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.
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.