revisiting our used example, the components of the PM FDBMS s hould be mentioned. Basically, they are distinguished into four groups: interaction-oriented operators, control-flow-oriented operators, data-flow-oriented operators and transactionoriented operators. These groups are explained in detail by Tables 1 to 4.


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Using the introduced platform model, the PSM FDBMS is derived from the A-PSM. Figcs1.dwh.DBA. cs1.dwh.DBA. cs1.cdb.DBA. Orders Orderline sp_runMovement ure 3 shows this PSM FDBMS DataCleansing representation of the example cs1.cdb.DBA. cs1.cdb.DBA. process P13. The process is Orders Orderline transformed into the hierarFig. 3. Example FDBMS PSM P13 chical structure Procedure. Here, a BeginTX is implicitly inserted at BOT and a CommitTX is inserted at EOT. The first invoke and all correlated properties are represented by the Call element. Finally, there are two query trees, constructed of Insert and Resource Scan elements, which copy the two movement data relations from the consolidated database to the data warehouse. ETL Tool PSM. Similar to the first mentioned PSM, the PSM for ETL tools has some semantic differences to the MTM. This platform model is based on tuple routing between data-flow-oriented process steps, where the edges between these steps contain— on the conceptual layer—queues for buffering of tuples. Furthermore, it is an acyclic model, which is typical for the concentration on data flow semantics. In our generic platform model of ETL tools, no special process steps are included directly in the model because they are almost proprietary definitions. Using the mentioned platform model, the introduced example A-PSM P13 can be transformed to the ETL PSM. The derived model includes specialized process steps, where these are specific to the used source and target systems. Further, the parameter specifications are directly included in the used steps. This approach promises high performance but causes a higher data dependence than an EAI server would do. EAI Server PSM. Many commercial EAI servers use XML technologies and standard workflow languages like WSBPEL [10] or XPDL [13] for integration process specification. This abstract definition and the well-known adapter concept realize the needed data independence. Thus, the derivation of the EAI server PSM implies the mapping from A-PSM process types to WSBPEL process descriptions. Although this is a very easy mapping for interaction and control flow operators, it is recommended that the specific target integration systems support the WSBPEL extension WSBPEL-MT. Otherwise, the data-flow-oriented operators defined for the MTM could not be used. However, for the example process P13, this extension support is not required because interactionoriented operators, control-flow-oriented operators and the data-flow-oriented operator Assign are used exclusively. 4.3 DPD Generation In contrast to the MDA paradigm, where the lowest representation level is the Code layer, we name the lowest—MDA-relevant—level of the integration process representation the Declarative Process Description (DPD). This decision was made in order to distinguish (1) the integration process specifications (DPD) deployed in the specific integration system and (2) the actual generated integration code, internally produced by the integration system. However, the mentioned process descriptions are usually

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specified in a declarative way because this leaves enough space for physical optimization techniques. Let us face the concrete DPDs for federated DBMS, ETL tools and WSBPEL-conform EAI servers. In case of FDBMS, the PSM P13 is generated in a DPD, represented by several DDL statements and an SQL stored procedure. In contrast to that, P02 is generated in the form of several DDL statements and a trigger due to the different event type. Both examples are included in appendix A. The majority of ETL tools work based on XML specifications, which may be directly used or specified by an additional GUI. Also, the EAI server works similar to this, except for the standardized language specification. Further, there are two main types of XML specification usage for these two integration system types. Some tools statically generate imperative code in the form of internal execution plans. Other tools dynamically interpret the XML specifications, where created object graphs are used. The decision is made based on performance aspects as well as flexibility requirements.

5 Open Issues and Challenges Due to the complexity of model-driven generation, there are major challenges and open research issues to be solved. Basically, we see the following five points: – Schema mapping extraction: We extract schema mapping information from XSLT and STX stylesheets. Due to the complex functionality of these languages, there are cases, where schema mapping extraction cannot be realized. Hence, sophisticated techniques for the schema mapping generation have to be developed, including levels of uncertainty when no exact information can be provided. – Round-trip engineering: Our approach works in a top-down direction, so all changes should be made on the PIM or the A-PSM level. In order to support incremental changes and the migration of legacy integration tasks, reverse engineering is advantageous. However, there are lots of challenges correlated to this issue. – Intra-system process optimization: The model-driven generation leaves enough space for logical process optimization, rewriting process plans with rule-based and workload-based optimization techniques. – Inter-system process optimization: Aside from the aforementioned optimization, the most powerful global optimization technique is the decision on the optimal integration system (w.r.t. performance). Thus, the major challenge is the workloadbased decision on the chosen PSM and DPD. – Supporting different execution models: There are integration system types which have a completely different execution model (e.g., subscription systems like replication servers) and thus, supporting those, creates some challenges.

6 Summary and Conclusion In this paper, we addressed two major problems within the area of integration processes. First, there was the problem of a low degree of portability. Second, large efforts were needed in order to set up and maintain integration scenarios. We conceptually showed (and evaluated with the implemented GCIP Framework) that a model-driven generation approach can dramatically reduce these two problems and is thus, advantageous for this


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application context as well. However, there are two more major problems and a lot of open research challenges. In our future work, we will focus on the logical optimization of integration processes using our model-driven generation approach.

References 1. Kleppe, A., Warmer, J., Bast, W.: MDA Explained. The Model Driven Architecture: Practice and Promise. Addison-Wesley, Reading (2003) 2. Thomas, D., Barry, B.M.: Model driven development: the case for domain oriented programming. In: OOPSLA (2003) 3. OMG: Meta-Object Facility (MOF), Version 2.0 (2003) 4. OMG: Unified Modeling Language (UML), Version 2.0 (2003) 5. Königs, A.: Model transformation with triple graph grammars. In: MODELS (2005) 6. Czarnecki, K., Helsen, S.: Feature-based survey of model transformation approaches. IBM Syst. J. 45(3) (2006) 7. Habich, D., Richly, S., Lehner, W.: Gignomda - exploiting cross-layer optimization for complex database applications. In: VLDB (2006) 8. Richly, S., Habich, D., Lehner, W.: Gignomda - generation of complex database applications. In: Grundlagen von Datenbanken (2006) 9. OMG: Common Warehouse Metamodel (CWM), Version 1.0 (2001) 10. OASIS: Web Services Business Process Execution Language Version 2.0 (2006) 11. BMI: Business Process Modelling Notation, Version 1.0 (2006) 12. W3C: Web Service Choreography Description Language, Version 1.0 (2005) 13. WfMC: Process Definition Interface - XML Process Definition Language 2.0 (2005) 14. OASIS: ebXML Business Process Specification Schema, Version 2.0.1. (2005) 15. Dorda, C., Heinkel, U., Mitschang, B.: Improving application integration with model-driven engineering. In: ICITM (2007) 16. Simitsis, A., Vassiliadis, P.: A methodology for the conceptual modeling of ETL processes. In: CAiSE workshops (2003) 17. Trujillo, J., Luján-Mora, S.: A UML Based Approach for Modeling ETL Processes in Data Warehouses. In: Song, I.-Y., Liddle, S.W., Ling, T.-W., Scheuermann, P. (eds.) ER 2003. LNCS, vol. 2813, pp. 307–320. Springer, Heidelberg (2003) 18. Vassiliadis, P., Simitsis, A., Skiadopoulos, S.: Conceptual modeling for ETL processes. In: DOLAP (2002) 19. Hahn, K., Sapia, C., Blaschka, M.: Automatically generating OLAP schemata from conceptual graphical models. In: DOLAP (2000) 20. Mazón, J.N., Trujillo, J., Serrano, M., Piattini, M.: Applying mda to the development of data warehouses. In: DOLAP (2005) 21. Simitsis, A.: Mapping conceptual to logical models for ETL processes. In: DOLAP (2005) 22. Dessloch, S., Hernandez, M.A., Wisnesky, R., Radwan, A., Zhou, J.: Orchid: Integrating schema mapping and ETL. In: ICDE (2008) 23. Melnik, S., Rahm, E., Bernstein, P.A.: Rondo: A programming platform for generic model management. In: SIGMOD (2003) 24. Göres, J., Dessloch, S.: Towards an integrated model for data, metadata, and operations. In: BTW (2007) 25. Böhm, M., Habich, D., Wloka, U., Bittner, J., Lehner, W.: Towards self-optimization of message transformation processes. In: ADBIS (2007) 26. Böhm, M., Habich, D., Lehner, W., Wloka, U.: Dipbench: An independent benchmark for data intensive integration processes. In: IIMAS (2008)

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27. Böhm, M., Habich, D., Lehner, W., Wloka, U.: Dipbench toolsuite: A framework for benchmarking integration systems. In: ICDE (2008) 28. Rahm, E., Do, H.H.: Data cleaning: Problems and current approaches. IEEE Data Eng. Bull. (2000)

A Example FDBMS DPD EXEC s p a d d s e r v e r cs1 , . . . EXEC s p a d d e x t e r n l o g i n cs1 , . . . CREATE PROXY TABLE rOrdersCDB EXTERNAL TABLE CREATE PROXY TABLE r O r d e r l i n e C D B EXTERNAL TABLE CREATE PROXY TABLE rOrdersDWH EXTERNAL TABLE CREATE PROXY TABLE r O r d er l i n eDWH EXTERNAL TABLE

AT AT AT AT

” c s 1 . cd b .DBA. ” c s 1 . cd b .DBA. ” c s 1 . dwh .DBA. ” c s 1 . dwh .DBA.

Orders ” O r d e r l i n e” Orders ” O r d e r l i n e”

CREATE PROCEDURE P13 AS BEGIN BEGIN TRANSACTION EXEC c s 1 . cd b .DBA. s p r u n M o v e m e n t D a t a C l e a n s i n g INSERT INTO rOrdersDWH SELECT ∗ FROM rOrdersCDB INSERT INTO r O r d er l i n eDWH SELECT ∗ FROM r O r d e r l i n e C D B COMMIT TRANSACTION END

Listing 1.1. Example FDBMS DPD P13 ... CREATE TRIGGER P02 ON P0 2 Qu eu e FOR INSERT AS BEGIN DECLARE @cu st o mer k ey BIGINT , ... @sitekey INTEGER , . . . BEGIN TRANSACTION SELECT @t i d = (SELECT TID FROM i n s e r t e d ) SELECT @i = 1 SELECT @xpath = ” / / c u s t o m e r [ 1 ] ” WHILE NOT (SELECT x m l e x t r a c t ( @xpath , (SELECT c o n v e r t (VARCHAR( 1 6 3 8 4 ) ,MSG) FROM P0 2 Qu eu e WHERE TID= @t i d ) RETURNS VARCHAR( 1 6 3 8 4 ) ) ) IS NULL BEGIN SELECT @rowptr = x m l e x t r a c t ( @xpath , ( SELECT c o n v e r t (VARCHAR( 1 6 3 8 4 ) ,MSG) FROM P0 2 Qu eu e WHERE TID= @t i d ) RETURNS VARCHAR( 1 6 3 8 4 ) ) SELECT @cu st o mer k ey = x m l e x t r a c t ( ’ / c u s t o m e r / @Customerkey ’ , @rowptr RETURNS BIGINT ) , @l ast n ame = x m l e x t r a c t ( ’ / c u s t o m e r / @Lastname ’ , @rowptr RETURNS VARCHAR( 4 0 ) ) , ... @ l a s t m o d i f i e d = x m l e x t r a c t ( ’ / c u s t o m e r / @Last Mo d i f i ed ’ , @rowptr RETURNS DATETIME) IF ( @ s i t e k e y = 2 OR @ s i t e k e y = 3 ) BEGIN IF NOT EXISTS ( SELECT 1 FROM rCompanyBP WHERE Companykey=@companykey ) BEGIN INSERT INTO rCompanyBP ( Companykey , Name , I m p o r t a n c e F l a g ) VALUES ( @companykey , @companyname , 1 ) END INSERT INTO r C u st o mer B P ( C u st o mer k ey , Last n ame , F i r s t n a m e , A d d r e s s S t r i n g , Zi p co d e , S i t e k e y , Phone1 , Phone2 , Companykey , B i r t h d a y , C r e a t e d , L a s t M o d i f i e d ) VALUES ( @customerkey , @lastname , @ f i r s t n a m e , @ad d r ess , @zip , @si t ek ey , @phone1 , @phone2 , @companykey , @b i r t h d ay , @cr eat ed , @ l a s t m o d i f i e d ) END ELSE IF ( @ s i t e k e y = 4 ) BEGIN IF NOT EXISTS ( SELECT 1 FROM rCompanyT WHERE Companykey=@companykey ) BEGIN INSERT INTO rCompanyT ( Companykey , Name , I m p o r t a n c e F l a g ) VALUES ( @companykey , @companyname , 1 ) END INSERT INTO r C u st o mer T ( C u st o mer k ey , Last n ame , F i r s t n a m e , A d d r e s s S t r i n g , Zi p co d e , S i t e k e y , Phone1 , Phone2 , Companykey , B i r t h d a y , C r e a t e d , L a s t M o d i f i e d ) VALUES ( @customerkey , @lastname , @ f i r s t n a m e , @ad d r ess , @zip , @si t ek ey , @phone1 , @phone2 , @companykey , @b i r t h d ay , @cr eat ed , @ l a s t m o d i f i e d ) END SELECT @i = @i + 1 SELECT @xpath = ” / / c u s t o m e r [ ” || c o n v e r t (VARCHAR( 2 0 ) , @i ) || ” ] ” END DELETE FROM P0 2 Qu eu e WHERE TID = @t i d COMMIT TRANSACTION END

Listing 1.2. Example FDBMS DPD P02

Towards a Metrics Suite for Object-Relational Mappings Stefan Holder1,*, Jim Buchan2, and Stephen G. MacDonell2 1

Max-Planck-Institut für Informatik, Campus E1 4, 66123 Saarbrücken, Germany [email protected] 2 School of Computing and Mathematical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand {jbuchan,smacdone}@aut.ac.nz

Abstract. Object-relational (O/R) middleware is frequently used in practice to bridge the semantic gap (the ‘impedance mismatch’) between object-oriented application systems and relational database management systems (RDBMSs). If O/R middleware is employed, the object model needs to be linked to the relational schema. Following the so-called forward engineering approach, the developer is faced with the challenge of choosing from a variety of mapping strategies for class associations and inheritance relationships. These mapping strategies have different impacts on the characteristics of application systems, such as their performance or maintainability. Quantifying these mapping impacts via metrics is considered beneficial in the context of O/R mapping tools since such metrics enable an automated and differentiated consideration of O/R mapping strategies. In this paper, the foundation of a metrics suite for objectrelational mappings and an initial set of metrics are presented. Keywords: Object-oriented software development; Relational database; Impedance mismatch; Object-relational mapping; Software metrics.

1 Introduction Applications designed using object-oriented (OO) principles often achieve persistence of the object model using a relational database management system (RDBMS). This introduces a so-called ‘impedance mismatch’ between an application based on an OO design paradigm and an RDBMS designed according to quite different principles from relational theory. Object-relational (O/R) middleware is often used to bridge this semantic gap by providing a mechanism to map the object model to relations in the database. This layered approach to O/R mapping using middleware achieves the design goal of loose coupling between application and relational schema, making it possible to change the relational schema without the need to also change the application source code. If O/R middleware is employed in this way, the object model needs to be linked to the relational schema using the mapping mechanism offered by the O/R middleware. In the case where a developer creates the object model first and then creates the relational *

The work reported here was conducted primarily while the first author was at Auckland University of Technology on exchange from Fulda University of Applied Sciences, Germany.

R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 43–54, 2008. © Springer-Verlag Berlin Heidelberg 2008

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schema by mapping the object model to relations, the so-called forward engineering approach, the developer is faced with the challenge of choosing from a variety of mapping strategies for class associations and inheritance relationships. These mapping strategies have different impacts on the non-functional properties of application systems, such as performance and maintainability [9, 11]. The problem for the developer, then, becomes selecting the mapping strategy that best suits the desired non-functional requirement priorities for a particular application. There are a number of approaches to addressing this problem and there is some tool support to automate the selection and generation of the mapping in the O/R middleware. In [8], for example, a single inheritance mapping strategy is used and a fixed mapping strategy for one-to-one, one-to-many, and many-to-many associations is applied, respectively. This approach is straightforward; however, the different impacts of alternative mapping strategies are not considered. Philippi [11] therefore suggests a model-driven generation of O/R mappings. In his approach, the developer defines mapping requirements in terms of quality trade-offs. The mapping tool then automatically selects mappings that fulfill the specified requirements based on general heuristics regarding the impacts of mapping strategies. However, mapping impacts strongly depend on concrete object schema characteristics such as inheritance depth and the number of class attributes [9, 13], properties not considered in [11]. Furthermore, while model-driven generation of O/R mappings is said to ease mapping specification for the developer, such an approach reduces the developer’s control over the mapping process and may result in a sub-optimal mapping for a given application. The developer should therefore have the option to define and refine mappings manually when required. We therefore suggest that concrete schema characteristics such as inheritance depth and the number of class attributes should be considered in the selection of O/R mappings. We propose an approach that incorporates schema characteristics into metrics that will provide more accurate and sensitive measures of the impacts of a given mapping specification on non-functional requirements. We further suggest the application of metrics for O/R mappings in order to give the developer sophisticated feedback on the impacts of the chosen mapping. The quantification of mapping impacts using metrics is considered beneficial in the context of O/R mapping tools since these metrics provide a semi-automated mechanism for a developer to evaluate the appropriateness of a selected mapping in terms of its likely impact on desired application non-functional requirements such as performance and maintainability. As its main contribution, this paper provides the foundation for a metrics suite for inheritance mappings and defines several initial metrics. While it is feasible to measure the impact of both association and inheritance mapping strategies with O/R metrics, we focus here on metrics for inheritance mapping strategies, for two reasons. First, the three basic inheritance mapping strategies (described in the following section) are applicable to all inheritance relationships, so the developer will always be required to choose one of them for each inheritance relationship. This is in contrast to association mapping strategies, whose applicability depends on the cardinality of the association, and so sometimes there is only limited choice or even no choice. Second, the impacts of inheritance mapping strategies strongly depend on the characteristics of the object model, such as inheritance depth and number of attributes. Thus, the drivers for the application of O/R metrics for inheritance relationships are more compelling.

Towards a Metrics Suite for Object-Relational Mappings

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The remainder of this paper is structured as follows. In Section 2, basic strategies for mapping entire inheritance hierarchies are described. In Section 3, the semantics of mapping strategies for individual inheritance relationships are defined. The goals of measurement are set in Section 4 before an initial metrics suite for inheritance mappings is proposed in Section 5, based on the defined inheritance mapping semantics. In Section 6, the coverage of the proposed metrics suite is explained. Finally, a conclusion to this paper is given in Section 7.

2 Basic Strategies for Mapping Inheritance Hierarchies In the literature, inheritance mapping strategies are generally described as being applicable to whole class hierarchies. These ‘pure’ mapping strategies are explained in this section using the notation suggested in [9]. 2.1 One Class - One Table Following the ‘one class – one table’ mapping strategy, there is a one-to-one mapping between classes and tables. The corresponding table of a class contains a relational field for each non-inherited class attribute. Thus, object persistence is achieved by distributing object data over multiple tables. In order to link these tables, all tables share the same primary key. In addition, the primary keys are also foreign keys that mimic the inheritance relationships of the object schema [9]. Each row in a table, then, maps to objects in that table’s corresponding class and subclasses. It can be seen that for this mapping strategy, only one table needs to be accessed in order to identify the objects that match the query criteria of polymorphic queries. Whereas a non-polymorphic query only returns the objects of the class against which the query is issued, a polymorphic query returns the objects of the specified class and its subclasses for which the query criteria match. This mapping strategy is commonly recommended if the object model’s changeability (i.e. ability to easily change) is of primary importance because new classes can be added easily, without the need to modify existing tables. A significant drawback of this mapping strategy, however, is that multiple joins are needed to assemble all attribute data of an object. Moreover, if no views are used, it is relatively difficult to formulate ad-hoc queries because multiple tables need to be accessed to retrieve all object data [9]. 2.2 One Inheritance Tree – One Table Following the ‘one inheritance tree – one table’ mapping strategy, all classes of an inheritance hierarchy are mapped to the same relational table. This mapping strategy requires an additional relational field in the shared table, which indicates the type of each row. This mapping strategy offers the best performance for polymorphic queries and allows easy ad-hoc reporting since only a single table needs to be accessed [9]. The changeability of the object model is reduced compared to the ‘one class – one table’ mapping strategy, however, because any object schema modification forces a modification of the sole inheritance table, which may already contain data. Finally, if objects are stored using this mapping strategy, all relational fields that are not needed to store an object must contain null values. This especially applies to

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relational fields corresponding to attributes of subclasses since these fields are not used when storing objects from other subclasses. 2.3 One Inheritance Path – One Table The ‘one inheritance path - one table’ mapping strategy only maps each concrete class to a table. Thereby, all inherited and non-inherited attributes of a class are mapped to the same table. Each table then only contains instances of its corresponding concrete class. Under this mapping strategy non-polymorphic queries run as quickly as they do under the ‘one inheritance tree – one table’ mapping strategy because only one table needs to be accessed. In contrast, a polymorphic query against a class needs to access the table that corresponds to this class and all tables that correspond to its subclasses, which could result in a significant performance overhead. Moreover, this mapping strategy implies a duplication of relational fields, which results in multiple updates of these fields if the corresponding class attribute is changed. The three inheritance mapping strategies just described are restrictive in that they are only applicable to entire inheritance hierarchies. In the next section, we introduce more finely-grained inheritance mapping strategies that can be used in combination on elements of an inheritance hierarchy in order to produce an optimal mapping.

