![R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 1–7, 2008.
© Springer-Verlag Berlin Heidelberg 2008
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 evalua-
tion 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 func-
tional 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](https://image.slidesharecdn.com/68553-250410163712-59efea76/85/Model-Based-Software-and-Data-Integration-Communications-in-Computer-and-Information-Science-8-1st-Edition-Ralf-Detlef-Kutsche-16-320.jpg)
![2 M. Malek
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]. As-
tronomy 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 re-
sulted 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 as-
tounding. 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 sto-
chastic 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 im-
plementation (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 struc-
tured 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.](https://image.slidesharecdn.com/68553-250410163712-59efea76/85/Model-Based-Software-and-Data-Integration-Communications-in-Computer-and-Information-Science-8-1st-Edition-Ralf-Detlef-Kutsche-17-320.jpg)
![The Art of Creating Models and Models Integration 3
In the eighties the dominant development was pattern design [4] that promoted the re-
use of certain solutions in software development process and with objects [5] and com-
ponents [6] the software reuse became a reality. Also, representation of finite state auto-
mata, 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 syn-
thetize 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 - Com-
puter-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 ser-
vice
• 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 complemen-
tary 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 activi-
ties, 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 op-
posite to the goals of a real system. It is highly desirable to be able to measure the dis-
tance 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 real-
ity (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](https://image.slidesharecdn.com/68553-250410163712-59efea76/85/Model-Based-Software-and-Data-Integration-Communications-in-Computer-and-Information-Science-8-1st-Edition-Ralf-Detlef-Kutsche-18-320.jpg)
![4 M. Malek
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. Creation of artifacts, systems that do not exist in nature, so it is difficult to as-
sess quantitatively their quality even when they are created as there is no refer-
ence point. The only way out are comparative analysis (e.g., who has developed
bigger or faster engine, algorithm, etc.).
2. 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).
3. 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.
4. Demand for an ever-growing number of features (e.g., scalability, adaptivity or
a real-time behavior).
5. 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).
6. Dynamicity of systems caused by varying configurations, patches updates, up-
grades/downgrades requires highly flexible and dynamic models.
7. Composability and integration (e.g., ability to combine various service models
into a business process; models integration of structure, behavior and both func-
tional and non-functional properties; integration of software, hardware, interop-
erability/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 mak-
ing 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.](https://image.slidesharecdn.com/68553-250410163712-59efea76/85/Model-Based-Software-and-Data-Integration-Communications-in-Computer-and-Information-Science-8-1st-Edition-Ralf-Detlef-Kutsche-19-320.jpg)
![The Art of Creating Models and Models Integration 5
This best practice guide [8] is based on the experience we have gained when inves-
tigating these topics:
a. complexity reduction, showing that selecting the most predictive subset of vari-
ables contributes more to model quality than selecting a particular linear or non-
linear modeling technique
b. 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,
c. data-based empirical modeling of complex software systems, cross benchmark-
ing 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.
d) Model Application
c) Model Estimation
b) Variable Selection /
Complexity Reduction
forward selection
backward elimination
probabilistic w rapper
ARMA / AR
multivariate linear
unsiversal basis
functions (UBF)
radial basis functions
(RBF)
support vector
machines (SVM)
sensitivity analysis
forecasting
a) System Observation
time series (numerical)
log files (categorical)
e) Reaction / Actuation
offline system
adaptation
online reaction schemes
f) closing the control loop
system experts
...
