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Journal of Engineering and Applied Sciences
Year: 2018 | Volume: 13 | Issue: 2 | Page No.: 493-500
DOI: 10.3923/jeasci.2018.493.500  
Generative Automatic Matching Between Heterogeneous Meta-Model’ Systems
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.
DOI: 10.3923/jeasci.2018.493.500
URL: http://medwelljournals.com/abstract/?doi=jeasci.2018.493.500