3 Semantics of Mapping Strategies for Individual Inheritance Relationships In practice, current O/R middleware products such as Hibernate [6] support the mixing of different inheritance mapping strategies for one inheritance hierarchy, thereby providing a finer level of granularity in the mapping strategy selection than the basic mapping strategies described in Section 2. However, the opportunity to mix inheritance mapping strategies means that the developer must decide what mix of mapping strategies will be optimum for a given object model structure. This requirement strengthens the likely usefulness of a set of mapping metrics that reflect this finer granularity. Hence, the ability to use such a suite of metrics to inform this decision, as proposed by this paper, should ease the developer’s effort and result in a better quality decision. Before discussing the development of these metrics a method of clearly representing the semantics of individual inheritance mapping strategies is needed. The following notation will be used and follows the inheritance mapping model definitions suggested by [4]. In the following definitions, P denotes the superclass (parent class) at the superclass-end of an example inheritance relationship while C denotes the subclass (child class) at the subclass-end of this inheritance relationship. Union superclass: If UC denotes the set of classes that are reachable from C (including C) via ‘union superclass’ inheritance relationships, the attributes defined by all classes in UC are mapped to a table corresponding to the most general class in UC. Moreover, a type indicator field is needed in this table in order to determine the class type of each row. Joined subclass: The attributes defined by C as well as the primary key attributes inherited from P or another superclass are mapped to an own table T. The primary key fields of T contain a foreign key constraint to the same primary key fields in the table to which P is mapped.


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Union subclass: If C is abstract and all inheritance relationships to its direct subclasses are mapped with ‘union subclass’ (or C does not have any subclasses), then no corresponding table is created for C. Otherwise, the attributes of C and the attributes of all superclasses of C are mapped to an own table. Figure 1, adapted from an example in [4], shows a sample mapping that represents the above definitions. In it, white boxes denote classes and grey boxes denote tables. The mapping from classes to tables is indicated with black arrows and inheritance relationships (white arrows) are labeled with the mapping strategies applied to them. Having introduced the semantics of mapping strategies for individual inheritance relationships, in the next section we describe the derivation of measurement goals relevant to the assessment of the impact of these mapping strategies. Person

person

id name

union subclass

union superclass

student id name university

Student university

Employee salary department

foreign key

manager union subclass id name salary department bonus

id name salary department type

joined subclass clerk

Manager bonus

Clerk occupation

id occupation

Fig. 1. Example of mixed inheritance mapping strategies

4 Measurement Goals Bearing in mind that different mapping strategies have different impacts in terms of the non-functional characteristics of applications, our intent in this section is to identify measures that would be useful in guiding the developer’s choice of mapping. In order to identify appropriate metrics, we follow a simplified variant of the commonly employed Goal/Question/Metric (GQM) approach [2, 3]. The GQM approach defines a framework for identifying metrics by defining goals, asking questions related to how these goals could be achieved and defining metrics that are intended to answer the posed questions. In the first step of identifying relevant measurement goals, we consider software quality (non-functional) characteristics that are influenced by O/R mappings. The result of our top-down approach to identifying software quality characteristics for O/R mappings is depicted in Figure 2.

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S. Holder, J. Buchan, and S.G. MacDonell Quality characteristics according to ISO/IEC 9126

Refined quality characteristics

DB queries Time behaviour Efficiency Resource utilisation

Secondary storage

Polymorphic queries Non-polymorphic queries DB inserts and updates Additional null values Redundancy Change propagation

Changeability

Change isolation Stability

Maintainability

Mapping understandability

Analysability

Usability

Operability

Ad-hoc queries

Constraint assurance Mapping uniformity Schema correspondence Query complexity

Fig. 2. Quality characteristics of O/R mappings Table 1. Description of quality characteristics Quality characteristic Time behavior of polymorphic database queries Time behavior of non-polymorphic database queries Time behavior of database inserts and updates Additional null values Redundancy Change propagation

Change isolation Constraint assurance Mapping uniformity

Schema correspondence Query complexity

Description Mainly depends on the number of tables that need to be accessed / the number of joins that need to be performed. Higher redundancy can improve the time behavior of polymorphic database queries but can negatively affect the time behavior of database inserts and updates [9]. Null values that solely result from the applied mapping strategy The degree of redundancy that is caused by the applied mapping strategy Extent to which it is necessary to adapt the relational schema and the O/R mappings to changes in the object model Depends on whether existing tables need to be modified for adding/deleting classes Depends on the ability of the relational schema to enforce integrity constraints Uniformity of applied mapping strategies. Refers to the overall mapping and is not applicable to individual mapping strategies Extent to which the object model resembles the relational schema Effort required to formulate ad-hoc queries


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This network of quality characteristics subsumes and extends the classifications of O/R mapping impacts suggested by [9, 11]. It starts with the quality characteristics efficiency, maintainability, and usability, which are a subset of the software quality characteristics defined by the ISO/IEC 9126-1 standard [7]. The other high-level software quality characteristics of the ISO/IEC 9126-1 standard – functionality, reliability, and portability – are not considered to be significantly influenced by O/R mappings, and so are not included here. Efficiency, maintainability, and usability are then split into quality sub-characteristics, also defined by the ISO/IEC 9126-1 standard. Finally, these sub-characteristics are refined into specific quality characteristics relevant to O/R mappings. In Section 5, we propose metrics to measure these specific quality characteristics. In Table 1, the specific quality characteristics are listed and explained. This builds on the works of Keller [9] and Philippi [11] by considering additional characteristics, namely Redundancy, Change isolation, Constraint assurance, and Mapping uniformity, and we further extend their work by proposing metrics for some of these. The next section describes and justifies the metrics developed for these quality characteristics and provides examples of their use.

5 Metrics for Inheritance Mappings Metrics have been suggested for object-oriented design [5] and for relational database systems [12] as well as for object-relational database systems [1]. These metrics, while useful, are considered insufficient for measuring the impacts of O/R mappings for two reasons. First, the available metrics for relational schemas do not sufficiently cover the suggested network of quality characteristics (see Section 4). Second, the metrics focus on either object-oriented design or relational schemas but not on the mapping between them. Therefore, the metrics suite suggested here, comprising four metrics at the level of individual classes, is complementary to those described elsewhere, in that it explicitly addresses the measurement of O/R mappings. 5.1 Table Accesses for Type Identification (TATI) Polymorphic queries against a class C return objects whose most specific class is C or one of its subclasses. Before an object can be completely retrieved, it is necessary to identify the most specific class of this object. It should be noted that identifying the most specific class is equivalent to identifying the tables that need to be queried in order to retrieve the requested object. In contrast, for non-polymorphic queries, the most specific class of the requested object is the same class against which the query is issued. There are two strategies to identify the most specific class: either each possible table is queried individually and the search is stopped as soon as the most specific class is identified, or all possible tables are queried with one query. While the former strategy means that the search is completed as soon as the most specific class is found, the latter strategy allows the database system to query the tables in parallel. The latter strategy can be accomplished by sending multiple queries to the database at the same time or by using the SQL UNION clause. The maximum number of table accesses measured with this metric is therefore only relevant if the former strategy is applied.

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Definition: For queries that are issued against a class C, TATI(C) equals the maximum number of tables that have to be accessed in order to identify the most specific class of the requested object. The maximum number of tables that need to be accessed for a query issued against a class C equals the number of different tables that correspond to C and all of its subclasses. In Figure 1, TATI(Person) = 4 since Person is the root class of the inheritance hierarchy and the inheritance hierarchy is mapped to 4 tables altogether. Similarly, TATI(Employee) = 3 since Employee and its subclasses Manager and Clerk are mapped to the 3 tables person, manager, and clerk. 5.2 Number of Corresponding Tables (NCT) In contrast to polymorphic queries, the most specific class of a queried object in a nonpolymorphic query is known. Therefore, no queries are necessary in this case to determine the most specific class. The performance of non-polymorphic queries therefore mainly depends on the number of tables that contain data of the requested object. For polymorphic queries, it is possible to retrieve the complete object data while querying tables in order to identify the most specific class of the requested object. We therefore propose the metric NCT(C), which equals the number of tables that contain data from instances of a class C. This number depends on the inheritance mapping strategies that are used for the inheritance relationships on the path of class C to the root class of the inheritance hierarchy. In particular, the application of the ‘joined subclass’ strategy results in increasing values of NCT. As already indicated, this metric is a measure of object retrieval performance. In addition, this metric is a measure of query complexity in the context of ad-hoc queries since we consider the number of involved tables to be an appropriate measure of the user’s effort in formulating a query. However, using the number of tables to measure ad-hoc queries assumes that no views are employed to ease query formulation. Definition: NCT is formally defined by equation (1), where the function p(C) returns the direct superclass of C. if C is the root class or ⎧1 ⎪ ↑ (p(C ), C ) is mapped via ' union subclass' (1) ⎪ NCT (C ) = ⎨ if C is mapped to the same table as its superclass ⎪ NCT ( p (C )) ⎪⎩ NCT ( p (C )) + 1 if C is mapped to its own table

In Figure 1, NCT(Clerk) = 2 because the tables clerk and person contain data that are necessary to assemble objects of Clerk. In contrast NCT(Manager) = 1, as all defined and inherited attributes of the class Manager are mapped to relational fields of the table manager. 5.3 Number of Corresponding Relational Fields (NCRF) The Number of Corresponding Relational Fields (NCRF) gives a measure for the degree of change propagation for a given O/R mapping. More specifically, this metric reflects the effort required to adapt the relational schema after inserting, modifying, or deleting a class attribute. This effort is mainly influenced by the application of the ‘union subclass’


51

mapping strategy since applying this mapping strategy typically results in the duplication of relational fields (see Section 2.3). Because of these duplications, changes in the object model result in multiple changes to the relational schema. In contrast, the duplication of relational fields does not occur when the ‘joined subclass’ or the ‘union superclass’ mapping strategies are applied. (Note: primary key fields are not considered by this metric because they should be resistant to changes.) Definition: For a class C, NCRF(C) equals the number of relational fields in all tables that correspond to each non-inherited non-key attribute of C. If C does not have any noninherited non-key class attributes, NCRF(C) equals the number of relational fields to which each non-inherited non-key class attribute of C would be mapped. In Figure 1, NCRF(Person) = 3 since the class attribute Person.name is mapped to the three relational fields person.name, student.name, and manager.name. NCRF(Employee) = 2 since each of the attributes Employee.salary and Employee.department is mapped to one relational field in the table person and one relational field in the table manager. Finally, NCRF(Student) = NCRF(Manager) = NCRF(Clerk) = 1 since the only noninherited class attribute of each of these three classes (Student.university, Manager.bonus, Clerk.occupation) is mapped to 1 relational field. 5.4 Additional Null Values (ANV) ANV measures additional storage space in terms of null values that result when different classes are stored together in the same table using the ‘union superclass’ mapping strategy (see Section 2.2). For a definition of ANV(C), the following is considered. Let AC be the set of noninherited attributes of class C and let FC be the set of corresponding relational fields in the shared table. Applied to a particular class C, the aim of ANV(C) is to give a measure for the number of null values that occur at the relational fields FC. An important observation is that null values at the relational fields FC occur if and only if instances of classes different from C and different from subclasses of C are stored in the shared table. More precisely, if instances of two distinct classes B and C are stored together in a shared table and B is not a subclass of C, then each row in the shared table that represents an instance of B contains a null value at each relational field in FC. ANV(C) is therefore higher the more classes different from C and different from subclasses of C are mapped to the same table as C. The number of null values depends on the number of instances of each class, something that may be unknown at the stage of mapping specification. In order to give an approximation of additional null values, it is assumed that there is the same number of instances for all (concrete) classes. Furthermore, ANV is normalized by assuming that there is only one instance of each class. This assumption also ensures that ANV only depends on a given object model and is thus in line with the previously described metrics. Note, however, that this metric could easily be generalized to take the number of instances per class into account if this is known. Definition: ANV(C) equals the number of non-inherited attributes in C multiplied by the number of concrete classes that are mapped to T, excluding C and all of its subclasses.

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person «abstract» Person

id name university majorSubject isEnrolled salary occupation type

id name

union superclass

union superclass

Student

Employee

university majorSubject isEnrolled

salary

joined subclass

union superclass

manager Manager

id bonus

bonus

Clerk occupation

Fig. 3. Example mapping to illustrate the ANV metric

For the example mapping shown in Figure 3, ANV(Student) is calculated as follows. Concrete classes that are mapped to the same table as Student are Employee and Clerk. These two classes do not inherit the attributes declared by Student; therefore, additional null values are contained by rows that correspond to instances of Employee and Clerk. If one instance for each class was stored, in total there would be 2·3 = 6 null values in the rows of the table person at the fields university, majorSubject, and isEnrolled. Thus, ANV(Student) = 6.

6 Metric Coverage Table 2 shows the coverage of the defined quality characteristics by the proposed metrics. This table shows that more metrics are needed in order to more fully measure the relevant quality characteristics of mapping strategies. Table 2. Metric coverage for inheritance mapping strategies Quality characteristic Polymorphic queries Non-polymorphic queries DB inserts and updates Additional null values Change propagation Change isolation Constraint assurance Mapping uniformity Schema correspondence Query complexity

TATI X

NCT X X X

NCRF

ANV

X X

X


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The quality characteristic Redundancy is not included in the table since this characteristic is only applicable to association mapping strategies and not to inheritance mapping strategies [10]. It should be noted that although the ‘one class – one inheritance tree’ mapping strategy leads to a duplication of relational fields, it does not imply redundancy.

7 Conclusions and Further Research The application of metrics in O/R mapping tools provides significant potential for supporting the developer in the considerably difficult task of mapping specification. Developers would benefit from a facility that supports the manual specification of O/R mappings by giving feedback on the impacts of these mapping strategies. Since the impacts of mapping strategies strongly depend on the concrete characteristics of the object model, metrics are considered an appropriate means to convey these schema characteristics. Moreover, the adoption of O/R metrics in model-driven generation of O/R mappings should enable a more appropriate selection of mapping strategies that leads to better fulfillment of non-functional requirements. As mapping impacts differ particularly for inheritance mappings, we have focused on developing a set of metrics for inheritance mapping strategies. This metrics suite is based on a novel inheritance mapping model that supports the mixing of inheritance mapping strategies in inheritance hierarchies. We plan to empirically evaluate the suggested metrics in terms of their utility in giving feedback about the impacts of mapping strategies to developers. We will extend this further by investigating algorithms that automatically determine the appropriate mapping strategy based on the requirements of the developer. To achieve this goal, normalization of the metrics will be required to ensure that metric values can be weighted and compared appropriately. Finally, as object-relational database management systems (ORDBMSs) become increasingly prevalent, support for mapping from object-oriented programming languages to ORDBMS-schema also becomes more important. We will therefore further investigate how mapping specification for ORDBMS-schemas can be supported by the application of metrics.

References 1. Baroni, A.L., Calero, C., Piattini, M., Abreu, F.B.: A Formal Definition for ObjectRelational Database Metrics. In: 7th International Conference on Enterprise Information Systems (ICEIS 2005), Miami, pp. 334–339 (2005) 2. Basili, V.R., Caldiera, G., Rombach, H.D.: The Goal Question Metric approach. Encyclopedia of Software Engineering 1, 528–532 (1994) 3. Basili, V.R., Rombach, H.D.: The TAME project: Towards Improvement-Oriented Software Environments. IEEE Transactions on Software Engineering 14(6), 758–773 (1988) 4. Cabibbo, L., Carosi, A.: Managing Inheritance Hierarchies in Object/Relational Mapping Tools. In: Pastor, Ó., Falcão e Cunha, J. (eds.) CAiSE 2005. LNCS, vol. 3520, pp. 135– 150. Springer, Heidelberg (2005)

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5. Chidamber, S.R., Kemerer, C.F.: A Metrics Suite for Object Oriented design. IEEE Transactions on Software Engineering 20(6), 476–493 (1994) 6. Hibernate Open Source Software, http://www.hibernate.org 7. ISO/IEC 9126-1:2001 Software Engineering - Product Quality - Part 1: Quality Model, http://www.iso.org 8. Keller, A.M., Jensen, R., Keene, C.: Persistence Software: Bridging Object-Oriented Programming and Relational Databases. In: Proc. of the 1993 ACM SIGMOD Int. Conf. on Management of Data, Washington, D.C, pp. 523–528 (1993) 9. Keller, W.: Mapping Objects to Tables: A Pattern Language. In: Proc. of the 1997 European Conf. on Pattern Languages of Programming (EuroPLoP 1997), Irrsee, Germany (1997) 10. Oertly, F., Schiller, G.: Evolutionary database design. In: 5th International Conference on Data Engineering (ICDE), Los Angeles, pp. 618–624 (1989) 11. Philippi, S.: Model Driven Generation and Testing of Object-Relational Mappings. Journal of Systems and Software 77(2), 193–207 (2005) 12. Piattini, M., Calero, C., Genero, M.: Table Oriented Metrics for Relational Databases. Software Quality Journal 9(2), 79–97 (2001) 13. Rumbaugh, J., Blaha, M.R., Premerlani, W., Eddy, F., Lorensen, W.: Object-Oriented Modelling and Design. Prentice-Hall, Englewood Cliffs (1991)

View-Based Integration of Process-Driven SOA Models at Various Abstraction Levels Huy Tran, Uwe Zdun, and Schahram Dustdar Distributed Systems Group, Institute of Information Systems Vienna University of Technology, Austria {htran,zdun,dustdar}@infosys.tuwien.ac.at

Abstract. SOA is an emerging architectural style to achieve looselycoupling and high interoperability of software components and systems by using message exchanges via standard public interfaces. In SOAs, software components are exposed as services and typically coordinated by using processes which enable service invocations from corresponding activities. These processes are described in high-level or low-level modeling languages. The extreme divergence in term of syntax, semantics and levels of abstraction of existing process modeling languages hinders the interoperability and reusability of software components or systems being built upon or relying on such models. In this paper we present a novel approach that provides an automated integration of modeling languages at different abstraction levels using the concept of architectural view. Our approach is realized as a view-based reverse engineering tool-chain in which process descriptions are mapped onto appropriate high-level or low-level views, offered by a view-based modeling framework.

1

Introduction

In a Service-oriented Architecture (SOA), software components are often exposed as services that have standard interfaces and can be invoked by message exchanges. A number of relevant services can be coordinated to achieve a specific business functionality. The integration and interoperability of the software components or systems are accomplished by orchestrating them using a process, which is deployed in a process engine. Each process typically consists of a control flow, a number of service invocations and other activities for data processing, fault and transaction handling, and so on. Processes are often developed using modeling languages such as EPC [4, 13], BPMN [9], UML Activity Diagram extensions [8], BPEL [7] or XPDL [15]. Business analysts usually design processes in high abstraction languages, such as BPMN, EPC, or UML Activity Diagram, and developers implement them using executable languages, such as BPEL/WSDL. An important issue that hinders the interoperability and the reusability of existing process models is the huge divergence of these modeling languages. This issue occurs because there is no explicit link between two modeling languages at the same or different abstraction levels. For instance, developers could not re-use or integrate the R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 55–66, 2008. c Springer-Verlag Berlin Heidelberg 2008

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whole or part of a process described using BPEL in another process developed using BPMN or EPC, and vice versa. The most popular solution for this issue is to define direct transformations between the different process modeling languages [11,6,16,5]. These approaches, even though they partially solve the problem, pose a serious limitation regarding extensibility. First, they focus on one concern, the control flow of the process models, and ignore other crucial concerns, such as collaborations, data processing, fault handling, and so on. Second, each of these transformation approaches only provides the integration of two specific kinds of process models, but provides neither interoperability with process models realized in other languages than those two nor the reusability of these models. To overcome this issue and the limitations of transformation-based approaches, we present a novel integration approach for enhancing the interoperability and reusability of process-driven SOA models. Our approach exploits the concept of architectural view during the bottom-up analysis, then maps the process descriptions onto relevant high-level or low-level views. Using a view-based modeling framework (VbMF), originally introduced in our previous work [12], we can provide the interoperability and reusability between these views, or in other words, between different process modeling languages. In this paper, we first present an overview of VbMF in Section 2. Section 3 describes the model integration approach we propose in this paper in terms of our view-based reverse engineering tool-chain. Section 4 depicts the empirical analysis of this approach to exemplify process descriptions in popular modeling languages, namely, BPEL and WSDL, via a simple but realistic example. Finally, we compare to related work in Section 5 and conclude.