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 forecast-
ing 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].](https://image.slidesharecdn.com/68553-250410163712-59efea76/85/Model-Based-Software-and-Data-Integration-Communications-in-Computer-and-Information-Science-8-1st-Edition-Ralf-Detlef-Kutsche-20-320.jpg)
![6 M. Malek
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 Func-
tion (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 de-
sign 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 un-
derstanding 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 tam-
ing 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 creat-
ing 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 Lan-
guages in Mathematical Optimization, Kluwer, Boston (2006)
3. Nassi, I., Shneiderman, B.: Flowchart Techniques for Structured Programming. In: SIG-
PLAN 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)](https://image.slidesharecdn.com/68553-250410163712-59efea76/85/Model-Based-Software-and-Data-Integration-Communications-in-Computer-and-Information-Science-8-1st-Edition-Ralf-Detlef-Kutsche-21-320.jpg)
Model Based Software and Data Integration Communications in Computer and Information Science 8 1st Edition Ralf-Detlef Kutsche
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- 5. Model Based Software and Data Integration Communications in Computer and Information Science 8 1st Edition Ralf-Detlef Kutsche Digital Instant Download Author(s): Ralf-Detlef Kutsche, Nikola Milanovic ISBN(s): 9783540789987, 3540789987 Edition: 1 File Details: PDF, 7.11 MB Year: 2008 Language: english
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- 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 1 3
- 9. 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 1865-0929 ISBN-10 3-540-78998-7 Springer Berlin Heidelberg New York ISBN-13 978-3-540-78998-7 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 5 4 3 2 1 0
- 10. Preface The First International Workshop on Model-Based Software and Data Integra- tion (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 in- dustry 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 con- cepts not being broadly applied to system integration. Our workshop addressed this issue. The selection of papers tried to introduce a strict model-based de- sign, verification, development and evolution methodologies to system integra- tion 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
- 11. VI Preface language (UML). He presented a talk “Key Technical and Cultural Challenges for Model-Based Software Engineering,” in which he identified short- and long- term 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 In- tegration,” a joint effort of software SMEs and science under the acronym BIZY- CLE, 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 tech- nology 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
- 12. Organization MBSDI 2008 was organized by the Berlin University of Technology, Institute for Software Engineering and Theoretical Computer Science, research group Com- putation 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 Ralf-Detlef Kutsche (TU Berlin, Germany) Co-chair 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)
- 13. VIII 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 pro- grams 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 con- ference 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 re- viewed 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).
- 14. Table of Contents Invited Papers The Art of Creating Models and Models Integration . . . . . . . . . . . . . . . . . 1 Miroslaw Malek Modeling Services – An Inter-disciplinary Perspective. . . . . . . . . . . . . . . . . 8 Stefan Tai and Steffen Lamparter Data Mashups for Situational Applications . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Volker Markl, Mehmet Altinel, David Simmen, and Ashutosh Singh Data Integration Combining Effectiveness and Efficiency for Schema Matching Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Alsayed Algergawy, Eike Schallehn, and Gunter Saake Model-Driven Development of Complex and Data-Intensive Integration Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Matthias Böhm, Dirk Habich, Wolfgang Lehner, and Uwe Wloka Towards a Metrics Suite for Object-Relational Mappings . . . . . . . . . . . . . . 43 Stefan Holder, Jim Buchan, and Stephen G. MacDonell Software Architectures, Services and Migration View-Based Integration of Process-Driven SOA Models at Various Abstraction Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 Huy Tran, Uwe Zdun, and Schahram Dustdar Model-Driven Development of Composite Applications . . . . . . . . . . . . . . . . 67 Susanne Patig Towards Identification of Migration Increments to Enable Smooth Migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Niels Streekmann and Wilhelm Hasselbring Model-Based and Semantic Approaches Service-Based Architecture for Ontology-Driven Information Integration in Dynamic Logistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 A. Smirnov, T. Levashova, N. Shilov, and A. Kashevnik
- 15. X Table of Contents State of the Art on Topic Map Building Approaches . . . . . . . . . . . . . . . . . . 102 Nebrasse Ellouze, Mohamed Ben Ahmed, and Elisabeth Métais Construction of Consistent Models in Model-Driven Software Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Gabriele Taentzer Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
- 16. R.-D. Kutsche and N. Milanovic (Eds.): MBSDI 2008, CCIS 8, pp. 1–7, 2008. © Springer-Verlag Berlin Heidelberg 2008 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 evalua- tion 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 func- tional 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
- 17. 2 M. Malek 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]. As- tronomy 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 re- sulted 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 as- tounding. 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 sto- chastic 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 im- plementation (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 struc- tured 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.
