An automatic model-to-model mapping and transformation methodology to serve model-based systems engineering
Tiexin Wang (),
Sebastien Truptil and
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Tiexin Wang: University de Toulouse - Mines Albi
Sebastien Truptil: University de Toulouse - Mines Albi
Frederick Benaben: University de Toulouse - Mines Albi
Information Systems and e-Business Management, 2017, vol. 15, issue 2, No 5, 323-376
Abstract With enterprise collaboration becoming increasingly frequent, the ability of an enterprise to cooperate with others has become one of the core factors in gaining competitive advantage. This trend has led to an urgent requirement to improve cooperation ability. To this end, model-based systems engineering is being adapted so that it can be used to represent and simulate the working processes of enterprises. Model-to-model mappings and transformations, as important aspects in model-based systems engineering, have become two of the key factors in improving the cooperation capabilities of enterprises. However, the foundations for achieving automatic model-to-model transformation have not yet been built. Normally, model transformation rules are built on the basis of model mappings, and model mappings concern semantic or syntactic representations. One of the difficulties in achieving model-to-model mappings and transformations lies in detecting the semantics and semantic relations that are conveyed in different models. This paper presents an automatic model-to-model mapping and transformation methodology, which applies semantic and syntactic checking measurements to detect the meanings and relations between different models automatically. Both of the semantic and syntactic checking measurements are combined into a refined meta-model based model transformation process. To evaluate the performance of this methodology, we demonstrate its applicability with a realistic example.
Keywords: Enterprise collaboration; Model-driven engineering; Model-to-model mappings; Automatic model-to-model transformation; Semantic and syntactic checking (search for similar items in EconPapers)
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