2

Overview of View-based Modeling Framework (VbMF)

Figure 1(a) gives an overview of the concepts in VbMF. A view is defined according to a corresponding meta-model, which is a (semi-)formalized representation of a particular process concern. At the heart of VbMF is the Core meta-model (see Figure 1(b)) from which each view meta-model is derived. Example metamodels that we have derived from the Core include the following views [12]: Collaboration (see Figure 2(b)), Control Flow (see Figure 2(a)) and Information. Based on the meta-models, we derive particular views. For specific technologies, for instance, BPEL and WSDL, we provide extension meta-models which comprise additional details required to depict the specifics of these technologies. Figure 3 depicts the BPEL-specific extension of the collaboration view in Figure 2(b). Hence, we can use the distinction of Core meta-model, generic view meta-models, and extension view meta-models to handle different abstraction levels, including business-level concerns and technical concerns. The main task of the Core meta-model (shown in Figure 1(b)) is to provide integration points for the various meta-models defined as parts of VbMF, as well as extension points for enabling the extension with views for other concerns or more specific view meta-models, such as those for specific technologies. Consider the control flow view meta-model in Figure 2(a) as an example: A control flow

View-Based Integration of Process-Driven SOA Models

57

Metameta-model

M3 Core meta-model

M2

Collaboration View meta-model

Control Flow View meta-model

Information View meta-model

Transaction View meta-model

New-Concern View meta-model

NameSpace Extension View meta-model

Meta-level operations: - design (meta-model) - extend

M1 Extension Extension Extension View View View

M0

-uri : String -prefix : String

View-level operations: - design (view) - integrate - generate (code)

Extension Extension View View View

Extension Extension Executable View View Code

Extension Configuration View files

Code-level operations: - deploy - code

(a) View-based model-driven framework

View view -ID : String *

Process

provider

provided

*consumer

* required

*

element ExtensibleElement

Service

*

NamedElement -name : String

*

(b) The core meta-model

Fig. 1. Meta-meta-model and the Core meta-model View (core)

Link

ControlFlowView

outgoing 1

incoming 1

source 1

target 1

activity 1 1..* activity

View (core)

Activity

Service (core)

SimpleActivity

interaction Interaction * interaction *

CollaborationView

service * Service

partnerLink 1 PartnerLink

service *

StructuredActivity -link : Link [*]

Switch -otherwise : Activity [0..1]

* message Message

interface 1..* Interface

interface 1

role 1..*

1 message

1..* cases Case Flow

Sequence

-condition : String -activity : Activity [1]

(a) Control flow view meta-model

* role Role

-name : String -myRole : Role [0..1] -partnerRole : Role [0..1]

* channel in Channel *out *

* operation Operation

* partnerLinkType PartnerLinkType 1 partnerLinkType

(b) Collaboration view meta-model

Fig. 2. The control flow view and collaboration view meta-models

view comprises many activities and control structures. The activities are process tasks, such as service invocations, or data handling, while control structures describe the execution order of the activities. The control flow view meta-model is defined by extending the View and ExtensibleElement meta-classes from Core. All other view meta-models are essentially defined in the same way: by extending the model elements provided as integration and extension points in the Core meta-model. The more specific extension view meta-models are also defined in the same way: they extend the elements of their root meta-models and of the Core meta-model. For instance, a BPEL-specific control flow meta-model with

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H. Tran, U. Zdun and S. Dustdar Interaction (collaboration) CorrelationSet

Correlation * 0..1 correlation -isInitiate : Boolean correlation

correlationSet

-properties : Property 1..*

AbstractInteraction

interface 1

Interface (collaboration)

1..* correlationSet CollaborationView (collaboration)

CorrelationSets

Invoke

Reply

Receive -createInstance : Boolean

0..1 correlationSets BPELCollaboration View * propertyAlias PropertyAlias

*

-messageType : String -part : String -query : String

property 1

property Property

0..1 out variable Variable 0..1 variable variable * 0..1 0..1 in

-type : String

Fig. 3. BPEL extension of the collaboration view meta-model interpretes

Process descriptions (BPEL,WSDL,etc.) High-level Views

produces

Framework meta-models conforms

produces

Low-level Views

"virtual" integration of high-level and low-level representations in various languages corresponds to

High-level Languages described in

defines

refines into

View-based intepreters

corresponds to

"virtually" refines

conforms

described in

Low-level Languages

Fig. 4. Integration of various modeling languages using view-based reverse engineering

extra elements, such as while, wait, terminate, etc., can be defined by extending the elements from Figures 1(b) and 2(a). In our implementation of these concepts, we exploit the model-driven software development (MDSD) paradigm [14] to separate the platform-neutral views from the platform-specific views. Platform-specific models or executable code, for instance, Java code, or BPEL and WSDL descriptions, can be generated from the views by using model-to-code transformations. Our prototype is realized using openArchitectureWare (oAW) [10], a model-driven software development tool, and the Eclipse Modeling Framework [3]. The tools allow stake-holders of a process to only view a specific perspective, by examining a single view, or to analyze any combination of views (i.e., to produce an integrated view).

3

View-Based Model Integration

In this section, we present a view-based reverse engineering approach for addressing the divergence issue of modeling languages and for overcoming the limitations of transformation-based solutions. The ultimate goal of this approach is


59

to map the process descriptions onto high-level or low-level views, appropriate for different stakeholders (see Figure 4). To demonstrate our approach, we have exemplified it using the combination of BPEL and WSDL, which are possibly the most popular process and service modeling descriptions used by numerous companies today. The same approach can be taken for any other process-driven SOA technologies. In addition, due to space limitation, we only present the extraction and the integration of two basic views in VbMF: the control flow view and the collaboration view. Other views, for instance, the Information View, the Transaction View, etc., can be integrated using the same approach. The tool-chain consists of a number of view-based interpreters, such as control flow interpreter, information view interpreter, collaboration view interpreter, and so on. Each interpreter is responsible for extracting one view from the process descriptions. Therefore, an interpreter for a certain view must be defined based on the meta-model which that view conforms to. For instance, the control flow view consists of elements, such as Activity, Flow, Sequence, Switch, etc. (see also Figure 2(a)). To extract the control flow view from process descriptions, the interpreter walks through the input descriptions to pick only these elements and ignores others. As the modeling framework grows with additional views, the reverse engineering tool-chain can be scaled to fit to the growing framework. To add a new view to the framework, an adequate meta-model of this view has to be specified using extension mechanisms [12]. Next, we develop an interpreter based on the new meta-model specification and hook the it into the tool-chain.

4

Empirical Analysis of Model Integration

In this section, we exemplify our approach for the combination of the process and service modeling languages BPEL and WSDL. However, the concepts are not specific for BPEL/WSDL, but applicable in the same way for other processdriven modeling languages. To demonstrate the realization of aforementioned concepts, we present a simple but realistic case study, namely, a Shopping process, which depicts a typical e-commerce scenario. The Shopping process is represented in BPEL and WSDL. The process starts when the customer’s purchase order arrives together with their billing information (e.g., credit card). Then, the process invokes a Banking service to validate the customer’s credit card through the VerifyCreditCard activity. If the validation is successful, the customer will obtain the purchased items which is shipped by a delegated Shipping service. After that, the process will send the order invoice to the customer. Otherwise, the order will be canceled as a negative confirmation is received from the Banking service. An excerpt from the BPEL/WSDL code is shown on the left hand side of Figures 5 and 6. 4.1

Control Flow View Mapping

According to the specification of the control flow view meta-model (see Figure 2(a)), a control flow view consists of elements which represent the control

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H. Tran, U. Zdun and S. Dustdar <process name="Shopping"> <partnerLinks> <partnerLink name="Seller" partnerLinkType="Seller" myRole="Seller" /> <partnerLink name="Approver" partnerRole="Approver" partnerLinkType="Approver" /> <partnerLink name="Payer" partnerRole="Payer" partnerLinkType="Payer" /> <partnerLink name="Shipping" partnerRole="Shipper" partnerLinkType="Shipping"/> <sequence> <switch> <sequence>

Fig. 5. The mapping of the Shopping process descriptions in BPEL to the collaboration view (top-right) and the control flow view (bottom-right) Table 1. The basic mapping from BPEL onto the control flow view BPEL element invoke, receive, reply, assign name=”...” sequence name=”...” flow name=”...” switch name=”...” case name=”...” condition=”...” otherwise name=”...”

Control flow view element controlflow::SimpleActivity setName() controlflow::Sequence/setName() controlflow::Flow/setName() controlflow::Switch/setName() controlflow::Case setName() setCondition() controlflow::Otherwise/setName()

flow of a business process. The hierarchy and the execution order of the control flow are defined using basic structured activities. These structured activities include: sequence, for defining a sequential execution order; flow, for the concurrent executions; and switch-case-otherwise, for conditional branches. Structured activities can be nested and combined in arbitrary manners to represent various complex control flows in BPEL processes. In addition, BPEL also has primitive activities, such as invoke, an service invocation; receive, waiting for a message


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from partners; reply, sending back a response to a certain partner; assign, assigning values to BPEL variables. The interpreter walks through the process description in BPEL and collects the information of atomic and structured activities. Then, it creates the correspondent elements in the view and assigns relevant values (e.g. the name attribute) to their attributes. We describe in Table 1 the mapping specification between BPEL and the control flow view elements/attributes. The BPEL mapping is illustrated in Figure 5. 4.2

Collaboration View Mapping

The collaboration view interpreter is realized using the same approach as the control flow interpreter. However, the collaboration view comprises not only the elements from BPEL but also from WSDL. Hence, first of all, the interpreter has to collect all service interface, message, role, partnerLinkType descriptions from WSDL. Then, the interpreter creates relevant elements in the collaboration view according the mapping rules given in Table 2. Figure 6 depicts the mapping of the Shopping process in WSDL to corresponding elements in the collaboration view. Next, the interpreter walks through the BPEL code to extract collaborative elements in a similar manner. The basic activities, namely, invoke, receive, and reply, appear on the collaboration view with the same name as in the control flow view. However, these activities contain additional collaborative attributes as depicted in Table 3. The BPEL mapping is illustrated in Figure 5. Table 2. The basic mapping from WSDL onto collaboration view elements WSDL element definition message name=”...” portType name=”...” operation name=”...” input,output name=”...” message=”...” plnk::partnerLinkType name=”...” plnk::Role name=”...” service name=”...”

Collaboration view element core::Service collaboration::Message/setName() collaboration::Interface/setName() collaboration::Operation/setName() collaboration::Channel setName(), setMessage() collaboration::PartnerLinkType/setName() collaboration::Role/setName() core::Service.setName()

Besides generating the collaboration view’s elements, the interpreter uses the information collected in the former step to establish necessary relationships between these elements. For instance, the relationship between collaboration::Interaction and collaboration::PartnerLink elements is derived from the association between the communication activities (e.g.,invoke, receive, reply) and the partnerLink, or the relationship between collaboration::PartnerLink and collaboration::PartnerLinkType is derived from the association among the partnerLinkType elements in WSDL and the partnerLink elements in BPEL, and so on.

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H. Tran, U. Zdun and S. Dustdar <definitions> <message name="PurchaseOrder" /> <message name="OrderResponse" /> <portType name="Shopping"> <partnerLinkType name="Seller"> <portType name="Shopping" /> <partnerLinkType name="Approver"> <portType name="CreditCard" /> <partnerLinkType name="Payer"> <portType name="CreditCard" /> <partnerLinkType name="Shipping"> <portType name="Shipping" />

Fig. 6. The mapping of the Shopping process descriptions in WSDL to the collaboration view

4.3

BPEL-extension Collaboration View Mapping

According to the collaboration view meta-model, we map the Shopping process onto a high abstraction view as shown in Figures 6 and 5. This view is suitable for communication with business analysts, but it does not provide appropriate information for IT experts. As we mentioned in Section 2, the collaboration view can be refined into a lower abstraction view, namely, the BPELCollaboration View (see Figure 3). To demonstrate the concept of view refinement, we present the mapping of the Shopping process in BPEL to the corresponding BPEL-specific collaboration view in Figure 7 according to the specification in Table 3. For the sake of readability, we omit the elements inherited from the collaboration view and depict an excerpt of the BPEL-extension collaboration view together with additional features. 4.4

Discussion

In the previous empirical analysis, we illustrated the mapping of process descriptions onto high-level or low-level views. The high-level views in our approach are platform-independent models designed to capture abstract features in a process. Corresponding to these views are high-level modeling languages, such as EPC, BPMN or UML Activity Diagram. In this paper, we illustrated a mapping of such models into our framework from high-level view models, which reflect the concepts in the low-level models rather closely. This has a number of advantages. Firstly, it uses one kind of modeling approach for all types of views. Secondly,


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<process name="Shopping"> ... <property name="CustomerName" type="xsd:string" /> <property name="CreditCard" type="xsd:string" /> <propertyAlias propertyName="CustomerName" messageType="Customer" part="fullname" query="/Customer/fullname" /> <propertyAlias propertyName="CreditCard" messageType="CreditCard" part="CCNo" query="/CreditCard/CCNo" /> <sequence> .... ....

Fig. 7. The mapping of the Shopping process descriptions in BPEL to the BPEL collaboration view

it avoids any semantic mismatch or transformation between modeling concepts. But, on the other hand, this approach has the disadvantage that existing modeling language code (say realized in EPCs, BPMN or UML Activity Diagrams) would have to be mapped to our high-level models, which could be a considerable effort for huge existing process repositories. But in general this is possible and can even be largely automated, because our control flow view represents five basics patterns that exist in any modeling language; the collaboration view describes generic interactions that typically occur between a process and its partners [12]. Hence, an adequate interpreter for a certain language can distill these views from the process descriptions in that language using our approach. Alternatively, our approach can of course also be extended with a respective new view model, such as an EPC or BPMN control flow view. A high-level view can be refined into a low-level view which captures the specifics of a particular technology. For example, the collaboration view is extended and refined to the BPEL collaboration view that embodies several BPELspecific features. Using the same approach, we can define appropriate metamodels and interpreters for pulling out corresponding views from process implementations in low-level, executable languages such as BPEL. Using VbMF the stakeholders can work on a particular view or can examine any combination of several views instead of manipulating various kinds of process descriptions or digging into the implementation code in executable languages. Our approach can help the stakeholders to quickly understand and grasp the information in (different) modeling languages as well as re-use adequate views.

5

Related Work

In the software engineering area, the concept of reverse engineering is the process of analyzing a system to identify the system’s components and their relationships

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H. Tran, U. Zdun and S. Dustdar Table 3. The basic mapping from BPEL description onto the collaboration view

BPEL element invoke name=”...” partnerLink=”...” portType=”...” operation=”...” inputVariable=”...” outputVariable=”...” correlation set=”...” receive/reply name=”...” partnerLink=”...” portType=”...” operation=”...” variable=”...” createInstance=”yes” correlation set=”...” partnerLink name=”...” partnerLinkType=”...” myRole=”...” partnerRole=”...” correlationSets correlationSet name=”...” properties=”...” property name=”...” type=”...” propertyAlias propertyName=”...” messageType=”...” part=”...” query=”...”

Collaboration view element collaboration::Interaction setName() setPartnerLink() setInterface()

collaboration::Interaction setName() setPartnerLink() setInterface()

setCreateInstance(true) collaboration::PartnerLink setName() setPartnerLinkType() setMyRole() setPartnerRole()

BPELCollaboration view element bpel::Invoke setName() setPartnerLink() setInterface() setOperation() setInput() setOutput() createCorrelation() bpel::Receive/bpel::Reply setName() setPartnerLink() setInterface() setOperation() setVariable() setCreateInstance(true) createCorrelation() (inherits from parent – the collaboration view)

bpel::CorrelationSets bpel::CorrelationSet setName() setProperty() bpel::Property setName() setType() bpel::PropertyAlias setProperty() setMessageType() setPart() setQuery()

and create representations of the system in another form or at a higher level of abstraction [1, 2]. We devised a novel view-based reverse engineering approach that supports extracting relevant abstraction levels of process representations in terms of architectural views. Various modeling languages can be integrated into VbMF and manipulated or re-used in other process models. Existing modeling languages provide high-level abstractions, such as EPC [13, 4], BPMN [9], or UML Activity Diagram extensions [8], or low-level and executable descriptions, such as BPEL [7], or XPDL [15]. There are several efforts to transform process models described in one language into models represented in another language. For instance, Mendling et al. [6] present the mapping of


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BPEL to EPCs; Ziemann et al. [16] report on an approach to model BPEL processes using EPC-based models; Recker et al. [11] translate between BPMN and BPEL; Mendling et al. [5] discuss X-to-BPEL and BPEL-to-Y transformations. These transformation-based approaches mostly focus on one concern of the process models, namely, the control flow. There is no support for handling or integrating other process concerns, such as service interactions, data processing, or transaction handling. Moreover, each of these approaches only provide the integration of a certain pair of process modeling languages, but does not offer the interoperability of process models in other languages, or the reusability of these models to develop other processes. Zou et al. [17] propose an approach for extracting business logic, in term of workflows, from existing e-commerce applications. The analyzing process is guided by documented workflows to identify the business logic. Then, the business logic is captured in terms of the control flows using the concept of process algebra. This approach aims at providing high-level representations of processes and maintaining the relationships among different abstraction levels to quickly re-act to changes in business requirements. This approach only focuses on control flow and does not target other concerns. In addition, there is no support for the interoperability and the re-usability of different software components.

6

Conclusion

Interoperability and reusability suffer from the heterogeneous nature of the participants of a software system. SOA partially reconciles this heterogeneity by defining standard service interfaces as well as messaging mechanisms for communicating between services. Process-driven SOAs provide an efficient way of coordinating various services in terms of processes to accomplish a specific business goal. However, the huge divergence of process modeling languages raises a critical issue that deteriorates the interoperability and the reusability of software components or systems. Our approach, presented in this paper, exploits the concept of architectural views and a reverse engineering tool-chain to map high-level or low-level descriptions of processes into appropriate views. The resulting views are integrated into the view-based modeling framework and can be manipulated or re-used to develop other processes.

References 1. Biggerstaff, T.J.: Design recovery for maintenance and reuse. IEEE Computer 22(7), 36–49 (1989) 2. Chikofsky, E.J., Cross, J.H.I.: Reverse engineering and design recovery: A taxonomy. IEEE Software 7(1), 13–17 (1990) 3. Eclipse. Eclipse Modeling Framework (2006), http://www.eclipse.org/emf/ 4. Kindler, E.: On the semantics of EPCs: A framework for resolving the vicious circle. In: Business Process Management, pp. 82–97 (2004)

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5. Mendling, J., Lassen, K.B., Zdun, U.: Transformation strategies between blockoriented and graph-oriented process modelling languages. Technical Report JM200510 -10, WU Vienna (2005) 6. Mendling, J., Ziemann, J.: Transformation of BPEL processes to EPCs. In: Proc. of the 4th GI Workshop on Event-Driven Process Chains (EPK 2005), December 2005, vol. 167, pp. 41–53 (2005) 7. OASIS. Business Process Execution Language (WSBPEL) 2.0 (May 2007), http://docs.oasis-open.org/wsbpel/2.0/OS/wsbpel-v2.0-OS.pdf 8. OMG. Unified Modelling Language 2.0 (UML) (2004), http://www.uml.org 9. OMG. Business Process Modeling Notation (February 2006), http://www. bpmn.org/Documents/OMG-02-01.pdf 10. openArchitectureWare.org (August 2002), http://www.openarchitectureware.org 11. Recker, J., Mendling, J.: On the translation between BPMN and BPEL: Conceptual mismatch between process modeling languages. In: Eleventh Int. Workshop on Exploring Modeling Methods in Systems Analysis and Design (EMMSAD 2006), June 2006, pp. 521–532 (2006) 12. Tran, H., Zdun, U., Dustdar, S.: View-based and Model-driven Approach for Reducing the Development Complexity in Process-Driven SOA. In: Intl. Working Conf. on Business Process and Services Computing (BPSC 2007), Lecture Notes in Informatics, vol. 116, September 2007, pp. 105–124 (2007) 13. van der Aalst, W.: On the verification of interorganizational workflows. In: Computing Science Reports 97/16, University of Technology, Eindhoven (1997) 14. V¨ olter, M., Stahl, T.: Model-Driven Software Development: Technology, Engineering, Management. Wiley, Chichester (2006) 15. WfMC. XML Process Definition Language (XPDL) (April 2005), http://www.wfmc.org/standards/XPDL.htm 16. Ziemann, J., Mendling, J.: EPC-based modelling of BPEL processes: a pragmatic transformation approach. In: Proc. of the 7th Int. Conference Modern Information Technology in the Innovation Processes of the Industrial Enterprises (MITIP 2005) (2005) 17. Zou, Y., Hung, M.: An approach for extracting workflows from e-commerce applications. In: ICPC 2006: Proc. of the 14th IEEE Int. Conf. on Program Comprehension (ICPC 2006), Washington, DC, USA, pp. 127–136. IEEE Computer Society, Los Alamitos (2006)

Model-Driven Development of Composite Applications Susanne Patig University of Bern, IWI, Engehaldenstrasse 8, CH-3012 Bern, Switzerland [email protected]

Abstract. In service-oriented architectures, composite applications (CA) are created by assembling existing software services. Model-driven development does not implement a CA directly, but starts from models that describe the services and their interactions (and map to source code). This article classifies existing approaches for the model-driven development of CAs. Based on a small example it is demonstrated that current approaches do not support the development of CAs where the order of service calls is not constrained and depends on user input. To solve this problem, a new approach for the composition of web services is presented, which combines the Service Component Architecture (SCA) and state transition models. Keywords: model-driven development, web services, composition

1 Composite Applications by Example In service-oriented architectures, composite applications (CA) are developed by assembling existing services [8]; this is also called service aggregation [12]. Services are self-contained, self-describing, stateless pieces of software that offer some functionality [23]. Based on the description of their functionality, services can be found and accessed by other services. This paper focuses on web services, which rely for their description and interaction on non-proprietary internet standards like XML, HTTP, SOAP and WSDL [29]. Service composition can be static or dynamic. Static compositions select and connect services at design time and execute this configuration at run time, whereas in dynamic compositions [2] the actual services invoked or their interactions are determined during the execution of the CA. Here, static service composition is examined, since dynamic composition requires (semantic) web service discovery, which still is an open issue in practice. Static web service composition is not necessarily model-driven. Imagine the following example: A small holiday planning CA should be implemented by assembling web services that allow users to look for airports in some country (getAir1 portInformationByCountry(country:String)::ListAirport) , to check the temperature in a town (getWeather(town:String)::Temperature) and to look for a guide book (getBook(town:String)::ListBook). The strings set in courier font are in fact operations of the web services Airport and GlobalWeather 1

operationName(inputParameterName:Type)::TypeOutputParameter.

R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 67–78, 2008. © Springer-Verlag Berlin Heidelberg 2008

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(http://www.webservicex.net/) as well as Amazon AWSECommerceService (http://soap.amazon.com/onca/soap?Service=AWSECommerceService). Direct implementation of such a CA usually proceeds as follows: (1) Determine the web services and their WSDL files. In this respect, the sample CA is simple, since the location of the web services is known and has not to be looked up in a UDDI. (2) For each web service involved, generate a local proxy from its WSDL. (3) Analyze the signatures of the web service operations - i.e., the inbound and outbound messages and code GUI and interaction logic accordingly. Fig. 1 shows a screenshot of the resulting CA, implemented by Eclipse 3.2.1 and JDK 1.5.0.