- 18. The Art of Creating Models and Models Integration 3 In the eighties the dominant development was pattern design [4] that promoted the re- use of certain solutions in software development process and with objects [5] and com- ponents [6] the software reuse became a reality. Also, representation of finite state auto- mata, 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 syn- thetize 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 - Com- puter-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 ser- vice • 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 complemen- tary 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 activi- ties, 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 op- posite to the goals of a real system. It is highly desirable to be able to measure the dis- tance 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 real- ity (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
- 19. 4 M. Malek 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. Creation of artifacts, systems that do not exist in nature, so it is difficult to as- sess quantitatively their quality even when they are created as there is no refer- ence point. The only way out are comparative analysis (e.g., who has developed bigger or faster engine, algorithm, etc.). 2. 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). 3. 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. 4. Demand for an ever-growing number of features (e.g., scalability, adaptivity or a real-time behavior). 5. 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). 6. Dynamicity of systems caused by varying configurations, patches updates, up- grades/downgrades requires highly flexible and dynamic models. 7. Composability and integration (e.g., ability to combine various service models into a business process; models integration of structure, behavior and both func- tional and non-functional properties; integration of software, hardware, interop- erability/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 mak- ing 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.
- 20. The Art of Creating Models and Models Integration 5 This best practice guide [8] is based on the experience we have gained when inves- tigating these topics: a. complexity reduction, showing that selecting the most predictive subset of vari- ables contributes more to model quality than selecting a particular linear or non- linear modeling technique b. 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, c. data-based empirical modeling of complex software systems, cross benchmark- ing 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. d) Model Application c) Model Estimation b) Variable Selection / Complexity Reduction forward selection backward elimination probabilistic w rapper ARMA / AR multivariate linear unsiversal basis functions (UBF) radial basis functions (RBF) support vector machines (SVM) sensitivity analysis forecasting a) System Observation time series (numerical) log files (categorical) e) Reaction / Actuation offline system adaptation online reaction schemes f) closing the control loop system experts ... 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 forecast- ing 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].
- 21. 6 M. Malek 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 Func- tion (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 de- sign 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 un- derstanding 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 tam- ing 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 creat- ing 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 Lan- guages in Mathematical Optimization, Kluwer, Boston (2006) 3. Nassi, I., Shneiderman, B.: Flowchart Techniques for Structured Programming. In: SIG- PLAN 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)
- 22. The Art of Creating Models and Models Integration 7 6. Szyperski, C.: Component Software: Beyond Object-Oriented Programming, 2nd edn. Ad- dison-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 Architec- ture. In: Proceedings of High Assurance Systems Engineering Symposium, Dallas, Texas (2007)
- 23. 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 Stefan Tai and Steffen Lamparter Karlsruhe Institute of Technology (KIT), Universität Karlsruhe (TH) Karlsruhe Service Research Institute 76128 Karlsruhe, Germany stefan.tai@kit.edu, sla@aifb.uni-karlsruhe.de 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 soft- ware-as-a-service on the Web, and is thus changing the way distributed computing sys- tems 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 ser- vices are increasingly ICT-enabled. Therefore, new ways of business process manage- ment, 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 ser- vices and computing services. We argue for an inter-disciplinary approach to modeling and engineering services, and discuss major challenges for model-driven service engi- neering. 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.
- 24. Modeling Services – An Inter-disciplinary Perspective 9 The Service Lifecycle comprises the phases of service inception and strategy, ser- vice 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 Archi- tecture (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 market- place which acts as an intermediary, and contracts must be established between the con- sumer 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-as- software (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.
- 25. 10 S. Tai and S. Lamparter 4 Example: Cloud Computing Services We illustrate our discussion of service modeling for the case of cloud computing ser- vices. 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 mid- dleware 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 re- sources 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, (scal- able) 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 stan- dard 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 en- sure availability, reliability, and security. Economic challenges range from competi- tive 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 busi- ness implications of outsourcing middleware and the business transformation needed. Organizational and possibly governmental and other regulatory policies must be con- sidered. 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 appro- priate business and ICT models in support of the above challenges. Technical and eco- nomic 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 ser- vice engineering. 5 Summary and Outlook Emerging services such as cloud computing services introduce new and complex eco- nomic and technical challenges. These are fundamentally changing the way that busi- nesses can operate and the way distributed computing systems are designed. Service
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