Towards Meta-model Interoperability of Models through Intelligent Transformations

  • Authors:

  • José Barranquero Tolosa;Vicente García-Díaz;Oscar Sanjuán-Martínez;Héctor Fernández-Fernández;Gloria García-Fernández

  • Affiliations:

  • Department of Computer Science, University of Oviedo, Asturias, Spain;Department of Computer Science, University of Oviedo, Asturias, Spain;Department of Computer Science, University of Oviedo, Asturias, Spain;Department of Computer Science, University of Oviedo, Asturias, Spain;Department of Computer Science, UPSAM, Madrid, Spain

  • Venue:
  • IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
  • Year:
  • 2009

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Abstract

Models and transformations between models are provided as the core of Model-Driven Engineering, offering reusability of knowledge and processes. In order to establish the basis of future advances in this emerging paradigm, this paper is focused on the principles of meta-models and transformation models. Moreover, the concept of meta-model is becoming an essential artifact for MDE based solutions, thus we have centered our background review in the state of art related to meta-model specifications and model transformation technologies. Our research is aimed at getting a higher degree of interoperability among available meta-model specifications by raising the transformation models to the upper meta-layers. Some conclusions extracted suggest that this is still an early solution which demands greater efforts in terms of research, development and specification, with many interesting open subjects like design of generic editors for model-agnostic visual modeling, integration of model instances from different meta-models, improvements of the semantic knowledge offered by present modeling languages or even the evaluation of the applicability of graph transformation techniques towards formal transformation models.