Fig. 1. GUI of the sample CA

From the functional point of view (non-functional requirements are not considered in this paper), the sample CA is characterized as follows: 1. The services require input data (country and airport town). 2. The input data can be provided by users or by other services, e.g., the town can be entered as a free text or selected from a list that is the outbound message of getAirportInformationByCountry. 3. The services are not ordered (unconstrained composition [12]). For example, if the decision on the travel destination mainly depends on the weather, users will start with getWeather. Alternatively, since nice weather is useless if the town does not have an airport, the web service operation getAirportInformationByCountry could be invoked first. Moreover, searching for a guide book (getBook) can be done at any planning stage (to get information about the town with the nice weather). Model-driven development (MDD) of web service CAs suggests itself for two reasons: First, there are several standard programming activities like the processing of WSDL, XML and SOAP and GUI development that produce considerable amount of technical code. MDD aims at generating such technical code from models. Secondly, in direct implementations the ‘application logic’ of the CA is interwoven with the technical code and, hence, for non-developers difficult to maintain. This obstructs the vision of Enterprise SOA, according to which business consultants should be able to

Model-Driven Development of Composite Applications

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assemble new applications from existing services [23]. MDD supports this vision by modelling the application logic (and by subsequently transforming these models). Surprisingly, finding an appropriate approach for the model-driven development of CA with the characteristics of the sample turned out to be difficult. Section 2 sketches the existing approaches to model services and their interactions. Since none of them is completely capable of modelling the sample CA, in Section 3 a new approach is presented, which combines the programming specifications of SCA (Service Component Architecture [19]) and state transition diagrams.

2 Model-Driven Approaches for Service Composition 2.1 Model-Driven Development and (Web) Services In model-driven development (MDD), a model is an abstract description of some software system [13]. The content of the model either hides all details of the implementation platform (PIM: Platform Independent Model) or not (PSM: Platform Specific Model); in other words, models can be more (PIM) or less (PSM) abstract. Basically, MDD consists in transforming more abstract models into more specific ones –with source code being the most concrete description of a software system. MDD models are created by applying some dedicated modelling language (metamodel). As a result of the Model-driven Architecture (MDA), which was proposed by the OMG in 2001 [16], the Unified Modelling Language (UML) became a de facto standard for modelling in MDD. However, MDD and its manifestation MDA are not tied to UML, since the Meta-Object Facility (MOF) – another standard included in the MDA recommendation - allows one to define new metamodels. Consequently, this paper does not restrict the discussion of model-driven approaches for the development of composite applications to UML-based ones. Because of the numerous standards involved, describing the composition of web services by models seems to be both possible and worthwhile. The most important standard to get access as well as usage information on existing web services is the Web Services Description Language (WSDL) [27], [28]. This paper heavily relies on the WSDL definition of services, which separates the abstract functionality of a web service from its mapping to particular implementations. The abstract part of a WSDL 2.0 definition consists of an interface (port type in WSDL 1.1), which is a collection of the abstract operations provided by the web service (and optional faults). Each operation is defined by its signature, i.e., its inbound and outbound messages, and a mandatory message exchange pattern (MEP), e.g., in-only, out-only, in-out, out-in [26]. In contrast to WSDL 1.1, WSDL 2.0 does not specify messages explicitly under a corresponding tag, but uses the XML type system. The implementation part of a WSDL definition comprises binding and endpoint. A binding specifies concrete message formats as well as transport protocols and supplies this information for each operation in the interface. An endpoint (port in WSDL 1.1) relates a binding to the network address of some implemented service. Altogether, a WSDL 2.0 service consists of a single interface and a list of endpoints. Platform independence as the distinctive feature of the web service standards in general and the WSDL in particular are in line with the MDA distinction between

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PIM and PSM. In MDD, the transformation from PIM to PSM is strictly forward. If software systems are developed that use web services, it must be distinguished between (1) setting up a (web) service-oriented architecture and (2) reusing an existing one. In contrast to the first case, which involves only forward transformations (e.g., from UML to WSDL to Java), the second case, which is the relevant one for CA development, may also require reverse transformations. Apart from the direction of the transformations, both cases differ in the content to be modelled: To set up an architecture, the services and their allowed message-based interactions must be described. In the CA case, the service-oriented architecture is given and restricts possible interactions, which are the focus of modelling. Service interactions come in three facets: conversation, choreography and orchestration. The particular messages exchanged between services and their order form a conversation [25]. Choreography describes all valid conversations by stating temporal and logical dependencies between messages (featuring sequencing rules, correlation, exception handling and transactions) [24]. Orchestrations differ from choreographies in the objects modelled (activities instead of messages) and in the view on behaviour (central control instead of decentralization), since they define an order for the activities (services) within a process [22]. Both static aspects (services and their interfaces) and dynamic ones (orchestration, choreography) must be described when CAs are developed; Section 2.2 and Section 2.3, respectively, summarize corresponding approaches. Data centric approaches (see Section 2.4) connect static and dynamic aspects by the data exchanged. 2.2 Static Modelling Approaches Static aspects of a CA can be modelled by either UML 2.0 profiles or SCA. UML 2.0 profiles, which follow the MDA tradition, are specializations of the UML metamodel, i.e., the elements of a profile are created by adding domain-specific semantics to standard UML elements. In detail, profiles are UML packages that collect a set of stereotypes. Stereotypes are domain-specific metaclasses carrying metaattributes (formerly ‘tagged values’), which are common in some domain [17]. Here, software architectures that rely on web services form the domain. Consequently, the corresponding UML 2.0 profiles relatively closely mirror the WSDL (see Table 1). Some service-relevant elements (e.g., interface, operation, message, and port) are already contained in the UML metamodel [OMG04] and, hence, need not to be defined in profile. Table 1. Selected UML 2.0 profiles for software services Abstract Types Messages Opera- Interface (Types) tion  () X Service Specification   () Port Type

Concrete Binding

Endpoint Property of Service Channel ? Service

: Already contained in UML 2.0 metamodel

Service Service Provider Service

?: Not explained in the paper

Connections between Services

Ref. [27]

Channel

[9]

Connector Type [2] Ref.: Reference


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The UML 2.0 class diagrams created by applying these profiles can be easily transformed into WSDL files and skeleton code for software components that provide web services (see, e.g., the profile usage in the IBM Rational Software Architect [9]). However, the described profiles at best rudimentary support the modelling of dynamic aspects of CAs: For example, profile elements (e.g., “collaboration” in the IBM profile) are provided that establish a link to processes in BPEL4WS (Business Process Execution Language for Web Services) [10]. The Service Component Architecture (SCA) is a set of specifications that describe how software systems can be built out of existing services and how service components can be created ([11], [19]). A SCA (service) component, the basic modelling element, is a configured instance of an implementation that provides a business function (service). Implementations may depend on services provided by other SCA components (modelled by references). Moreover, SCA components can have properties whose values influence the service provided by the implementation. Property values are set during configuration, which also wires the references to other services. The set of SCA specifications consists of the assembly specification (defining how SCA components can be combined to form a SCA system), the policy framework (constraints and capabilities related to security, reliability and transactions), the client implementation specification (guidelines for building clients for SCA components by using particular implementation technologies, e.g., Enterprise Java Beans or C++) and the binding specification (how to access service components by using specific protocols). Currently, SCA is more a set of programming guidelines than an MDD approach, since it does not provide a metamodel, but only a weak concrete syntax (notation) for SCA components and their constituents. However, a translational semantics [15] of the missing metamodel can be constructed from the SCA specifications that map SCA elements to implementation technologies. Such a metamodel also enables transformations from models to source code, which is the core of MDD. By including attributes that refer to the policy framework, SCA (in contrast to UML 2.0 profiles for software services) goes beyond architecture and functional requirements. Moreover, SCA explicitly considers the development of clients for web services. Dynamic aspects of CAs must be modelled by SCA components that are BPEL4WS processes [11]. [19]. 2.3 Dynamic Modelling Approaches For CAs, the static aspects are already given by the WSDL definitions of the services. The application logic of a CA refers to the dynamic aspects, since it consists in the required or allowed interactions between services. To support non-developers in assembling a CA, these dynamic aspects should be represented by models. Approaches that model dynamic aspects of composite applications aim at separating the design time of a CA from its run time: During design, the interactions are described by some model. At runtime, this model is executed by an execution engine, which invokes the services and does the message handling [20]. Models of dynamic aspects of CAs describe either the activities (services) that constitute a process (orchestration) or the messages exchanged between services (choreography). For both, UML or alternative modelling languages are used.

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If UML is applied, orchestrations are expressed by activity diagrams ([7], [21]), where activities represent (particular operations of) services, and their inbound and outbound messages are visualized > by pins (see Fig. 2). Consequently, message decideOnCountry exchange between services corresponds to data Country flows; any other service interaction is a control flow. Occasionally, additional stereotypes (marked by > in UML diagrams) are used Country to distinguish activities that are service calls from > activities that perform data transformations [21]. getAirportInformationbyCountry Analogously to UML activity diagrams, other ListAirport process modelling languages like BPMN (Business Process Modelling Notation [18]) or YAWL (Yet Another Workflow Language2) equate services with ListAirport activities in some process and show them as nodes in > decideOnAirport a graph; directed arcs between the nodes represent Airport data and control flows. If the process modelling language does not have its own execution engine3, the resulting process models are mapped to Airport BPEL4WS for their execution [20]. To model choreographies, UML sequence dia> getWeather grams can be directly applied, since they show the exchange of messages between lifelines. In the CA context, the lifelines correspond to services (see Fig. 3; the lifelines are enclosed in boxes). Once more, BPEL4WS provides the runtime; hence, the UML > sequence diagram must be transformed [3]. getBook By definition, choreographies concern more than one web service. For individual services, their handling of inbound and outbound messages can be described by automata-like modelling languages (e.g., Mealy implementations) [4]. The concrete Fig. 2. UML activity diagram for syntax of these metamodels consists in graphs, the sample CA where inbound and outbound messages correspond to transitions (labelled, directed arcs) between states (nodes). Since these automata can be directly composed [8], the modelling approach is applicable to CAs. However, so far, automata have been mainly used to obtain theoretical results concerning service composition (e.g., on composability); execution environments and mappings to source code are missing. Additionally, the deterministic Mealy automata underlying the sketched modelling approaches are not expressive enough for real world CAs that involve user interaction: Often, such CAs are more equivalently described by non-deterministic automata (although any nondeterministic automata can be transformed in an equivalent deterministic one [15]). 2 3

http://www.yawl-system.com/ As opposed to BPMN, YAWL has its own execution environment.


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Fig. 3. UML sequence diagram for the sample CA (IBM Rational Software Architect® 7.0)

2.4 Data-Centric Modelling Approaches Data-centric approaches for the model-driven development of CAs are the only ones for which successful applications in industry, banks, government, health care etc. have been reported [14], [1]. They centre around the data exchanged by messages. The data may be of arbitrary complex type and is usually described by XML. Data is associated with both activities (web service operations) and human user interactions. Thus, each activity is assigned at the most one form, which allows users to access data (see Fig. 4). A form must contain all data that is mandatory for the activity. In the forms of the case handling approach [1], users can also insert data that is required by other (later) activities. Additionally, so-called ‘restricted data’ may only be entered during a particular activity.

F ormular 1

F ormular 1

F ormular 1

D1: Country D1

D1: Country D1

D1 D1: Country

D2: Airport D2

D2: Airport D2

D2: Airport D2

A k tivitä

A k tivitä

A k tivitä

mandatory: D1

mandatory: D1

mandatory: D1

getAirportInformationbyCountry

Mandatory : D1

getWeather

Mandatory : D2

getBook

Mandatory : D2 C as e

Fig. 4. Case diagram for the sample CA

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Data-centric approaches model orders of activities (orchestration). But, since an activity can be completed if all of its mandatory and restricted data are provided, activities that have been sequentially ordered during design can be executed in parallel. Thus, the data-centric orchestration of services is more flexible than the one resulting from process modelling (see Section 2.3). If services in a CA do not require data input from users, the data-centric approach is not appropriate. Moreover, the metamodel of the case handling metamodel is not related to WSDL (as opposed to [14]), and its concrete syntax, depicted in Fig. 4, is rather vague.

3 WSCAM: Web Service Component Architecture Modelling Recall the sample CA from Section 1: Several services, described by WSDL, have to be composed. The services require input data from users or from other services. The order of service execution is unconstrained. Modelling this CA by UML 2.0 profiles that focus on static aspects does not add any value, since all the information captured by the resulting UML class diagrams is already known from the services’ WSDL files. Rather, the behaviour of the CA must be specified, e.g., by UML 2.0 activity or sequence diagrams. UML 2.0 activity diagrams force one to either define an order between the services (see Fig. 2) or to invent artificial control conditions. Modelling all six possible paths of service invocation would be an option here, but is not advisable when more services are involved. The constructs for forking and merging do not solve the problem either, because only getWeather and getBook could be executed in parallel (since they share the same input message type). Even in UML 2.0 sequence diagrams (see Fig. 3), the services are implicitly ordered by interaction time, which runs down from the top. In fact, Fig. 2 and Fig. 3 alternatively represent the same order of service invocations. Basically, the data-centric case diagram of the sample CA in Fig. 4 allows the execution of getBook and getWeather as soon as some (airport) town is entered. But, the case handling metamodel [1] has several weaknesses: (1) It does not specify whether or not activities must be ordered, it (2) does not distinguish between the input and the output of activities, and (3) it cannot be applied to activities that are not associated with data objects. Altogether and considering the missing relationship to WSDL, the data-centric approach is not general enough to model any kind of CA. In the following, I sketch a new approach (WSCAM: Web Service Component Architecture Modelling) for the model-driven development of both unconstrained and constrained CAs that may involve user interaction. The approach presumes services described by WSDL, which is true for web services. Moreover, WSCAM relies on the SCA assembly framework to (1) enable transformations from models to distinct implementation platforms, to (2) integrate non-functional properties of web services not described by WSDL (e.g., security, transaction handling), and to (3) arrive at an open and general, yet standardized approach for web services composition. WSCAM is prompted by the observation that the sample CA basically corresponds to the design pattern ‘observer’ [6]. According to this pattern, one ore more objects (observer, listener) are registered to observe events that may be raised by the observed


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objects. In fact, the implementation shown in Fig. 1 uses a Java Swing event listener to catch user interactions like “Button ‘Search Airports’ pressed”. The observer pattern can be easily combined with the design pattern ‘model-view-controller’ (MVC). In this pattern, application logic and data are contained in the MVC model, and the views present data and elements for user interaction. The controller (an advanced type of the observer) reacts to events from the views or the model and mediates between both [6]. In the following, a CA architecture is assumed that joins both design patterns: The MVC model is decentralized and consists of a set of web services, among which a controller mediates. Inbound and outbound messages correspond to events. MDD of this CA architecture requires a corresponding metamodel. The WSCAM metamodel is theoretically founded in state transition models, with state transition diagrams as concrete syntax. Three types of nodes exist: an initialization node, a controller node and a set of service nodes. All nodes represent states. Transitions between states are achieved by operations, which are shown by labelled directed arcs. The following labels are allowed: operation names, ‘x’ (exit) and ‘ε’. Epsilon transitions can be made without consuming any input data [15]. Following the idea of MDD, the semantics of the WSCAM metamodel is translational. In its original sense, translational semantics maps elements of one language to elements of another one [15]. To obtain an approach that is applicable in practice, the elements of the WSCAM metamodel are linked to given software development artifacts for web services (definitions in WSDL and SCA). Both WSDL and SCA support the assignment of these artifacts by type (abstract level) and by implementation (binding). By mapping to WSDL and SCA, the WSCAM approach, which concentrates on dynamic aspects, is linked to the static approaches. Transitions are equivalent to WSDL operations and identified by their name. Since overloading operations has been removed in WSDL 2.0, the operation name uniquely identifies the messages exchanged (by type) [27]. Moreover, each WSDL 2.0 operation includes a message exchange pattern (MEP) [WS04]. In WSCAM diagrams, the MEP is depicted by the number and direction of the arrowheads. Roughly, both service nodes (see Table 2) and controller nodes correspond to SCA components. At the abstract level, service nodes must be associated with an interface and may own (abstract) SCA references, views and transformations. The interface maps to WSDL or local software components. SCA references [19] pool interfaces of invoked service nodes and can have attributes, which determine SCA policies (concerning transactions, security etc.), target service nodes and binding time. By defining a target (and its multiplicity), messages can be redirected, i.e., outbound messages in an in-out MEP can be sent to service nodes other than the sender of the inbound message. Views are necessary to realize the MVC pattern in the CA architecture: They allow users to access message data and are derived from the signatures of the WSDL operations assigned to the service node. Transformations can manipulate inbound or outbound messages to achieve type conformance for sending or receiving. As a SCA component, the controller node can be a local software component or a BPEL4WS process, which may also be referenced [19]. The view associated with the controller corresponds to (a subset of) the union of its inbound and outbound operations. These operations uniquely identify the message data types involved. By transformations, even centralized data persistency (the schema constructed from the message data types) can be tied to the controller node.

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Table 2. Comparison of WSDL descriptions, SCA components and WSCAM service nodes Service in WSDL 2.0 (1.1) Interface (Port type) Operation: • Signature • MEP Pattern Faults Types (DataTypes)

SCA Components    bidirectional, conversational ⎯ ⎯

Binding Endpoint (Port)

WSDL-Binding Binding, Implementation References (Attributes, Interface, Binding) Properties

WSCAM Service Nodes     ⎯ Derived from operation signature and binding Binding  ⎯ View Transformation

•

Initialization Node, >: Service Node, : Controller Node, T: Transformed, R: Redirected

Fig. 5. Alternative WSCAM diagrams for the sample CA

The implementation level defines bindings for each operation, each service node and each of its references. Moreover, it also assigns generator templates (based on SCA specifications) for views and transformations to the nodes. Fig. 5 shows alternative WSCAM diagrams for the sample CA of Section 1. The left diagram exactly reflects the WSDL descriptions of the services, since all operations use the in-out MEP. In the right diagram, the outbound message of getAirportInfoBy Country is transformed (to choose one airport town from the outbound list) and


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redirected to get the weather in the selected town (or a guide book). The application logic behind Fig. 1 corresponds to the right hand part of Fig. 4.

4 Conclusion The WSCAM (Web Service Component Architecture Modelling) approach presented in this paper is designed for the model-driven development (MDD) of both unconstrained and constrained (orchestrated) composite applications (CA) that invoke web services by user interaction or automatically. The architecture of the CAs created by WSCAM combines the design patterns ‘observer’ and ‘model-view-controller’: The view allows users to insert data and to interact with the CA, the controller listens to events and handles them by normal GUI logic or by calling a BPEL4WS process. To ensure the practical applicability of this approach, the elements of the WSCAM metamodel are mapped to (parts of) WSDL descriptions and SCA components. SCA (Service Component Architecture) is a standard for the implementation of software components that provide or use software services. Thus, SCA can serve as a basis for transformations from models to various implementation platforms. Such transformations are a major concern of MDD and are currently defined for WSCAM. The way the WSCAM controller handles operations, i.e., message exchange, is similar to the idea of an enterprise service bus [5]. However, an enterprise service bus that does not solely deal with web services, and, as a type of middleware, does not define views for user interaction. Because of relying on standards and tried software design patterns, WSCAM can be implemented. Such an implementation is on the way (using Eclipse).

References 1. van der Aalst, W.M.P., Weske, M., Grünbauer, D.: Case handling: a new paradigm for business process support. Data & Knowledge Engineering 53, 129–162 (2005) 2. Baresi, L., Heckel, R., Thöne, S., Varro, D.: A UML-Profile for Service-Oriented Architectures. In: Companion to the 19th Annual ACM SIGPLAN OOPSLA 2003, pp. 192–193. ACM Press, New York (2003) 3. Bauer, B., Müller, J.P.: MDA Applied: From Sequence Diagrams to Web Service Choreography. In: Koch, N., Fraternali, P., Wirsing, M. (eds.) ICWE 2004. LNCS, vol. 3140, pp. 132–136. Springer, Heidelberg (2004) 4. Bultan, T., Fu, X., Hull, R., Su, J.: Conversation Specification: A New Approach to Design and Analysis of E-Service Conversations. In: WWW 2003, pp. 403–410. ACM Press, New York (2003) 5. Chappel, D.A.: Enterprise Service Bus. O’Reilly, Beijing (2004) 6. Gamma, E., Helm, R., Johnson, R.B.: Design Patterns: Elements of Reusable ObjectOriented Software. Addison-Wesley, Amsterdam (1995) 7. Gronmo, R., Solheim, I.: Towards Modeling Web Service Composition in UML. In: Bevinakoppa, S., Hu, J. (eds.) 2nd International Workshop on Web Services: Modeling, Architecture, and Infrastructure, pp. 72–86. INSTICC Press, Porto (2004) 8. Hull, R., Su, J.: Tools for Composite Web Services: A Short Overview. SIGMOD Record 34, 86–95 (2005)

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9. Johnston, S.K.: UML 2.0 Profile for Software Services, http://www.ibm.com/ developerworks/rational/library/05/419_soa/ 10. Johnson, S.K., Brown, A.W.: A Model-Driven Development Approach to Creating ServiceOriented Solutions. In: Dan, A., Lamersdorf, W. (eds.) ICSOC 2006. LNCS, vol. 4294, pp. 624–636. Springer, Heidelberg (2006) 11. Karmarkar, A., Edwards, M.: Assembly of Business Systems Using Service Component Architecture. In: Dan, A., Lamersdorf, W. (eds.) ICSOC 2006. LNCS, vol. 4294, pp. 529– 539. Springer, Heidelberg (2006) 12. Leymann, F., Khalaf, R.: On Web Services Aggregation. In: Benatallah, B., Shan, M.-C. (eds.) TES 2003. LNCS, vol. 2819, pp. 1–13. Springer, Heidelberg (2003) 13. Kleppe, A., Warmer, J., Bast, W.: MDA Explained: The Model Driven ArchitectureTM – Practice and Promise. Addison-Wesley, Boston (2003) 14. Manolescu, I., Futurs, I., Brambilla, M., Ceri, S., Comai, S., Fraternali, P.: Model-driven Design and Deployment of Service-Enabled Web Applications. ACM Transactions on Internet Technology 5, 439–479 (2005) 15. Mandrioli, D., Ghezzi, C.: Theoretical Foundations of Computer Science. Wiley, New York (1987) 16. Object Management Group: Model-driven Architecture, Guide Version 1.0.1, http:// www.omg.org/cgi-bin/doc?omg/03-06-01 17. Object Management Group: Unified Modeling Language: Infrastructure Specification, Version 2.1.1, formal/07-02-06, http://www.omg.org/cgi-bin/doc?formal/ 07-02-06 18. Object Management Group: Business Process Modeling Notation, OMG Final Adopted Specification. dtc/06-02-01, http://www.bpmn.org/ 19. Open Service Oriented Architecture: Service Component Architecture Assembly Model V1.00, http://www.osoa.org/ 20. Skogan, D., Gronmo, R., Solheim, I.: Web Service Composition in UML. In: EDOC 2004, IEEE Press, New York (2004) 21. Thöne, S., Depke, R., Engels, G.: Process-oriented, flexible composition of web services with UML. In: Olivé, À., Yoshikawa, M., Yu, E.S.K. (eds.) ER 2003. LNCS, vol. 2784, pp. 390–401. Springer, Heidelberg (2003) 22. Weske, M.: Business Process Management: Concepts, Languages, Architectures. Springer, Berlin (2007) 23. Woods, D., Mattern, T.: Enterprise SOA: Designing IT for Business Innovation. O’Reilly, Beijing (2006) 24. World Wide Web Consortium: Web Service Choreography Interface (WSCI) 1.0, http://www.w3.org/TR/wsci/ 25. World Wide Web Consortium: Web Services Conversation Language (WSCL) 1.0, http://www.w3.org/TR/wscl10/ 26. World Wide Web Consortium: Web Services Description Language (WSDL) Version 2.0, Part 2: Message Exchange Patterns, http://www.w3.org/TR/2004/WD-wsdl20patterns-20040326/ 27. World Wide Web Consortium: Web Services Description Language (WSDL) Version 2.0, Part 1: Core Language, http://www.w3.org/TR/wsdl20/ 28. World Wide Web Consortium: Web Services Description Language (WSDL) Version 2.0, Part 2: Adjuncts, http://www.w3.org/TR/2007/REC-wsdl20-adjuncts20070626/ 29. Web Services Interoperability Organization: Basic Profile Version 1.2. Working Group Approval Draft, http://www.ws-i.org/Profiles/BasicProfile-1_2WGAD.html

Towards Identification of Migration Increments to Enable Smooth Migration Niels Streekmann1 and Wilhelm Hasselbring2 1

OFFIS - Institute for Information Technology Escherweg 2, 26121 Oldenburg, Germany [email protected] 2 Carl von Ossietzky University Oldenburg Software Engineering Group 26111 Oldenburg, Germany [email protected]

Abstract. The migration of existing systems is a major problem in today’s enterprises. These systems, which are often called legacy systems, are usually business critical, but difficult to adapt to new business requirements. A promising solution is the smooth migration of these systems, i.e. the systems are integrated into the system landscape and then migrated in a number of smaller steps. This leads to the question of how these steps can be identified. We propose a method based on a dependency model of the existing system and graph clustering analyses to identify these steps and define migration increments.

