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200 ENTERPRISE INFORMATION SYSTEMS VI 4.3 Implementation Synthesis The next step in the feature matching, when the solution domain is selected, is actual implementation synthesis. During this process, for every element of the problem domain model, a suitable element or a suitable configuration of elements of the solution domain model is selected. The result is a mapping from the problem domain model to the solution domain model (i.e. implementation). Suitability of the solution domain element(s) for a given problem domain model element is decided by their corresponding features. Descriptions of concepts (or domain elements) are given by the sets of their features: C = F = {fi} and sets of features of configurations of concepts are the unions of all the feature sets of elements in the configuration: {C1, ..., Cn} = F1 ��Fn We represent the mapping between the concepts of the problem domain and those of the solution domain: or simply: � : {C P } � {C S } {C P } � {C S } We reduce finding a suitable configuration in the solution domain for the generic case to different specific cases, which cover all situations. The first case is trivial – when the feature set of a problem domain element is a subset of the feature set of a certain solution domain element, then the problem domain element is mapped directly to this solution domain element: F P � F S � {C P } � {C S } The second case – when the feature set of a problem domain element is a subset of the union of feature sets of a configuration of solution domain elements, then the problem domain element is mapped directly to this configuration of the solution domain elements: F P � F S 1 � … � F S m � {C P } � {C S 1, … , C S m} The third case – when there exists a configuration of problem space elements consisting of n elements, then if the union of feature sets of these elements is a subset of the feature set of a certain solution domain element, the given configuration of problem domain elements is mapped to this solution domain element: F P 1 � … � F P n � F S � {C P 1, … , C P n} � {C S } The last case is the most complex and describes also the generic case – when there exists a configuration of problem space elements consisting of n elements, then if the union of feature sets of these elements is a subset of union of feature sets of a certain configuration of solution domain elements, the given configuration of the problem domain elements is mapped to this configuration of solution domain elements: F P 1 �� F P n � F S 1 �� F S m � {C P 1, ..., C P n} � {C S 1, ..., C S m} This step is driven by the structure of the problem domain model and the analysis model. 4.4 Selecting Between Alternatives Different solution domains usually have different non-functional features or quality attributes (Bass, Clements & Kazman, 1998). These non-functional features could be divided to run-time features (e.g. performance, security, availability, usability) and maintenance features (e.g. modifiability, portability, reusability, integrability, testability). The combination of non-functional features corresponds to a certain set of business goals (e.g. time to market, cost, projected lifetime, market share, rollout schedule). The non-functional requirements connected to the problem specification can be used to choose between possible solution domains and usage styles of the given solution domain elements (e.g. software architecture style). Inside a solution domain there may exist many configurations of solution domain elements, which can be used to implement the same functional or non -functional requirements. There feature matching algorithm can use different strategies of choosing between elements and configurations of the solution domain. There can be alternatives between the elements or configurations of the solution space, which offer similar feature sets: F P � F S 1 & F P � F S 2 In this case, during the feature matching, it is possible to use different strategies to make the decision between alternatives. Possible feature
matching strategies are maximal, minimal, or optimal. The maximal strategy, where the solution domain element or configuration is selected, if it provides most additional features for implementing a given problem domain element: |F S 1 \ F P | � |F S 2 \ F P | � {C P } � {C S 2} The minimal strategy, where the solution domain element or configuration is selected, if it provides least additional features for implementing a given problem domain element: |F S 1 \ F P | � |F S 2 \ F P | � {C P } � {C S 1} The optimal strategy, where a solution domain element or a configuration is selected, based on the cost function: cost(F S 1) � cost(F S 2) � {C P } � {C S 1} where the cost function cost(F) is based on nonfunctional features of C S i. For example, if we take into account the scalability requirements in the case described above, we would select the configuration built around the SessionBean instead of EntityBean for the concept policy. When selecting a suitable solution, the domain can be viewed as global optimization, selecting suitable configurations in the selected solution domain can be viewed as local optimization. 