Journal of Engineering and Applied Sciences

Year: 2018
Volume: 13
Issue: 2
Page No. 493 - 500

Generative Automatic Matching Between Heterogeneous Meta-Model’ Systems

Authors : Zouhair Ibn Batouta, Rachid Dehbi, Mohammed Talea and Omar Hajoui

Abstract: Building computer systems has become increasingly difficult, this is essentially due to the great number of existing solutions. The aim of this study is to propose a new approach allowing the matching between meta-models of different systems, this will allow the generation between models conforming to these connected meta-models. First, we will elaborate a taxonomy study on existing approaches, then we present the architecture of our generative matching approach named GAM (Generative Automatic Matching), after that, we will introduce a case study explaining our approach. Finally, we will conclude by a SWOT analysis between the different matching approaches.

How to cite this article:

Zouhair Ibn Batouta, Rachid Dehbi, Mohammed Talea and Omar Hajoui, 2018. Generative Automatic Matching Between Heterogeneous Meta-Model’ Systems. Journal of Engineering and Applied Sciences, 13: 493-500.

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