1

Introduction

The migration of existing systems is a recurring task in software engineering. According to [1] there are different approaches to migration, namely the migration to new environments (hardware, runtime and development) and the migration of the software architecture. We focus on the migration of the software architecture, whereby in practice this is often connected with the migration of at least one of the mentioned environment migrations. It is assumed that the implementation of an existing system is to be migrated to a target architecture to improve its maintainability. It is further assumed that the existing system uses a structured data store, e.g. a database or XML files to manage its data. The replacement of the data store in the new system depends on certain criteria that base on the characteristics of the existing system and the circumstances of the migration project. [2] describes such criteria and corresponding migration patterns, that are derived from industrial migration projects. The assumptions made are valid since many enterprises suffer from systems that are an important part of their business, but have become hard to maintain over the years. The reason for this phenomenon is that they have grown in years of maintenance so that the current systems do no longer respect the original architecture and data store design. This makes it hard to predict the R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 79–90, 2008. c Springer-Verlag Berlin Heidelberg 2008

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effect of further changes and increases the costs of future development. Another reason is that these systems often are implemented using outdated programming languages and database systems. An important requirement for existing systems is the integration with other systems and the adaptability to new business requirements. To gain the required flexibility to fulfil these requirements many systems are migrated to service-oriented architectures (SOA). Research in that area can e.g. be found in [3], [4] and [5]. The migration to SOA can be seen as the first step of a smooth migration process, that changes the externally visible interfaces of the system, but not the architecture of the system itself. In most cases the integration with other systems is implemented using service wrappers. The achievement of maintainability is not necessarily concerned with the integration with other systems but with the integration of new functionality into the existing system. An example can be found in [6], where the existing system is wrapped by services. These are used to access functionality of the existing system from newly implemented functionality in a different runtime environment and therefore integrates the existing system with newly developed functionality. To preserve the long-term maintainability of the integrated systems, the implementation of the systems has to be migrated to conform to its new architecture. In many cases this is only possible by reimplementing these systems. In other cases refactoring is an alternative. The decision which way is suited best depends on the quality of source code and data store design and also on the used development environment. There are several methods to perform the reimplementation. [7] gives an overview of possible approaches. We propose a smooth migration approach. Thereby the reimplementation process is performed in a number of small steps with minimal influence on the operational systems. The systems remain operational without restrictions throughout the migration process. Another advantage is that it is possible to add new functionality or change the migration plan according to new requirements. Further reasons for smooth migration are listed in [6]. Although we concentrate on component-based reimplementation in this paper, the approach described in Section 3 could also be a preliminary step for an extensive refactoring project. The contribution of this paper is an approach to identify migration increments that constitute the steps of a smooth migration process. The approach is based on dependency models of existing systems and graph clustering analysis. In contrast to other clustering-based migration approaches it incorporates the target architecture of the system. In this way it is prevented that a poorly structured existing system leads to an also poorly structured future system, which is the case, when the migration increments are only defined according to the dependencies in the existing system. The target architecture defines initial clusters of the existing system and thereby leads the migration process. Since the approach has not yet been evaluated in a case study it is described throughout the paper using an abstract example. The remainder of the paper is structured as follows. Section 2 defines the smooth migration process that is the basis of the described approach.

Towards Identification of Migration Increments to Enable Smooth Migration

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The identification of migration increments is described in Section 3. Section 4 describes related work before Section 5 concludes the paper.

2

Smooth Migration

Smooth migration represents a migration process that allows the incremental migration of existing systems. The smooth migration promises some benefits compared to other migration solutions like the cold turkey approach described in [7]. These benefits include that there is always a running system incorporating new functional requirements and that no parallel implementations are needed which would lead to higher costs. The incremental migration process is enabled by an initial integration of the existing system into its new environment. This is similar to the migration to SOA described in Section 1. The integration is based on the future architecture of the existing system, called target architecture. We differentiate between externally visible and internally visible interfaces of the target architecture. Externally visible interfaces can be used by other system to communicate with the system while internally visible interfaces are only used by internal components of the application that is in the scope of migration. Other systems or the implementation of new functionality, as described in [6], only use the system through interfaces described in the target architecture. The target architecture does not only describe the externally visible interfaces of the new system, but its architecture as a whole. Since the external view of the existing system conforms to the target architecture after the integration step, it is possible to migrate the implementation of the system in small steps without changing its external behaviour. The parts of the system migrated in one step are called migration increments. To avoid inconsistencies in the collaboration of the existing and the newly implemented system during the smooth migration process, the number and size of the migration increments has to be chosen according to their dependencies. The sequence of migrating the increments also depends on the dependencies and additionally on organisational aspects and requirements which lead to new functions of the system. The implementation of this new functionality should be carried out in the new system as far as this is possible to reduce dual implementation efforts. New requirements can also lead to changes in the sequence of the migration steps. The remainder of this section describes the integration of existing systems and the migration of their implementation according to the target architecture in more detail. 2.1

Integration into the Target Architecture

As a first step the target architecture has to be designed. This architecture represents the envisioned state of the existing system and considers maintainability and the ability to be integrated into existing or new system landscapes.

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The target architecture is the source for the creation of service wrappers for the existing system. In [8] van den Heuvel describes this creation with greater detail. By wrapping the existing system it becomes reusable in its target environment. From an external point of view the existing system can now be used in the same way as its successor. Figure 1 shows an abstract target component architecture and an existing system that is integrated using service wrappers. The interfaces of the service adapters correspond to the external interfaces of the target architecture. This target architecture is the basis for the abstract example used throughout the paper. It is shown how the existing system in Figure 1(b) implements the interfaces of the target architecture and how migration increments can be found to execute the migration of the system.

(a) Target architecture Service Wrapper 1 Interface 1

Existing System

Service Wrapper 2 Interface 2

(b) Existing system with service wrappers Fig. 1. Integration of an existing system based on a target architecture

2.2

Migration of the Implementation

The integration into the target architecture increases the reuseability of the existing system, however the internal quality of the system and thus its maintainability is not enhanced. In the long run however this is a critical aspect for the future use and extension of the system. Hence we propose to replace the service adapters of the existing system by interfaces implemented in components of the target system in small steps.


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Therefore all interfaces of the target architecture and not only the externally visible interfaces have to be mapped to functions of the existing system. Figure 2 shows the mapping of internal interfaces (1 and 2) and external interfaces (3-5) of the target architecture to functions of the existing system for the example from Figure 1 and the dependencies between the units of the existing system. Figure 3 shows an intermediate state of the smooth migration of the existing system. The interfaces 1 and 2 are already implemented in a new environment by component 1 and component 2. These still use parts of the existing system through the internal interfaces described in the target architecture.

Fig. 2. Architecture model of the existing system including all target architecture interfaces

An important challenge is to find the increments of the stepwise migration, i.e. to define which parts of the existing system are replaced in the single steps. The ideal would be to migrate every interface individually. This is rarely possible in practice, since the implementation of an interface in the existing system usually has internal dependencies that make this approach infeasible. The dependencies that are considered in the approach are described in Section 3.1. In the following section we present an approach to identify migration increments based on dependency models. Thereby we initially focus on static functional and data dependencies. The increments are related to one or more interfaces and are characterised by minimal dependencies between the increments. The goal is to minimise the effort to resolve the dependencies between the increments in the existing code to ease the migration. In the worst case the implementation of all interfaces has to be migrated at once, which equals the cold turkey approach.

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Fig. 3. Partly migrated system

3

Identification of Migration Increments

Migration increments are the main objects in the execution of a smooth migration path. The goal is to subdivide the existing system into groups of units with high cohesion and low coupling. Units are structural entities in the implementation of existing systems, e.g. classes and methods in object-oriented programming or database tables. By doing so it is assumed that the reimplementation of the increments causes as little refactoring effort as possible. It is expected that refactorings of the existing system are necessary to enable the usage of code that has already been reimplemented in the new system. The identification of migration increments is based on the interfaces defined in the target architecture. Implementations of these interfaces are created in the integration phase of the smooth migration process using adapters. Each migration increment has to be based on at least one interface. When a migration increment is based on an interface, it has to include all units that directly implement this interface. This includes at least all methods, functions etc. that implement an operation of the interface. Dependent units are not necessarily part of the same migration increment. If the target implementation is component-based the ideal partitioning of migration increments probably includes one migration increment for all interfaces of one component. In this case a new component can be developed in one step and replace a certain part of the existing system. A simple example for this case is shown in Figure 4(a). In practice however this ideal cannot always be reached. There may be internal dependencies in the existing system that can not be solved by efficient refactorings. In this case it will be more efficient to migrate the implementation of a number of interfaces in one migration increment. Figure 4(b) shows such a case. The feasibility of a migration plan based on the migration increments depends on the dependencies between the increments. They have to be solved by a mapping to the interfaces of the target architecture or by refactorings in the existing system. To keep the effort for these refactorings as low as possible, the migration increments need to have high cohesion and low coupling. The metric for coupling


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(a) One migration increment for each in- (b) Migration of several interfaces in one terface increment Fig. 4. Partitionings of migration increments

and cohesion used here bases on dependencies among units. More details about the metrics are given in the next section. 3.1

Dependencies

There are manifold dependencies within a software system. Most research on dependencies concentrates on functional dependencies, as e.g. [9]. Functional dependencies are e.g. function calls, inheritance of using classes as types of variables and parameters. Besides functional dependencies data dependencies are the essential influence on the decoupling of software systems. We assume that the existing systems use a structured data store. Since there are many existing systems (also called legacy systems) with a poor and grown database design the database is in many cases also migrated to a new design. Criteria and patterns that support the decision whether to migrate the data store or not are given in [2]. The design of the data store has a strong influence on the definition of migration increments, since interfaces implementations that work on the same data often cannot be migrated separately, because this would lead to data inconsistencies. Therefore the dependencies between code and data also have to be considered in the dependency model. In a first step we examine the dependencies between functional code and database tables. It is subject to future work to examine whether the consideration of single table columns may enhance the quality of migration increment definitions in special cases. We defined a first dependency metamodel to describe dependency models of existing systems. The abstract classes of the metamodel are shown in Figure 5. The main elements are units and dependencies between them. Units can be code units or data units. Concrete classes of units and dependencies are omitted in Figure 5 due to readability. Examples are given in the following explanations. Code units are building blocks of imperative and object-oriented programming languages. Existing systems developed according to other programming paradigms

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N. Streekmann and W. Hasselbring CodeUnit Dependency -name -weight

Generalisation

Unit

has

-name on

Part

DataUnit

Usage

Datastore Reference

Reference

Fig. 5. Dependency Metamodel

are not considered in our approach. Examples of concrete classes of code units are function, class or method. Data units describe different ways of data representation. These can be any kind of data structures as e.g. structures in an XML file or database tables. It is also possible to define weights for dependencies. These can be used in analyses to evaluate the effort that is necessary to refactor the existing system in order to use functionality that has already been migrated to the target environment. Dependencies are relations between two code units, two data units or a code and a data unit. Figure 5 shows abstract classes of data units. Generalisation dependencies refer to concepts of object-oriented programming as inheritance and interface implementation. An example of a part dependency is the containment of methods and attributes in classes. Usage dependencies are function and method calls as well as the usage of a certain database table. Data store references are internal dependencies of data stores as e.g. references in an XML document or a foreign key relation in a database. Reference dependencies refer to the type concept in imperative programming and cover attribute and variable types as well as parameter types. The construction of a dependency model for an existing system is not part of the approach. However there are several methods to create such a model. Preferably the creation of the dependency model should be automated. The modelling of all dependencies of a complex system by hand is far to laborious to be applied in practice. There already are methods to automate this task, namely the analyses of the source code and the monitoring of the system at runtime. The first method requires existing analyses tools for the programming language the system is implemented in. The advantage is that no further efforts are needed, while the disadvantage is, that only static dependencies can be found. The second method requires some kind of instrumentation in order to execute the monitoring, but has the advantage that also dynamic dependencies that occur in the monitoring process can be found. 3.2

Analyses

To identify migration increments based on the dependency model, we need a suitable analysis algorithm. The analysis we propose for this purpose is graph


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clustering. Therefore the dependency model is transformed to a graph model. The nodes of the graph represent units while dependencies are modelled by edges. To model multiple dependencies between units or the degree of the dependencies, weights can be assigned to the edges. Weights for the degree of dependencies are also used in [9]. An extensive overview of graph clustering is given in [10]. The result of the analysis are graph clusters that represent migration increments. Figure 6 shows an exemplary graph with clusters according to the partitioning example from Figure 4(a). The units in the graph correspond to the functions with the same numbers in that figure. Since migration increments contain one or more interfaces defined in the target architecture, it has to be defined which units of the system constitute the implementation of one interface as stated above. These are necessarily part of the same migration increment. In this way the mapping between source and target architecture is addressed in the definition of migration increments.

Fig. 6. Graph clustering example

The goal of graph clustering algorithms is to minimise the number or weights of the edges between the clusters. This corresponds to the application for the identification of migration increments, since the edges represent the dependencies between units and therefore minimising connections between clusters corresponds to the minimisation of coupling and maximisation of cohesion in migration increments. To incorporate the link to the target architecture into the algorithm that defines clusters on the dependency model of the existing system, we propose to define initial clusters on the basis of the interfaces of the target architecture. These clusters include all units that directly implement the interfaces. The clustering algorithm will then add the remaining units of the existing system to the

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clusters based on the dependencies they have to the initial clusters. In a second step it has to be checked whether the dependencies between two clusters can be solved. If the refactoring effort is to high it should be taken into consideration to migrate the clusters as one migration increments as shown in Figure 4(b). The identified migration increments are the basis of the smooth migration path of the existing system. The implementation of new components will replace the according units of the existing system, grouped in the migration increment that implements the same interfaces as the new component. The order of the reimplementation of migration increments is based upon the dependencies between the migration increments and new functional requirements that need to be fulfilled by the system. New requirements should preferably be implemented in the new runtime environment to reduce the effort of repeated implementations. This corresponds to the Dublo pattern described in [6].

4

Related Work

The research project Application2Web [11] has the goal to migrate existing systems to modern web applications. These can be seen as a subset of the more general goal to migrate existing systems to new architectures described here. The project uses graph clustering based on static dependencies between code units to identify the components of the new system. Therefore the basic concept is the same as in the approach described here. The difference is that components of the target architecture and their interfaces are already given in our approach and that our goal is to find ideal migration increments for a smooth migration process. [12] and others describe methods for the transformation of software systems, whereby transformation means the automated transfer into a new environment. Our approach assumes the reimplementation of software systems instead. The reason is that we concentrate on existing systems that lack a high quality software architecture that allows for the long-term usability of the system. The main goal of the approach is to migrate existing systems to that kind of architectures.

5

Conclusions and Future Work

The paper introduces an approach to identify migration increments in a smooth migration process. The identification is based on a target architecture and a dependency model of the existing system describing the dependencies between units in the system. The units are grouped in migration increments using graph clustering algorithms. These migration increments are the building blocks of a smooth migration path. In our future work we will examine whether the Knowledge Discovery Metamodel [13] is suitable to replace our own dependency metamodel. This would


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conform our approach to more standardised methods and would ease the integration with reengineering tools that also support the KDM. The goal of the KDM is to define a common representation of existing systems for the integration between analysis tools. Furthermore suitable graph partitioning algorithms have to be found for the implementation of the approach and transformations have to be defined from dependency and target architecture models to input models of the algorithms. More work also has to be done on the mapping from analysis results to a smooth migration plan. The approach is going be evaluated in case studies. The goal of these studies is to find out whether the graph clustering algorithms executed on dependency models lead to suitable migration increments of existing systems that fit the target architecture. Furthermore they will show which degree of dependencies in an existing system is acceptable in order to migrate it in a smooth migration process and from which degree on it is more reasonable to migrate the system using another migration method.

References 1. Gimnich, R., Winter, A.: Workflows der Software-Migration. SoftwaretechnikTrends 25(2), 22–24 (2005) 2. Hasselbring, W., B¨ udenbender, A., Grasmann, S., Krieghoff, S., Marz, J.: Muster zur Migration betrieblicher Informationssysteme. In: Tagungsband Software Engineering 2008, February 2008, K¨ ollen Druck, M¨ unchen (2008) 3. Ziemann, J., Leyking, K., Kahl, T., Werth, D.: Enterprise Model driven Migration from Legacy to SOA. In: Gimnich, R., Winter, A. (eds.) Workshop SoftwareReengineering und Services, pp. 18–27. University of Koblenz-Landau, Germany (2006) 4. Winter, A., Ziemann, J.: Model-based Migration to Service-oriented Architectures - A Project Outline. In: Sneed, H. (ed.) CSMR 2007, 11th European Conference on Software Maintenance and Reengineering, Workshops, vol. 3, pp. 107–110. Vrije Universiteit, Amsterdam (2007) 5. Sneed, H.M.: Migration in eine Service-orientierte Architektur. SoftwaretechnikTrends 27(2), 15–18 (2007) 6. Hasselbring, W., Reussner, R., Jaekel, H., Schlegelmilch, J., Teschke, T., Krieghoff, S.: The Dublo Architecture Pattern for Smooth Migration of Business Information Systems: An Experience Report. In: Proceedings of the 26th International Conference on Software Engeneering (ICSE 2004), May 2004, pp. 117–126. IEEE Computer Society Press, Los Alamitos (2004) 7. Brodie, M.L., Stonebraker, M.: Migrating legacy systems: gateways, interfaces & the incremental approach. Morgan Kaufmann Publishers Inc., San Francisco (1995) 8. van den Heuvel, W.J.: Aligning Modern Business Processes and Legacy Systems A Component-Based Perspective. MIT Press, Cambridge (2007) 9. Hitz, M., Montazeri, B.: Measuring Coupling and Cohesion In Object-Oriented Systems. In: Proceedings of the 3rd International Symposium on Applied Corporate Computing (ISACC1995) (October 1995) 10. Schaeffer, S.E.: Graph Clustering. Computer Science Review 1(1), 27–64 (2007)

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11. Andriessens, C., Bauer, M., Berg, H., Girard, J.F., Schlemmer, M., Seng, O.: Strategien zur Migration von Altsystemen in komponenten-orientierte Systeme. Technical report, Fraunhofer IESE / FZI Karlsruhe (2002) 12. K¨ uhnemann, M., R¨ unger, G.: Modellgetriebene Transformation von Legacy Business-Software. In: 3. Workshop Reengineering Prozesse (RePro2006),Software Migration. Mainzer Informatik-Berichte, vol. 2, pp. 20–21 (2006) 13. Gerber, A., Glynn, E., MacDonald, A., Lawley, M., Raymond, K.: Knowledge Discovery Metamodel - Initial Submission, OMG Submission admtf/04-04-01 (2004)

Service-Based Architecture for Ontology-Driven Information Integration in Dynamic Logistics A. Smirnov, T. Levashova, N. Shilov, and A. Kashevnik St.Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 39, 14 line, 199178, St.Petersburg, Russia {smir,oleg,nick,alexey}@iias.spb.su

Abstract. The paper describes an approach to information and knowledge integration based on usage of such technologies as Web services, radiofrequency identification (RFID), global positioning systems (GPS), etc. To provide for the information integration at the level of semantics, the ontological model is used. The model is based on the knowledge represenation formalism of objectoriented constraint networks. The architecture of the system implementing the proposed approach is based on the idea of self-organising networks whose nodes represent agent-based Web-services. Dynamic logistics has been chosen as the application domain for the approach.