5 RELATED WORK A similar problem has been analyzed in the context of domain engineering approach in SEI (Peterson and Stanley, 1994). Peterson and Stanley have studied mapping of the domain model to a generic design. In their work, they presented mapping from the domain analysis results presented in FODA into the predefined architecture (OCA – Object Connection Architecture) by architecture elements. Another similar technique is presented in the Feature-Oriented Reuse Method (FORM) developed by K. C. Kang (Kang, 1998). In this method, also a feature space (result form FODA) is mapped into a predefined artifact space (an architecture) by using kinds of features identified in the feature modeling. Both of these methods allow mapping of the problem domain results only into predefined architecture. The difference of our approach from these two approaches is that we allow synthesis of FEATURE MATCHING IN MODEL-BASED SOFTWARE ENGINEERING implementations in different, not predefined solution domains. Selection of the architectural style, based on reasoning about the quality attributes of architectural styles is dealt with in the Attribute-Based Architecture Styles (ABAS) method (Bass, Clements & Kazman, 1998). Lately the MDA initiative from OMG (OMG, 2001a) has been establishing modeling standards needed to develop supporting tools for mapping platform independent models (PIMs) into platform specific models (PSMs). Techniques and tools presented in the article are in line with MDA and useful when the MDA approach is applied to the development of large-scale business systems. 6 CONCLUSIONS 201 The difference of our method from other domain specific and model-based methods is the separate step of solution domain analysis, which results in a reusable solution domain model, and using a feature space that is common to the problem and solution domains, for selecting the solution domain, the architecture style, and specific implementations. We have shown that there exists a common feature space for both the problem domain and solution domain elements. We have presented an algorithm based on this common feature space for selecting the solution domain, architectural style, and for synthesizing an implementation. We have also shown that it is possible to drive the solution domain selection and implementation synthesis algorithm with a suitable cost function. The presented method allows shorter software development cycles due to the automation of the implementation phase, reusability of the problem domain knowledge (i.e. business analysis models) with different solution domains (i.e. implementation technologies), and better usability of solution domain knowledge. It is applicable to OMG MDA for transformation or mapping of the platform independent model (PIM) to platform specific models (PSMs). In the future, providers of implementation technologies (e.g. J2EE) may supply also the models of their solution domains (incl. feature models), together with other artifacts of a given implementation technology. Together with the development of tools that could synthesize implementations based on the problem domain models by using feature matching, this would dramatically reduce the threshold of using new implementation technologies for software
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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The major limit A of the operation
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RELATIONAL SAMPLING FOR DATA QUALIT
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TOWARDS DESIGN RATIONALES OF SOFTWA
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((Brown et al., 1998), pp. 159-190,
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sive data filtering, presentation (
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• implementation changes in servi
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MEMORY MANAGEMENT FOR LARGE SCALE D
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Data Rate [bytes/sec] 1600000 14000
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E.g., DV Camcorder Camera Packets (
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Procedure CheckOptimal (n rs 1 ...n
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MEMORY MANAGEMENT FOR LARGE SCALE D
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PART 2 Artificial Intelligence and
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110 ENTERPRISE INFORMATION SYSTEMS
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DYNAMIC MULTI-AGENT BASED VARIETY F
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Page 169 and 170: AN EXPERIENCE IN MANAGEMENT OF IMPR
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Figure 4: Symbols emission in DAR-H
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A METHODOLOGY FOR INTERFACE DESIGN
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and mobility loss coupled with redu
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Tradeoff: The default input is poss
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Sessions on the VABS which are held
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A CONTACT RECOMMENDER SYSTEM FOR A
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A CONTACT RECOMMENDER SYSTEM FOR A
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- "Going to the sources". The user
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Here are the matrix W and P for our
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EMOTION SYNTHESIS IN VIRTUAL ENVIRO
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theme turns out to be surprisingly
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6 FROM FEATURES TO SYMBOLS 6.1 Face
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Electronic Imaging 2000 Conference
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to the accessibility problem for th
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Figure 3: Hierarchical View of the
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4 CONCLUSIONS AND FUTURE WORK On th
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PERSONALISED RESOURCE DISCOVERY SEA
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profile, the user defines his curre
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Abdelkafi, N. .....................
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