Generative Matching Between Heterogeneous Meta-Model' Systems Based on Hybrid Heuristic

Generative Matching Between Heterogeneous Meta-Model' Systems Based on Hybrid Heuristic

Zouhair Ibn Batouta (Hassan II University, Faculty of Science Ben M'Sik, LTI Laboratory, Casablanca, Morocco), Rachid Dehbi (Hassan II University, Faculty of Science Aïn Chock, LR2I Laboratory, Maarif, Morocco), Mohamed Talea (Hassan II University, Faculty of Science Ben M'Sik, LTI Laboratory, Casablanca, Morocco) and Hajoui Omar (Faculty of Science Ben M'Sik Hassan II University, Casablanca, Morocco)
Copyright: © 2019 |Pages: 19
DOI: 10.4018/JITR.2019040104
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Nowadays, designing and building computer systems has become increasingly difficult; this is essentially due to the great number of existing solutions. This article proposes a hybrid heuristic allowing the connection between meta-models of different systems, which will allow the generation between models conforming to these connected meta-models. First, this article presents the architecture of the generative matching approach named generative automatic matching (GAM), then is introduced an important part of this approach, a hybrid heuristic allowing the matching between the meta-models. Finally, the authors conclude by a multiple criteria evaluation of this approach.
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Generative Automatic Matching Approach

This notion of generative matching or generative automatic matching has never been used before in the literature; Indeed, most of the approaches that have dealt with the problem of heterogeneities of the meta-models dealt only with the matching part between the models or even the meta-models, without treating the generation component between the models conforming to these meta-models, which presents the strong point of our approach. In this section, we will present our new approach GAM that addresses the problem of increasing heterogeneity of solutions. The aim of this approach is to allow the automatic generation of a system based on the matching between the different meta-models constituting the system, this approach is compose of two phases:

  • The first step of our approach allows the linkage between completely heterogeneous meta-models, not just the linkage between Level 1 schemes or instances and models or even meta-models dealing with the same domains or representing different meta-models of the same version as is the case for traditional matching approaches, for example this approach encompasses the alignment between database meta-models (relational or big data), and meta-models representing schemas as XML meta-models and meta-models of different object-oriented languages like c # or java;

  • The second step of our approach is the treatment of a second essential component, namely automatic generation between models; Indeed, the result of the matching is exploited to generate models called target of a final system from source models and that based on the automatic matching found in the first step.

Figure 1 shows the overall architecture of our approach.

Figure 1.

Global architecture of the generative matching approach


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