1 Introduction Currently the recognition of the need for organizational structures that could be distributed, mobile and flexible and therefore exhibit characteristics of innovation, resilience, and self-management, is growing. One of the most widespread forms of organizational structures is the form of flexible supply chain. Logistics systems play an important role in companies based on this concept. And they become even more important when build-toorder (BTO) supply networks are considered where customized products are built upon receipt of customer orders without precise forecasts, inventory, or purchasing delays. An intelligent decision making support based on such technologies as Web services, radiofrequency identification (RFID), global positioning systems (GPS), etc. and integration of information from heterogeneous sources may significantly enhance the logistics system abilities (e.g., reduce costs and times of delivery) especially in the dynamic environment of BTO supply chains. An important factor for efficient implementation of decision support systems providing intelligent support to logistics is usage of up-to-date technologies supporting automated logistics management systems. One of such technologies is the technology of RFID for automated product tracking. RFID is a way of storing and remote writing/reading data via radio signals to/from small and cheap devices (RFID tags or transponders). This technology enables collecting information about objects (e.g., materials and products), their locations and transportations, performing tracking and getting information concerning operations with the object without human intervention and with minimal amount of errors. R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 91–101, 2008. © Springer-Verlag Berlin Heidelberg 2008

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In supply chains centralized control is likely impossible due to different subordination of partners involved, and other reasons. Another disadvantage of the centralized control is its possible failure that would cause stopping the entire system. A possible solution for this is organization of a decentralized self-organizing coalitions consisting of flexible supply chain members. For information integration purposes it is currently reasonable to use Web services since they are becoming a de-facto standard for information exchange and sharing.

2 Model-Driven Approach to Information Systems Building Model-driven approaches to systems development is a relatively new and intensively elaborated paradigm (e.g., Stahl and Voelter, 2006). Model-driven paradigm assumes orientation to models at all stages of the system development, operation and discontinuation. In software development this paradigm facilitates the automatic generation of significant amounts of code, which otherwise would need to be generated manually (Seidman and Ritsko, 2006). This has lead to appearance of such approaches as for example model-driven software factories (Langlois and Exertier, 2004). Application of the above paradigm to a wide range of application can be fruitful as well. We propose to use it for building an information system for decision support in the area of dynamic logistics. Such decision support would require integration of information from various sources. There are different research efforts undertaken in this field. In (Balmelli et al., 2006) an approach is presented to systems development mainly based on usage of UML. For distributed systems the service-oriented architecture as one of the component paradigm is often used (e.g., Atkinson et al., 2002). Definition of enterprise information architectures based on the business component identification (BCI) method is described in (Albani et al., 2006). In this work an enterprise ontology is used and three types of relationships for business component identification: relationships between single process steps, between information objects and between process steps and information objects. In this paper an approach to model-driven building a distributed information integration system is presented. It is proposed to integrate environmental information and knowledge in context as a problem model (Smirnov et al., 2005). The context is purposed to represent only relevant information and knowledge from the large amount of those. Relevance of the information and knowledge is evaluated on a basis how they are related to modelling of an ad hoc problem. It is done through linkage of stored structured knowledge represented via ontologies with parametric knowledge / information from information sources. The approach (Fig. 1) considers context as a problem model built using knowledge extracted from the application domain and formalized within an ontology by a set of constraints. Two types of context are used: 1) abstract context that is an ontologybased model integrating information and knowledge relevant to the problem, and 2) operational context that is an instantiation of the abstract context with data provided by the information sources or calculated based on functions specified in the abstract context.

Service-Based Architecture for Ontology-Driven Information Integration Application Ontology

Problem Model (Abstract Context)

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Operational Context Decision

Application Domain

Problem

Current Situation

Fig. 1. Context-based decision support

3 Ontological Model for Information Integration Before the information integration can take place the ontological model describing the problem domain and represented by the application ontology has to be built. The application ontology (AO) combines the domain knowledge described in domain ontologies and problem solving knowledge described in the task and methods ontologies. Domain ontologies, in turn, are built based on existing ontologies describing related domains. AO integrates domain knowledge and problem solving knowledge. At the moment this is proposed to be done by experts supported by developed ontology management environment. In the presented approach the formalism of Object-Oriented Constraint Networks (OOCN) is used for a formal ontology representation. According to this representation an ontology (A) is defined using a set of object classes (“classes”), each of the entities in a class is considered as an instance of the class; a set of class attributes (“attributes”); a set of attribute domains (“domains”); and a set of constraints. For the chosen notation the following six types of constraints have been defined: (1) accessory of attributes to classes; (2) accessory of domains to attributes; (3) classes compatibility (compatibility structural constraints); (4) hierarchical relationships (hierarchical structural constraints) “is a” defining class taxonomy, and “has part”/“part of” defining class hierarchy; (5) associative relationships (“one-level” structural constraints); and (6) functional constraints representing functional dependencies between attribute values. More detailed description of the formalism can be found in (Smirnov et al., 2007). In the OOCN-formalism tasks and methods are represented as classes, the sets of their arguments and argument’s types are represented by sets of attributes and domains, respectively. The methods are configured into the task in accordance with task-method decomposition structure. Methods involved in task solving are represented by “part-of” relationships. Alternative methods are represented by “is-a” relationships. For example, the methods Truck availability and Truck location determine availability and location of transportation vehicles. The task of determination of the availability of a route is represented by two alternative methods (Fig. 2): a raw method and a precise method. These methods take different sets of input arguments and return results of different precision. Unlike the precise method, the raw method does not take into account visibility.

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4 Technological Framework As it was mentioned self-organising networks provide a better coordination for communities of independent parties. The generic scheme of a self-organizing service network is presented in Fig. 3. Each supply chain member is represented as an intelligent agentbased service (or several services) acting in the system. Each service has its own knowledge stored in its knowledge base. This knowledge is described by a portion of the common shared application ontology related to the current service’s tasks and capabilities and called context. Capabilities, preferences and other information about the service are stored in its profile that is available for viewing by other agent-based services of the system. This facilitates communication, which is performed via the communication module responsible for meeting protocols and standards that are used within the system. The services communicate with other services for two main purposes: (1) they establish links and exchange information for better situation awareness; and (2) they negotiate and make agreements for coordination of their activities during the operation. The services may also get information from various information sources, for example, local road network for transportation can be acquired from a geographical information system (GIS). Route availability

part-of Get point

is-a

Route availability (raw method)

Route availability (precise method)

Road floodability Get road

Beginning of road

Get Precipitation

Weather conditions

Get Temperature

Get Wind Direction

Get Wind Speed

Get Visibility

Ending of road

Fig. 2. Route availability: alternative methods Common shared application ontology

Information source

Agent-based service Agent-based service

Alternative information sources

Agent-based service

OWL-based information exchange SOAP-based information exchange

Fig. 3. Generic scheme of a self-organising network

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To make agent-based services independent a component Service Directory Facilitator (SDF) has been proposed. SDF is responsible for service registration and update of autonomous services (Fig. 4). Initially a service does not have any information about neighbouring services and its portion of the application ontology is empty. An automatic tool or an expert assigns it a set of the application ontology elements related to the service. For knowledge source services this could be classes which can be reified or attributes those values can be defined using content of knowledge sources. For problem solving services this could be tasks and methods existing in the problem domain. Service Directory Facilitator

PSS

KSS

PSS

KSS

KSS PSS

PSS KSS PSS – Problem Solving Service KSS – Knowledge Source Service

PSS KSS

PSS

Fig. 4. Registration and update of services

Services inform SDF about their appearance, modifications, intention to leave the system and send scheduling messages to update the internal repository. The task of SDF is to build a slice of the application ontology for each service, update references to the neighbouring services, maintain a list of services (type, location, schedule, and notification of services about changes in the network). Organization of references between services is a complex task and is out of scope of the paper. Well-organized references result in a list of services which are used as initiators of the self-organisation process.

5 Web-Services for Information Acquisition Within AO the domain and tasks & methods ontologies are interrelated by relationships specifying values of which class attributes of the domain ontology serve as input arguments for the methods of the task & methods ontology. Infromation and knowledge acquired from outside the system is described via references to information / knowledge sources. Knowledge source services are responsible for the interaction with information / knowledge sources in the following ways: • • • • •

representation of information provided by information sources by means of the OOCN-formalism; querying information sources; transfer of the information to the system; information integration; data conversion.

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The following types of information sources are distinguished: • • • • •

sensors are physical devices that detect a signal, physical conditions, etc; Web-services working with sensors have to support their outputs and interfaces. databases are organized collections of information; Web-services that work with databases have to support SQL queries; Web-sites and RSS (RDF Site Summary) are textual information with predefined structure; Web-services that work with this sources have be able to work with RSS structure; users may know large amount of information and can pass it to the Webservice through graphical user interface (GUI); other information sources allowing interaction through Web-services or for which appropriate Web-services can be developed.

The mechanisms of interaction between information sources and their assigned services are out of the scope of this research. Above regard the term “information source” will denote the information source itself and the Web-service assigned to it together. For abstract context instantiation the values acquired from information sources are used. These values are taken from the Web-services that are identified in the application to supply values to particular attributes. If an attribute obtain values from several sources, this information is integrated. For this purpose special functions are implemented in the Web-services. The values can also be obtained as results of calculations (functional constraints included into the abstract context). Since the approach uses the formalism of object-oriented constraint networks, the resulting operational context is a constraint network with valued variables. In Fig. 5 the scheme of retrieving information from the information sources is shown. One or more class attributes contained in the abstract and operational contexts can request needed information from one Web-service that, in turn, can request this information from one or more information sources. When necessary the Web-service calculates the value(s) of the attribute(s) based on information from different information sources. Attribute 1

Attribute 2

Attribute 3

Web-service 2 Web-service 1

Information source 1



Fig. 5. Retrieving information from information sources


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Exemplified tasks to be solved in the tasks & methods ontology are (Table 1): • • •

define the number of required trucks taking into transportation order type and volume; determine route availabilities based on the current weather conditions; select trucks for transporting cargo to warehouses based on their current availabilities, warehouses locations and availabilities, and route availabilities;

Tasks of the same row in Table 1 are implemented within one Web-service. Weather conditions are provided by sensors. The sensors provide values for precipitation, temperature, wind speed and directions, and visibility. In order to specify that, a set of methods taking values from the appropriate sensors are introduced in AO. These methods in the tasks & methods ontology are represented as classes Get Precipitations, Get Temperature, Get Wind Direction, Get Wind Speed, and Get Visibility respectively. To specify that a method takes the value from the sensor, URI of the Web-service responsible for the interactions with this sensor is introduced as an input argument of the method. The output argument of the method is the value the Web-service returns. In the operational context this value will instantiate an appropriate attribute specified in the class Weather. An example of “linking” AO and sensors is given in Fig. 6. In the example a sensor provides values of the current air temperature. URI of the Web-service responsible for these values (attribute Servive_URI) is specified as an input argument of the method Get Temperature. The method’s output argument is specified as Temperature. In order to specify dependencies between domain and tasks & methods constituents within AO, functional constraints are used. These constraints state what domain knowledge is involved in problem solving and in what way. For instance, the method Get Temperature outputs the temperature value. This value is used to instantiate the attribute Air temperature of the class Weather. In order to specify this, a functional constraint is introduced between the classes Weather and Get Temperature. Fig. 6 shows specification of the function between attribute Air Temperature and the output argument Temperature of the method Get Temperature. The functional constraint AirTemperature is introduced in the class Weather. The value of this function is calculated by the method Get Temperature (the bottom list box). In the top list box the attribute Air Temperature is chosen as a value returned by the method Get Temperature. The fact that the method Get Temperature returns Temperature as the output argument has been specified above. For the purpose of testing the possibilities of the RFID technology a table imitator that allows performing different experiments has been built. The RFID system consists of tags storing the data and readers or sensors (devices for writing and reading the data to and from tags). The antenna of the reader emits a radio signal that is received by the tag and powers its microchip. Using the received energy the tag performs an exchange of radio signals with the reader for self-identification and data transfer. Then, the reader sends the information received into the information system. RFID doesn’t need a direct connection or direct visibility; the tags are read quickly and precisely, through dirt, steam, water, paint, plastics, etc.; tags can store relatively large volume of information. This technology enables a wide range of possibilities in the areas of logistics, identification, access control, and other.

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GIS

Task

Description

Input

Output

Get point Get Latitude Get Longitude

Returns array of points of the region with their coordinates

Service_URI

IDs and coordinates of points

Get road Road floodability Beginning of road Ending of road

Returns array of roads of the region with their properties

Service_URI

IDs and “floodability” of roads, connected points (beginning and ending)

Get temperature

Returns current temperature

Service_URI

Temperature

Get wind

Returns current wind conditions

Service_URI

Wind speed and direction

Get precipitations Get visibility

Returns current precipitations and visibility conditions

Service_URI

Precipitations, visibility

Trucks

Truck availability

Returns list of trucks in the region and service URIs for acquiring their properties

Service_URI

IDs, types and service URIs of available trucks

Truck

Truck location Get truck

Returns current location of the given truck

Service_URI Truck id

Location point ID

Warehouses

Get warehouse availability Get warehouse location

Returns a list of warehouses in the region and related information

Service_URI

IDs of the warehouses, their addresses, free capacity, availability

Calculations

Route availability

Checks if a road is currently available or not for a given vehicle type

Road, its id and properties (e.g., flooadbility), vehicle type, weather conditions

Available / not available

Quantity of trucks

Calculates required quantity of trucks

Order type Order volume

Required number of trucks

Weather


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Fig. 6. Linking AO and information sources

The scheme of coupling the table imitator with the presented technological framework is given in Fig. 7. RFID tags installed on trucks and containers are read by the readers. A PC with installed software processes this information to define current locations of the trucks and containers (in real life can be combined with GPS or similar systems) and statuses of deliveries. This information is used by the system to update delivery plans and schedules. For this purpose the system also acquires information about the road network of the region provided by a GIS, the traffic situation is acquired from an Intelligent Transportation System, current weather conditions are provided by a weather service, available warehouses and their current capacities are acquired from a special directory. Currently, the table imitator is equipped with RFID readers ID ISC.M02 and ID ISC.PR101, and RFID tags RI-I11-112A-03. The characteristics of this equipment can be found in (FEIG Electronic 2007; TI 2007). Car models are used for movement imitation. The case study considers a dynamic transportation problem. There are several trucks in different known locations that change in time. The goal is to provide for the transportation of cargo in accordance with existing orders. Though the problem seems to be simple it appears to be more complicated than it looks. For example, it can be more reasonable for one vehicle to make two or more rides instead of using two or more vehicles. Possible solution for the dynamic transportation problem is presented in (Fig. 8). The implemented system has a Web-based interface. This means that regular Web browsers can be used for working with the system. The decision maker can see an interactive map and choose different parameters and criteria for problem solving. The drivers of the vehicles receive their assignments via Internet as well. They can see their routes using PDA or mobile phones.

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RFID

Class Attribute

Methods: Routing() Context TransportationPlanning() … Delivery Status

Location Truck ID Container ID Associative Relationships

Data from Information Sources

Fig. 7. Coupling RFID table imitation with technological framework

- warehouses - vehicles - unavailable roads - depot location

Fig. 8. Example of the solution for the dynamic transportation problem


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6 Conclusion The paper represents a technological framework and architecture for the approach to information integration based on usage of ontologies, Web-services and self- organizing networks. The paper describes the common shared application ontology that is used by the network members interoperability at the level of semantics. Application of Webservices enables for interoperability at the technological level. The dynamic logistics problem in BTO supply chains is chosen as the application domain of the proposed approach, where intensive information integration, exchange and sharing are required.

Acknowledgements The research described in this paper is supported by grants from following projects: Integrated Project FP6-IST-NMP 507592-2 "Intelligent Logistics for Innovative Product Technologies" sponsored by European Commission; projects funded by grants # 05-0100151 and # 06-07-89242 of the Russian Foundation for Basic Research; projects funded by grants # 16.2.35 of the research program "Mathematical Modelling and Intelligent Systems", and # 1.9 of the research program “Fundamental Basics of Information Technologies and Computer Systems” of the Russian Academy of Sciences (RAS) and project of the scientific program of St.Petersburg Scientific Center of RAS.

References Albani, A., Dietz, J.: The Benefit of Enterprise Ontology in Identifying Business Components. In: The Past and Future of Information Systems: 1976-2006 and Beyond. In: Proceedings of IFIP 19th World Computer Congress, TC-8, Information System Stream (WCC 2006), pp. 243–254 (2006) Atkinson, C., Bunse, C., Groß, H.-G., Kühne, T.: Towards a General Component Model for Web-Based Applications. Annals of Software Engineering 13(1-4), 35–69 (2002) Balmelli, L., Brown, D., Cantor, M., Mott, M.: Model-Driven Systems Development. IBM Systems Journal, IBM 45(3), 569–585 (2006) FEIG Electronic (2007) Web site, http://www.feig.de/index.php?option= com_content&task=view&id=71&Itemid=134 Langlois, B., Exertier, D.: MDSOFA: A Model-Driven Software Factory. In: Position papers for OOPSLA & GPCE Workshop Best Practices for Model Driven Software Development, electronic resource (2007), http://www.softmetaware.com/oopsla2004/langlois.pdf Seidman, D.I., Ritsko, J.J.: Preface. IBM Systems Journal, IBM 45(3), 449–450 (2006) Smirnov, A., Pashkin, M., Chilov, N., Levashova, T.: Constraint-driven methodology for context-based decision support. Design, Building and Evaluation of Intelligent DMSS (Journal of Decision Systems) 14(3), 279–301 (2005) Smirnov, A., Levashova, T., Shilov, N.: Semantic-oriented support of interoperability between production information systems. International Journal of Product Development, Inderscience Enterprises Ltd. 4(3/4), 225–240 (2007) Stahl, T., Voelter, M.: Model-Driven Software Development: Technology, Engineering, Management, p. 444. Wiley, Chichester (2006) Texas Instruments (2007), Web site http://www.ti.com/rfid/shtml/prod-trans.shtml

State of the Art on Topic Map Building Approaches Nebrasse Ellouze1, Mohamed Ben Ahmed1, and Elisabeth Métais2 1

Ecole Nationale des Sciences de l’Informatique, Laboratoire RIADI Université de la Manouba, 1010 La Manouba, Tunisie {nebrasse.ellouze,mohamed.benahmed}@riadi.rnu.tn 2 Laboratoire Cedric, CNAM 292 rue Saint Martin, 75141 Paris cedex 3, France [email protected]

Abstract. Topic Maps standard (ISO-13250) has been gradually recognized as an emerging standard for information exploration and knowledge organization. One advantage of topic maps is that they enable a user to navigate and access the documents he wants in an organized manner, rather than browsing through hyperlinks that are generally unstructured and often misleading. Nowadays, the topic maps are generally manually constructed by domain experts or users since the functionality and feasibility of automatically generated topic maps still in progress. In this paper, we give an overview of Topic Map building approaches. These approaches take as input different data types: structured documents, structured knowledge, unstructured documents and semi-structured data, and propose different techniques to build a Topic Map such as merging, mapping from RDF to TM and learning techniques. Some other research works are dedicated to cooperative Topic Map building and another research area deals with automatic generation of TM from XML documents. Keywords: Knowledge Extraction, Knowledge Representation, Topic Map (TM), Automatic Construction.

1 Introduction Studies have shown that Topic Map [1] authors face major difficulties in constructing topic maps similar to the difficulties associated with ontology construction. For users with no prior experience, generating topic maps manually can be a hard and time consuming task. The major difficulties that they faced were related to ontology building, i.e. to content conceptualization and classification, and to identifying and naming topics and relationships between these topics. Indeed, in most cases, TM authors had to deal with large and complex information systems involving a great diversity of resources, concepts and actors. Thus, TM construction can be very costly and can quickly become a bottleneck in any large-scale application if recourse is not made to automatic or semi-automatic building approaches. This paper gives an overview of Topic Maps construction proposed for building and generating Topic Maps. In section 2, we give a brief introduction to the Topic Map model. We describe, in the section 3, the general tasks involved in the TM R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 102–112, 2008. © Springer-Verlag Berlin Heidelberg 2008

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construction process. Then, in section 4, we present some related works about TM building. The last section aims to be a conclusion of the state of the art in the TM building area.

2 Topic Maps Topic Maps are an ISO standard which allows to describe knowledge and to link it to existing information resources. They are intended to enhance navigation in complex and heterogeneous data sets. The key features of Topic Maps are: topics representing entities of the modelized domain, and associations connecting topics and identifying the role played by each topic in the association. Topics can be identified by names and characterized buy occurrences (intrinsic properties). Two specific binary associations are standardized for any TM: Class-instance association and superclasssubclass association. The Topic Map standard is like an intelligent extension of the index of books with key features as topics, associations between topics, and occurrences of topics. It is very flexible in merging and extending different sets of Topic Maps. Topic Maps were designed to solve the problem of large quantities of unorganized and heterogeneous information in a way that can be optimized for navigation. We can observe a growing interest in the use of Topic Maps for modeling and sharing knowledge, and consequently tools and methods have been proposed to ease their management. There are some commercial tools [2] compliant with the ISO 13250 [1] TM standard and with the XML Version 1.0 Specification [3] and available for creating, manipulating and publishing Topic Maps from Ontopia, Infoloom, Empolis and other vendors. There is even an effort to standardize the API used by vendors, called Topic Map API (TMAPI). There are also some open source tools [2], we can classify these tools according to their main functionalities and the support brought to the user, we distinguish three categories: TM engines like TM4J, tinyTIM and XTM4XMLDB used to load TM from XML documents, store TM in databases and modify and access TM, TM navigators such as TMNav and TM editors like TM4L Editor and Viewer.

3 Main Functionalities Involved in the Topic Maps Construction Process In this section we are going to present the main functionalities involved in the Topic Map construction process. To illustrate this, we will present an example [4] whose subject is a company and its employees. Topic Map construction is a process requiring at least three main phases: The first one is focused on knowledge sources preparation: (textual corpus, collection of web documents, XML documents), eventually using a priori knowledge (domain ontologies, taxonomies, learning repositories, dictionaries etc.); The second phase starts by identifying topics types, the topics that define classes (or types) of topics for example “Person”, “Company”, associations such as “employedBy”, and occurrences such as “web site”. After that, it consists on identifying topics e.g., “Pepper”, “Ontopia”, identifying associations

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“Pepper is employed by Ontopia” and identifying occurrences eg a website of Ontopia, a photo of Pepper; The third phase is concerned with Topic Map evaluation and validation (generally done by experts). A TM building approach must include the following functionalities: • • •

• • • • • • •

Defining resources: identifying resource types, adding, deleting, modifying, and merging resources. Identifying and maintaining concepts/topics. Identifying and maintaining relationships/associations between topics: adding and deleting relationship types, member roles (with no constraint on the number of members in a relationship), and relationship instances. Defining different views on a Topic Map including selected topics, relationships, and/or resources. Storing Topic Maps persistently either in standard XTM files or in databases. Merging Topic Maps. Importing/exporting Topic Maps. Including external resources Providing a user interface for search and navigation in the TM Evaluating and validating the resulting Topic Map.

In the following, we will describe some existing Topic Map construction approaches, each approach will be presented according to the functionalities that we have identified above.

4 Topic Maps Building Approaches Some approaches have been proposed to build, manage and maintain Topic Maps. We notice that, very few Topic Maps have to be developed from scratch, since, in most cases; construction approaches are based on existing data sources and use a priori knowledge (such as domain ontologies, thesaurus) to build and populate the Topic Maps. These approaches take as input different data types: structured documents, databases, unstructured documents and semi-structured data. We notice that there are many sources of topic map data, much of this such as XML documents may be leveraged very efficiently through automated processes, some of it can be mapped directly to topic maps such as RDF metadata. We note also that manual enrichment can add considerable value to the TM building process; in fact, most topic map creation approaches are a combination of auto-generation and manual enrichment. Some other approaches propose to use learning techniques and Natural Language Processing (NLP) techniques to extract topics and associations from textual documents. Learning methods can be applied with different automation levels: manual, semi-automatic, automatic. Some research works are dedicated to cooperative TM building. Another research area deals with merging Topic Maps. 4.1 Mapping from RDF to Topic Maps Some research work has been proposed to automatically extract Topic Maps constructs by leveraging existing metadata in RDF [5] format [6], [7]. The approach


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proposed by [7] is a three-step procedure for extracting knowledge from different data sources (relational databases, web sites, enterprise information systems, etc) first in the form of an RDF model and finally as a topic map. The first step consists on recognizing subjects which means find occurrences related to a given subject in the source data. This can be used to extract statements about the subject that can be useful in the knowledge base. The next step is to extract those statements as RDF statements. In fact, an RDF model consists of statements about resources, which are often called triples. A statement has three parts: subject, the resource the statement is about, property, the property being assigned to the subject, and value, the value assigned to the subject. The last step is to map the RDF statements into topics and topic characteristics. In this step, they have to decide whether RDF statements are to be mapped into subject identities, names, occurrences or associations. Finally, since knowledge is extracted from multiple data sources, they propose to create topic maps for the individual data sources and then merge the resulting topic maps. 4.2 Automatic Generation of Topic Maps from XML Documents We can point out some propositions dedicated to automatic generation of TM from XML documents for example, the approach described in [8] propose to use XSLT for Topic Map generation over sets of XML resources. The first stage is hand authoring a relatively invariant ontology Topic Map. This consists of defining the ontology of types and associations that capture the data model for a particular subject domain. The second step is generating additional Topic Maps through an algorithmic process (XSLT) applied to XML document instances. The third is hand authoring those things not captured in the first two stages. This consists of the capture of information not directly discernible from the markup, or stored in non- XML resources. The resultant Topic Maps are merged giving a Topic Map that can be as rich as if completely hand authored. Topic Map merging enables these generated XTMs to be combined with topical information that can't be extracted using a style-sheet. Another approach presented in [9] describes a research prototype of topic maps repository. The main purpose of this repository is to promote knowledge structure sharing and reuse in representing and organizing information resources. Knowledge structure may include existing ontologies, thesauri and existing Topic Maps. As shown in Fig.1, the overall architecture of the repository includes wrappers created to convert disperse knowledge structures into an integrated XML schema used in the repository. For that, they incorporate hierarchical enhancement in the TM structure in order to support hierarchical relationships in thesauri such as BT (Broader Term), NT (Narrower Term), TT (Top Term). Using wrappers, they can convert ISO 2788-based thesauri, RDF-based taxonomies, or XML-based ontologies to the repository. For each new standard, only a new wrapper needs to be defined by introducing more elements in the internal XML schema. In this case, topic maps created through this knowledge repository can be easily export to XTM-based topic maps, and XTM topic maps can also be imported to the repository. The repository is implemented as a relational database (using MY-SQL). Then, they convert data in the database to XML documents using DOM-based servlets. They propose also a web-based authoring tool supported by Java servlets. Through the interface, a user can create topic maps without having to know the syntaxes of

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Fig. 1. Major components of the knowledge repository

topic maps and XML. The user can add or modify topics, occurrences, associations and other related information directly on the web. The approach proposed in [10] presents the Topic Map Builder, a processor that extracts topics and relations from instances of a family of XML documents. The TMBuilder is an XSL stylesheet that receives an XML document as input and generates another XML file that contains a Topic Map. The algorithm of the TM-Builder is a three-step algorithm: (1) Initially, for the given ontology creates all the topics types, occurrences roles, occurrences types and associations types; (2) During a document tree traversal, for each association, define the: association type and association members; (3) For each element in the source that is seen as a topic, create the topic ID, topic type, topic names and topic occurrences. This TM-Builder is strongly dependent on the resources structure. So, to extract a topic map for different collections of information resources, they have to implement several TM-Builders, one for each collection. To overcome this inconvenient, they have created an XML abstraction layer for TM-Builders. This layer is composed by one specification in XSTM (XML Specification for Topic Maps), a new XML language that enables to specify topic maps for a class of XML documents. Roberson and Dicheva in [11] present an approach that aims to support the authors by automatically extracting topic map constructs from web pages and building a “draft” topic map for the authors to start with and later modify. The idea is to extract topic map constructs by crawling a website and parsing its pages. Crawling is a method by which web pages are visited starting from a given web page (specified by its URL) and then traversing the pages through the hypertext links contained in them. They propose also a set of heuristics that can be used for extracting semantic information from the HTML markup of the web pages. They have used this approach to design and implement a plug-in for the topic map editor TM4L that automatically extracts topics and relationships from a website specified by the author. 4.3 Co-construction of Topic Maps with Different Users: Collaborative Approaches The proposed approaches are based on co-construction of Topic Maps with many users. Ahmed in [12] propose a peer to peer application to exchange TM fragments between peers in a distributed environment, each local TM is enriched by adding TM constructs as a result to users requests analysis. At the end, these TM fragments are


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Fig. 2. The TM-Builder architecture

combined with each other using the TM merging process to present a unified TM of the information available from a collection of interacting peers. The approach presented in [13] describe an e-learning environment, called BrainBank Learning, a web application for construction of individual topic maps as a mean for learning, where the learners can construct their own learning ontology during a course or a complete study. It works with standard Internet browsers and users enter the application through individual accounts. Topics that the learner meets during education activities are entered and described. The topics can then be connected by linking phrases to form propositions or associations: The learner creates his own associated network of topics that represents his knowledge. This way of documenting in the learning process is good for the learner’s understanding of the area of study (placing knowledge in a context), as well as navigating and overview of the acquired knowledge later on. To further describe topics and associations, digital resources such as documents, pictures, movie clips and sound clips can be attached to the topics. These resources can be either linked to or uploaded to and stored in BrainBank application. Zaher and Cahier in [14] extend the TM model and propose the Hypertopic model created by Tech-CICO lab and the Agoræ software tool based on this model. It is an all-purpose Knowledge Management approach, that they have called “Socio Semantic Web”, to help communities to formulate, to publish, or to broadcast knowledge, especially scientific knowledge in the field of Human and Social Sciences. The HyperTopic model is a knowledge representation language allowing to build Hypertopic maps using a few basic concepts such as Entity, Point of view, Topic, Association, Resource, Standard attribute. The cooperation model to co-build the map is based the “Knowledge-Based Market Place” (KBM) model with 3 predefined roles (“reader”, “contributor”, “semantic editor”). Through this platform, community members can describe and find domain entities and collections, by designing and browsing “multi-point of view” knowledge maps. Every contributor may declare the characteristics of an entity following an index structure made of several tree diagrams. Thus, the community would build a dynamic and collective meaning. The work described in [15] propose the TM4L environment which enables the creation, maintenance, and use of ontology-aware online learning repositories based on the ISO Topic Maps standard. The proposed framework of ontology-aware discipline-specific repositories is based on building a domain conceptual structure and using it for structuring and classification of learning content. The classification

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involves linking learning objects (content) to the relevant ontology terms (concepts), i.e. using the ontological structure to index the repository content. An assumed and implicit purpose of the conceptual exploration is that some form of learning will occur. By browsing the map, the learner will gain insight into the domain. They have proposed a layered information structure of the learning material repository consisting of three layers, a Semantic layer which contains a conceptual model of the knowledge domain in terms of key concepts and relationships among them, a Resource layer: which contains a collection of diverse information resources associated with the specific knowledge domain and a Context layer to specify different views (contexts) on the repository resources depending on a particular goal, type of users, etc., by dynamically associating components from the other two layers. 4.4 Topic Maps Learning The approach defined in [16] intends to extract knowledge from web sites to help users find relevant information on the Web. The construction process starts by defining the profile of a TM (and later applying it to Web sites). These profiles characterize Topic Maps and help evaluate their relevance to users' information needs. Second, the analysis identifies topics that have no interest, semantically speaking, which allows to "clean" the TM. That Topic Maps - or Web sites - characterization, filtering and clustering are deduced from the results of a conceptual classification algorithm based on Formal Concept Analysis and Galois connections. TM characterrization is based on calculating statistics for every object of the topic map. They propose to compute a weighted mean of these statistics. Each object has a weight which is assigned according to its importance in the topic map. The clustering algorithm consist on grouping topics which share common properties into clusters in order to provide different level of detail (or scales) of the topic map. Fig. 3 shows the TM analysis algorithm:

Fig. 3. TM Analysis Algorithm


109

The approach described in [17] has focused on Inductive Natural Language Processing techniques to construct a Navigable Topic Map adapted to different users' viewpoints from free textual documents. NLP techniques are used to identify semantic classes on the basis of word associations patterns and to construct semantic dimensions which define the conceptual space of a user viewpoint. These classes constitute potential topics in the resulting topic map. The associations among the topics are constrained to a certain conceptual space or scope. They propose to model scopes [18] as conceptual spaces based on a geometric structure. On the one hand, it provides a way to embed topics in an abstract n-dimensional description space where spatial relations between topics have a semantic interpretation. On the other hand, the topological structure of the space provides geometric constraints for comparing conceptual spaces and thus assessing the semantic convergences and divergences across different viewpoints. This approach is useful for identifying semantic patterns in disparate resources and organizing them in a topic map framework for such tasks as domain monitoring, technology watch, opinion tracking, etc. The approach presented in [19] consists in developing a software framework to semi automatically generate a Topic Map from ten years of a conference proceedings (a set of text files) using machine learning techniques. The result is presented on a structured information portal and content management system with the references to the occurrences of the instances in the source text. This source text, available in various formats and structures, is processed to have uniform structure. The second phase consists in identifying the main topics and their associations present in the source text. Two basic identification methods are applied. One is the identification of the topics and associations from the structure of the source text. For example topics like the paper title, the author, the affiliation of the author can be identified by pointing to the appropriate item in the structured metadata. The other identification method is based on the analysis of existing external taxonomy or ontology to assign keywords to papers. The associations between keywords are defined by the external ontology. The result of this phase is a Topic Map skeleton which is a combination of the external ontology and the ontology defined by the source text structure. The final phase of the framework is the content management phase. In this phase the completed Topic Map is loaded into an informational portal where the Topic Map can be presented to the user in a user friendly way using a content management system. 4.5 Merging Topic Maps Merging has been defined in the XML Topic Maps specification. A topic map application may use multiple topic maps. Each topic map may emanate from a different source, generated by a different technique or written in a different syntax. Merging takes places on the basis of subject identity or topic names. When two topic maps are merged, topics that represent the same subject should be merged to a single topic and the resulting topic has the union of the characteristics of the two original topics. As we mentioned above, there are other approaches such as [9], [10], [12] that use the merging technique. Another approach described in [20] is essentially based on merging technique. It consists on semantic integration of Web-based learning resources using the Topic Maps standard. It is a three steps approach including learning objects representation,

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enriching learning object with semantics and semantic integration of distributed learning resources. In fact, these learning resources belong to different repositories. Learning resources of a learning repository are represented in Topic Maps knowledge base. Semantics of a learning resource is incorporated in the knowledge base as links to concepts of DAML + OIL ontology. This is made for each learning repository leading to one topic map for each repository. So, semantic integration consists to merge these topic maps into a single one. Integration consists to merge all these topic maps into a single and coherent one. Topic Maps is an adequate formalism for integration. That is, everything is topic in Topic Maps. Even associations are reified by topics. Thus, integration is simply made by merging topics referencing same concept of the common ontology. In the next table, we show a summary of the approaches described in this paper. Table 1. Summary of Topic Maps building approaches

0

* &345

Automatic generation of TM via RDF statements

' &365

TM Authoring with reusable ontologies and automated knowledge mining

, 7 385

Design knowledge repository to integrate and share existing knowledge structures TMBuilder: Automatic extraction of TM constructs from XML documents Semi-automatic ontology extraction to create draft TM

-Mapping RDF to TM -TM Merging -XSLT -Manual enrichment with domain expert -TM merging TM enhancement with hierarchical structure

, 395 ' ( " 35

&3 5

,"1 3!5 : 3%5 ( " ( "3/5

TMShare: Designing a peerto-peer information sharing application based on TM standard BrainBank Learning :A web application to build personal TM as a strategy for learning HyperTopic Model: Co-build a TM with different actors TM4L: Environment for creating and browsing educational Topic Maps

, 3;5

Navigation and information retrieval on the Web

) 3 95

' 1 No

2"

Relational Databases

Information not available in papers TM4J

Information not available in papers -Domain expert -User

Domain ontology

XML resources

No

Thesauri Ontologies Topic Maps

XML stylesheets for displaying TM

User (future work)

A new automatic TM extractor from XML documents Crawling web sites using a set of heuristics -Collaborative build -Enrichment with users queries -TM merging -Collaborative build -Learning methods

No

XML documents

User

Ontology

Web pages

Shared vocabularies

Local Topic Maps

TM Extractor developed by the authors TM4J TM4L WebSphinx TM4J Framework JXTA

No

Web resources

Ontopia Knowledge Suite

User

-Cooperative build

No

Web pages

Agorae tool

-Classification (using the ontological structure to index the repository content) -Collaborative build -Clustering techniques -Statistical methods NLP techniques

Domain ontology

Learning repositories

TM4L

-User -Expert -User -Expert -Tests

Ontology

Web sites

No

Domain texts

Domain ontology

-Texts -Structured metadata Learning repositories

3D visualisation tool developed by the authors -Zellig -ALCESTE text mining tools Tapestry web framework

-Machine learning -Mapping from metatada to TM -TM Enrichment -TM Merging

Ontology

Information not available in papers

-User -Tests User

User

User

Information not available in papers User

5 Discussion and Conclusion This paper presents an overview of the most relevant Topic Maps construction approaches taken as input different sources like text, structured data, document metadata etc. Based on the state of the art, we notice that there are many techniques


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used to generate Topic Maps from different sources. Some of these approaches take as input XML documents and propose to apply automated processes to leverage these documents; other approaches propose to map directly RDF metadata to topic maps. We note also that collaborative construction involving different users are very appropriate to the TM building process. In addition, we notice that most topic map construction approaches are a combination of auto-generation enrichment techniques and merging process. In all proposed approaches for TM construction, we notice that automated Topic Map construction is not enough developed. Indeed, the construction of a TM can be very costly and can quickly become a bottleneck in any large-scale application if recourse is not made to automatic methods. Problems of maintenance and coherence may arise when the TM is applied to heterogeneous and multilingual information sources, as manual construction and maintenance can not keep pace with any incoming documents. Also, the main lacks for all the approaches presented in this paper are that they are dedicated to a specific domain and there are not approaches or techniques for building Topic Maps from multilingual resources. In fact, the multilingual aspect is not landed in all existing approaches which support a monolingual environment. The problem is that the large majority of resources available today are written in various languages, and these resources are relevant even to non-native speakers. In this case, it is difficult for users to find relevant information in documents using their native language. At least, we note that the majority of the TM construction approaches do not propose techniques for the evaluation of the resulting Topic Map.

References 1. ISO/IEC :13250. Topic Maps: Information technology-document description and markup languages (2000), http://www.y12.doe.gov/sgml/sc34/document/0129.pdf 2. The Topic Map website, http://www.topicmap.com 3. TopicMaps.Org XTM Authoring Group, XML Topic Maps (XTM) 1.0 (March 3, 2001), http://www.TopicMaps.org, http://www.topicmaps.org/xtm/1.0/ 4. Pepper, S.: Methods for the Automatic Construction of Topic Maps (2002), http://www.ontopia.net/topicmaps/materials/autogen-pres.pdf 5. Ora, L., Swick, R.: Resource Description Framework (RDF) Model and Syntax Specification, W3C Recommendation (1999), http://www.w3.org/TR/REC-rdf-syntax/ 6. Pepper, S.: Topic Map Erotica RDF and Topic Maps in flagrante (2002), http:// www.ontopia.net/topicmaps/materials/MapMaker_files/frame.htm 7. Gronmo, G.O.: Automagic Topic Maps, (2002), http://www.ontopia.net/topicmaps/materials/automagic.html 8. Reynolds, J., Kimber, W.E.: Topic Map Authoring With Reusable Ontologies and Automated Knowledge Mining. In: XML 2002 Proceedings by deepX (2002) 9. Lin, X., Qin, J.: Building a Topic Map Repository (2002), http://www.knowledgetechnologies.net/proceedings/presentations/lin/xialin.pdf 10. Librelotto, G.R., Ramalho, J.C., Henriques, P.R.: TM-Builder: An Ontology Builder based on XML Topic Maps. Clei electronic journal 7(2), paper 4 (2004)

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11. Roberson, S., Dicheva, D.: Semi-Automatic Ontology Extraction to Create Draft Topic Maps. In: 45th ACM Southeast Conference, Winston-Salem, NC, March 2007, pp. 23–24 (2007) 12. Ahmed, K.: TMShare – Topic Map Fragment Exchange in a Peer-To-Peer Application. (2003), http://www.idealliance.org/papers/dx_xmle03/papers/0203-03/02-03-03.pdf 13. Lavik, S., Nordeng, T.W., Meloy, J.R.: BrainBank Learning - building personal topic maps as a strategy for learning. IN XML, Washington (2004) 14. Zaher, L.H., Cahier, J.-P., Zacklad, M.: The Agoræ / Hypertopic approach. In: International Workshop IKHS - Indexing and Knowledge in Human Sciences, SdC, Nantes (2006) 15. Dicheva, D., Dichev, C.: TM4L: Creating and Browsing Educational Topic Maps. British Journal of Educational Technology - BJET 37(3), 391–404 (2006) 16. LeGrand, B., Soto, M.: Topic Maps et navigation intelligente sur le Web Sémantique, AS CNRS Web Sémantique, CNRS Ivry-sur-Seine - (October 2002) 17. Folch, H., Habert, B.: Articulating conceptual spaces using the Topic Map standard. In: Proceedings XML 2002, Baltimore, December 2002, pp. 8–13 (2002) 18. Pepper, S., Gronmo, G.O.: Towards a General Theory of Scope (2002), http://www.ontopia.net/topicmaps/materials/scope.htm 19. Kasler, L., Venczel, Z., Varga, L.Z.: Framework for Semi Automatically Generating Topic Maps. TIR-06. In: Proceedings of the 3rd international workshop on text-based information retrieval, Riva del Grada, pp. 24–30 (2006) 20. Ouziri, M.: Semantic integration of Web-based learning resources: A Topic Maps-based approach. In: Proceedings of the Sixth International Conference on Advanced Learning Technologies (ICALT 2006), 0-7695-2632-2/06 $20.00 ©, IEEE, Los Alamitos (2006)

Construction of Consistent Models in Model-Driven Software Development Gabriele Taentzer Philipps-Universit¨ at Marburg, Germany [email protected]

Abstract. Model-driven software development is considered as a promising paradigm in software engineering. Models are ideal means for abstraction and can enable developers to master the increasing complexity of software systems. However, it is not easy to construct consistent models. Inconsistent models are usually the source for erroneous code which cannot be compiled or, if compiled, lead to malfunctioning applications. Developers have little help in producing consistent models, i.e. they are often not well informed by adequate error messages. Starting with a consistent initial model, we follow the idea to identify designated model development steps between consistent models only. These development steps are defined as model transformations. Recurring modeling patterns are identified and formalized as transformation rules. As essential contribution, a construction approach for consistent models in model-driven development is deduced and specified on the basis of graph transformation concepts. Using this approach, developers can be guided in the modeling process such that consistent models are developed only. Keywords: model-driven software development, UML, Eclipse, model transformation, graph transformation.

1

Introduction

Software development is a creative process, like writing or painting. A number of software development methodologies such as the Unified Process [14] and extreme programming [9], exist which guide the developers through the development process and identify tasks where creativity is needed. Model-driven software development (MDSD) [19] is an approach where models are the central artifacts in software development and drive the code generation. Thus, creativity is mainly focussed on the modeling phases in the development process, while the implementation is performed automatically in large parts, once a code generation infrastructure has been built up. As a consequence, MDSD can increase the systematic proceeding in software development, due to a higher abstraction level. (However, some implementation tasks usually still remain, since code generation is seldom hundred-percent.) The conclusion that high-quality software can only be generated from highquality models has led to an ongoing discussion on model quality and the question how to achieve high-quality models. An evolving line of research is model R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 113–124, 2008. c Springer-Verlag Berlin Heidelberg 2008

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refactoring, but still in its infancy. Having defined model refactoring by lifting refactoring concepts from the code to the model level, the relation between model refactoring and its effect on the model quality remains a largely unanswered question. Also, the types of models for which refactoring is supported are rather few. (See [17] for a survey on software refactoring.) An important aspect of refactoring in the context of MDSD is how model refactoring interferes with code generation. Similarly to code refactoring, model refactoring is intended to improve model structures while the model behavior has to be preserved. Since models are used for code generation, direct lifting of code refactorings to the model level may lead to model refactorings which do not preserve model behavior. For example, the renaming of model elements class diagrams might lead to different text labels in generated data management Web pages. (The question which model modifications should be considered as refactorings, is discussed in [16].) In current MDSD-approaches, developers get little help in producing consistent models. Although code generation usually starts with model validation, not all errors are found in this first validation phase. Therefore, the subsequent compiling phase might report further errors which are difficult to understand, since they are concerned with the generated code. Even worse, erroneous models may also cause runtime errors which are even more difficult to find, especially since model debugging and testing are still in their infancy. To support the construction of consistent models only, we identify development steps which lead to meaningful models only. The main idea is to extract modeling patterns which represent modeling knowledge of experienced developers. If needed, several model elements are inserted or changed, resp., to keep the consistency of several model parts during their modification. For example, creating a new use case includes the creation of a new activity diagram as refinement. Or if e.g. a method call shall be inserted as action, the corresponding method, if not existing, has to be created. Our construction of consistent models in model-driven software development is structured in development steps which specify the least model modifications to reach a consistent model again. The approach is illustrated by a small development scenario which consists of code generation from UML models by AndroMDA [6]. The specification of development patterns is based on the Eclipse Modeling Framework (EMF) [2] and the transformation of EMF models [11]. To summarize, an approach to systematic model-driven development by patterns is deduced and specified on the basis of EMF model transformations. The chosen model transformation approach can be formalized by algebraic graph transformation [12].

2

A Development Scenario in Model-Driven Software Development

Let us consider a development scenario using AndroMDA as one of the state-ofthe-art tool for model-driven software development. AndroMDA has a generic

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code generation engine which can be adapted to certain domains by so-called cartridges. Pre-existing cartridges enable the generation of web applications based on software libraries such as Spring [5], Hibernate [8], and J2EE [4]. We use AndroMDA to generate a simple university calendar as web application from a UML model. The cartridges are not only responsible for code generation, but also define a UML profile where special stereotypes and tagged values drive the code generation. In the following, we consider some elements of the AndroMDA UML profile to develop our scenario. For a detailed introduction to AndroMDA, the reader is referred to [6]. The overall MDSD-development approach is use case-driven. Starting from a use case description of a software system, each use case is specified by an activity diagram which is successively refined. The associated class model defines the underlying domain model. Modifying one part of a model requires that all other parts are kept consistent. Otherwise, it might happen that the consistency checker within the generator reports errors. That is the point where modeling patterns might help. They represent the knowledge of experienced developers about model development which keeps model parts consistent. We describe modeling patterns by model transformation rules.

Fig. 1. A concrete modeling step

Imagine a simple domain model specifying lectures, rooms and persons who act as lecturers which serves as a basis to realize use cases such as ”search lecture”, ”modify lecture”, and ”insert new room”. As concrete modeling scenario, we consider the model modifications to realize use case ”search lecture”. The class model has to incorporate new service and controller classes and the use case has to be refined by an activity diagram describing the corresponding

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web page. One modification step of corresponding class and activity diagrams is shown in Figure 1. The upper class and activity diagrams are developed to the lower diagrams in the figure. I.e. both model parts have to be changed simultaneously to keep the whole model consistent. The class diagram shows a section of a service-oriented class model. A controller class has to exist which can rely on services, here offered by class “LectureService”. The use case is refined by an activity diagram which searches for all lectures first and then, shows them to the users. Thereafter, they can filter this list by search criteria. To really show a filtered set of lectures, controller method “showLectures()” is needed which is inserted in the modeling step considered. This method can only be used here, if it is also added to the corresponding controller class. Thus, the resulting model is consistent only, if not only the activity diagram is extended but also the class diagram is adapted accordingly. To realize the full use case, the activity diagram has to be extended by signals modeling the search form and the presentation of search results. Moreover, methods “showLectures()” and “findLecture()” have to be implemented by hand.

3

Modeling Patterns

Considering concrete scenarios in model-driven development, different kinds of development activities can be recorded. Besides true development steps such as model refinements or extensions, other kinds such as refactorings and other optimizations as well as documentation are also needed. We consider a development step as micro step, if a model is modified and its consistency is kept. A modeling pattern identifies experiential knowledge which we describe first informally by choosing a pattern name, defining its input parameters, and providing an informal description. In the next section, we will specify modeling patterns as EMF model transformation rules. Consequently, micro steps will be specified as EMF model transformations. As an example for a modeling pattern, we deduce the knowledge from the small development scenario presented in the previous section. Starting with the development of a new use case, patterns like AddUseCase, AddActivity, and AddServiceClass are needed. AddMethodCallAsAction(activityName: String, methodName: String) is a pattern for inserting the call of a controller method as action in an activity. It leads to a consistent model only, if this method is already available in the corresponding controller class. Otherwise, pattern AddNewMethodCallAsAction(activityName: String, methodName: String) which additionally inserts a new method (without parameters) in the controller class, has to be applied. Its return type should always be void. This second pattern is the one which can be identified from the development step depicted in Fig. 1. Although service and controller methods have to be hand-coded, the pattern idea can be extended also to code. For example, the implementation of a controller method can include the realization of a corresponding web form. The controller code which would be inserted by a pattern, can deal with the realization of drop down menus for form entries and with the display of presentation data.


4

117

The General Approach

After having discussed concrete scenarios for model-driven development in Sec. 2 and after having introduced the concept of modeling patterns in Sec. 3, we now consider the construction approach for consistent models in general. Besides the example application of this approach presented in Sec. 2 based on UML and AndroMDA, there are further model-driven development scenarios such as the generation of visual editors by the Eclipse Graphical Modeling Framework (GMF) [1]. In the following, we first consider the relation between model and code and clarify their syntax representation by abstract syntax graphs. Thereafter, we structure model development in atomic steps, called micro steps, and consider the specification of these steps by patterns. Since a model in MDSD is the source for code generation, model development should not destroy the consistency of models, i.e. the code generated from a modified model, should be executable and meaningful. Only those development steps which fulfill these requirements, are called micro steps. Finally, we sketch how development steps can be specified as graph transformations. 4.1

Model and Code

The central part of a model-driven development is of course the model. Although often UML or derivates of UML are used as modeling languages for software development, other kinds of modeling languages are also possible. In the context of e.g. Eclipse GMF a combination of several EMF models and their mapping are considered as model. In the concrete MDSD scenario in Sec. 2, a special UML profile for AndroMDA is used which is needed for code generation. However, the code cannot be generated completely, but in addition, service and controller methods have to be written by hand. Thus, hand-written code has to be taken into account in development steps as well and has to be kept consistent with the UML model. From this point of view, we decide to consider these code fragments as model parts as well. Thus, we allow that a model can consist of several diagrams and of code fragments which means that hand-written code fragments are considered as model parts, too, according to the credo by Bezivin [10]: “Everything is a model.” 4.2

Syntax Representation

Although diagrams and code differ heavily in their concrete representation, their abstract syntax representations show a lot of similarities. Considering e.g. UML diagrams as instances of MOF models, they consist of typed model elements and their interrelations forming a graph-like structure. Code is usually represented by abstract syntax structures which form trees or again graph-like structures. Therefore, we consider graphs as abstract syntax structures for the kind of models we discuss for MDSD.

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Having two different graph representations for the diagram part of a model and for the code available, the question is how they are interrelated. Looking at the concrete AndroMDA model in Sec. 2, the interrelation between different diagrams is realized by common model elements such as classes, variables, etc. The interrelation between diagrams and code, however, is performed by names. Methods, parameters, classes, etc. are defined in the UML model and used in service and controller method bodies. The abstract syntax representation of an UML model can be given by an EMF instance model. Considering the upper two diagrams in Fig. 1, we see a section of the class diagram and the activity diagram for use case “Search Lecture”. A part of the complete example model is depicted as EMF instance model in Fig. 2. The corresponding model element types are shown in angle brackets such as . Thereafter, some of the model element attributes are depicted, mostly just names. (Others can be considered by a special property view.) Please note that class “LectureController” is highlighted and will be extended in the following example of a development step.

Fig. 2. Part of the UML model in Fig. 1 as EMF model

Although depicted as tree, an EMF (instance) model can be considered as graph where model elements represent graph nodes and links are considered as graph edges. 4.3

Micro Development

To discuss model developments in the MDSD context, we first have to clarify the notion of a consistent model: A model is considered to be consistent, if it is empty


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or derived by the application of modeling patterns. That means we do not use some kind of consistency checker but develop models by performing well-defined steps. Consider for example the development step in Fig. 1 which adds a method call as action to an activity. If the method to be called has not been defined, this modification has to be extended by adding a new controller method. Both model transformations together form a micro development step, but the first one alone does not, if the corresponding controller method does not exist. We specify micro development steps by modeling patterns as described in Sec. 3. Each modeling pattern has a name and a list of parameters needed to perform the corresponding development step. For example, consider again modeling pattern AddNewMethodCallAsAction(activityName: String, methodName: String) which is described in Sec. 3. Since we consider development steps as model transformations, modeling patterns are specified further by model transformation rules. Thus, pattern-based model-driven development is defined by rule-based model transformations. For models which are available as EMF models, development steps can be performed as rule-based EMF transformations. A framework for in-place EMF model transformations has been presented in [11]. Besides a compiler which generates transformation code in Java, this framework also contains an interpreter which translates an EMF transformation to a graph transformation performed by the graph transformation engine AGG [3]. In that way, we showed how EMF transformations can be considered as algebraic graph transformations. In the following, we discuss EMF model transformation “AddMethodCallAsAction”. Here, we consider the case that this method has to be newly created and added to the corresponding controller class. As described above, a modeling pattern is needed which is specified by the EMF model transformation rule in Fig. 3. This rule consists of four parts: a left-hand side (LHS) which defines a model pattern to be found as pre-condition. In our case, we have to find a CallOperationAction which refines a use case with an assigned controller class. In the right-hand side (RHS), the post-condition is formulated. Instances equally numbered indicate identical instances within this rule. In our example RHS, a new operation is assigned to the controller class and called in the action. Two further pre-conditions, so-called negative application conditions (NACs), have to be fulfilled: No other operation should be called and an operation named by methodName, is not yet included in the controller class. Both NACs are formulated by patterns which must not be found in the current model. They are depicted in the top row. Applying this transformation rule to the model part in Fig. 2, more exactly to the activity with name “search lectures” and setting parameter methodName to “showLectures”, would lead to a model with class “LectureController” containing a new operation “showLectures()” as depicted in Fig. 4. Furthermore, this new operation is called in activity “search lectures” which is not shown in the overall presentation of the EMF instance model. The model development above contains diagram modifications only. Others such as PopulateDropDownMenu discussed at the end of Sec. 3, modify

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Fig. 3. EMF model transformation rule for adding a method call as action

Fig. 4. Part of the UML EMF model in Fig. 2 after adding an operation

adjacent code parts. The corresponding development step has to be formulated as transformation of the corresponding abstract syntax tree (AST). Analogously, modeling patterns have to be specified by AST rewrites. 4.4

Macro Development

To implement a use case of some complexity, quite a number of micro steps are probably needed to be performed. Furthermore, it should be clear that a lot of different modeling patterns are needed to formulate the large variety of model developments as model transformation sequences. Hence starting with the development of a new use case, the developer should get some guidance which modeling patterns can and should be used. Since modeling patterns are specified by model transformation rules, each pattern can be checked for applicability at a certain model. In this way, we can find out those patterns which are usable at a certain development stage. However, there are certainly still too many applicable patterns such that the developer should be guided even further. Looking at existing software methodologies, a sequence of tasks can be identified in each iteration phase. Mapping modeling patterns to certain tasks would increase guidance.


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In general, a macro development step can be considered as a sequence of micro development steps such that the whole functionality of a use case is realized. Although model validation is usually a part of code generation, it is often not comprehensive enough. That means valid models can still lead to erroneous software code. All models developed by pattern-based development steps only, should always lead to valid models. Moreover, they should lead to syntactically correct code. Ideally, the set of all correct software system modeled without patterns can also be modeled with patterns. This fact would mean that the set of modeling patterns is comprehensive enough. In that case, we would call the set of modeling patterns complete wrt. to the modeling language. 4.5

Alternative Format for Model Development Steps

Besides Eclipse EMF, models can also be given in other formats: For example, UML models are usually stored in the XML format XMI [23]. Thus, development steps in this context would be XML transformations. Different transformation languages are around for XML such as XSL style sheets [24]. In [22], we have presented a graph transformation-based approach to XML transformations. XML structures are translated to graphs which are transformed in a rule-based manner and then, translated back to XML.

5

Related Work

Model refactoring is a specific kind of model development that allows us to improve the structure of models while preserving their semantics. Sunyè et al. [21] were the first who applied the idea of refactoring at the level of UML models. Others, e.g. Porres in [18] followed and considered not only class diagrams but also behavior diagrams. Moreover, Porres presents an approach where model refactorings are specified by a rule-based language, similarly to the pattern-based approach introduced in this work. Refactoring in the context of model-driven software engineering raises new challenges that need to be addressed. Model refactoring may also affect and require changes to the hand-written source code, in order to keep them synchronized with the generated code. This may require the need to perform code-level refactorings. In contrast to its original definition, model refactorings may change external qualities as perceived by the user, such as usability aspects. A comprehensive introduction into this subject is given in [16]. In this paper, we consider development steps in general where refactoring steps form a special kind of. The improvement of model quality is also the focus of St¨ urmer et.al. in [20] where MATLAB Simulink/Stateflow models are improved by the application of graph transformation using Fujaba and MOFLON. Although specifying various model improvements, this approach is not used for other kinds of model development such as model extensions. Batory has the vision of architectural meta programming [7] where programs are values and program development is performed by functions mapping programs to programs. He considers program refactoring, program synthesis, and

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model-driven development from this perspective. While functions only add structure, modeling patterns can perform any kind of change. Thus, our approach mirrors the usual process of software development which is not always as straight forward as desirable, in a better way.

6

Conclusion

This article presents a construction concept for consistent models in modeldriven software development based on modeling patterns. A complex development scenario is structured into steps between consistent models only. Each step is specified by a modeling pattern which specifies the intended model modifications. The approach has been illustrated by a small development scenario which consists of code generation from UML models by AndroMDA. Identified modeling patterns are formulated as EMF model transformations. Due to space limitations, only one example for a modeling pattern has been presented. However, further modeling pattern have been identified. To apply this model construction concept in a larger context, a catalog of modeling patterns is needed, similarly to catalogs of design patterns and refactorings. Note that such a pattern catalog should also contain patterns which take back model extensions, since development is not always as straight forward as desirable. Having a mature code generator for the considered modeling languages at hand, it is furthermore interesting to show that a pattern set is consistent and complete wrt. the modeling language. Of course, each MDD infrastructure with its modeling language and code generator needs its catalog of modeling patterns. Besides model-driven software development based on UML and AndroMDA, the approach could be applied also to further scenarios such as the development of EMF models to generate structured data models as well as Eclipse GMF models to generate graphical editors. Often model-driven development does not only mean model development, but also includes coding to a certain percentage. This situation does not allow a conception as clear as desirable. In the approach presented, these code parts conceptually belong to the model which means that development steps also comprise direct code modifications. The idea is to provide two graph parts for diagrams and code within an abstract syntax graph. These parts are not directly interconnected, but refer to each other by equal names. This solution is simple, but not always as flexible as needed. A further possibility is the use of triple graphs [15] consisting of a diagram and a code graph with a third graph in between specifying interconnections between these two ones. Alternatively, distributed graphs could be used to specify abstract syntax graphs of different view points as in [13]. This issue shows that our approach based on graph transformation concepts is full of potentials to consider fundamental questions in model-driven development in a new light.


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References 1. Eclipse Graphical Modeling Framework (2007), http://www.eclipse.org/gmf 2. Eclipse Modeling Framework (2007), http://www.eclipse.org/emf 3. Graph Transformation Environment AGG (2007), http://tfs.cs.tu-berlin.de/agg/ 4. Java platform, enterprise edition (2007), http://java.sun.com/javaee 5. Spring framework (2007), http://www.springframework.org 6. AndroMDA (2007), http://www.andromda.org 7. Batory, D.S.: Program Refactoring, Program Synthesis, and Model-Driven Development. In: Krishnamurthi, S., Odersky, M. (eds.) CC 2007. LNCS, vol. 4420, pp. 156–171. Springer, Heidelberg (2007) 8. Bauer, C., King, G.: Java Persistence with Hibernate. In: Manning Publications (2006) 9. Beck, K.: Extreme Programming Explained: Embrace Change. Addison-Wesley, Reading (2000) 10. Bezivin, J.: In search of a basic principle for model driven engineering. In: UPGRADE, Novatica, vol. 2 (2004) (special issue on UML and Model Engineering) 11. Weiss, E.: Graphical Definition of In-Place Transformations in the Eclipse Modeling Framework. In: Nierstrasz, O., Whittle, J., Harel, D., Reggio, G. (eds.) MoDELS 2006. LNCS, vol. 4199, pp. 425–439. Springer, Heidelberg (2006) 12. Ehrig, H., Ehrig, K., Prange, U., Taentzer, G.: Fundamentals of Algebraic Graph Transformation. In: EATCS Monographs in TCS, Springer, Heidelberg (2006) 13. Goedicke, M., Enders, B., Meyer, T., Taentzer, G.: ViewPoint-Oriented Software Development: Tool Support for Integrating Multiple Perspectives by Distributed Graph Transformation. In: Schwartzbach, M.I., Graf, S. (eds.) ETAPS 2000 and TACAS 2000. LNCS, vol. 1785, pp. 43–47. Springer, Heidelberg (2000) 14. Jacobson, I., Booch, G., Rumbaugh, J.: The Unified Software Development Process. Addison-Wesley, Reading (1999) 15. K¨ onig, A., Sch¨ urr, A.: Tool Integration with Triple Graph Grammars - A Survey. In: Heckel, R. (ed.) Proceedings of the SegraVis School on Foundations of Visual Modelling Techniques. Electronic Notes in Theoretical Computer Science, vol. 148, pp. 113–150. Elsevier Science Publ. Amsterdam (2006) 16. Mens, T., Taentzer, G., M¨ uller, D.: Model-driven software refactoring. In: Rech, J., Bunse, C. (eds.) Model-Driven Software Development: Integrating Quality Assurance, Idea Group Inc. (to appear, 2008) 17. Mens, T., Tourwé, T.: A survey of software refactoring. IEEE Transactions on Software Engineering 30(2), 126–139 (2004), http://w3.umh.ac.be/∼ infofs/preprints/index.php?page=paper info&ID=27 18. Porres, I.: Model Refactorings as Rule-Based Update Transformations. In: Stevens, P., Whittle, J., Booch, G. (eds.) UML 2003. LNCS, vol. 2863, pp. 159–174. Springer, Heidelberg (2003) 19. Stahl, T., V¨ olter, M.: Model-Driven Software Development. Wiley, Chichester (2006) 20. St¨ urmer, I., D¨ orr, H., Giese, H., Kelter, U., Sch¨ urr, A., Z¨ undorf, A.: Das MATE Projekt - visuelle Spezifikation von MATLAB Simulink/Stateflow Analysen und Transformationen. In: Conrad, M., Giese, H., Rumpe, B., Sch¨ atz, B. (eds.) Tagungsband des Dagstuhl-Workshops: Modellbasierte Entwicklung eingebetteter Systeme, vol. 2007-01, Informatik-Bericht der TU Braunschweig (2007)

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21. Sunye, G., Pollet, D., Le Traon, Y., Jezequel, J.M.: Refactoring UML models. The Unified Modeling Language, pp. 134–148 (2001), www.irisa.fr/triskell/publis/2001/Sunye01b.pdf 22. Taentzer, G., Toffetti Carughi, G.: A Graph-Based Approach to Transform XML Documents. In: Baresi, L., Heckel, R. (eds.) FASE 2006 and ETAPS 2006. LNCS, vol. 3922, pp. 48–62. Springer, Heidelberg (2006) 23. MOF 2.0 / XMI Mapping Specification (2007), http://www.omg.org/technology/ documents/formal/xmi.htm 24. XSL Transformations (XSLT) Version 2.0 (2007), http://www.w3.org/TR/xslt20/

Author Index

Algergawy, Alsayed 19 Altinel, Mehmet 12 Ben Ahmed, Mohamed B¨ ohm, Matthias 31 Buchan, Jim 43 Dustdar, Schahram Ellouze, Nebrasse

Patig, Susanne

55 102

Habich, Dirk 31 Hasselbring, Wilhelm Holder, Stefan 43 Kashevnik, A.

102

91

Lamparter, Steffen 8 Lehner, Wolfgang 31 Levashova, T. 91

MacDonell, Stephen G. Malek, Miroslaw 1 Markl, Volker 12 Métais, Elisabeth 102

79

67

Saake, Gunter 19 Schallehn, Eike 19 Shilov, N. 91 Simmen, David 12 Singh, Ashutosh 12 Smirnov, A. 91 Streekmann, Niels 79 Taentzer, Gabriele Tai, Stefan 8 Tran, Huy 55 Wloka, Uwe Zdun, Uwe

31 55

113

43

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