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Enhancing classic transformation languages to support multi-level modeling

Abstract

As practical tools for disciplined multi-level modeling have begun to mature, the problem of supporting simple and efficient transformations to-and-from multi-level models to facilitate interoperability has assumed growing importance. The challenge is not only to support efficient transformations between multi-level models, but also between multi-level and two-level model content represented in traditional modeling infrastructures such as the UML and programming languages. Multi-level model content can already be accessed by traditional transformation languages such as ATL and QVT, but in a way that is blind to the ontological classification information they contain. In this paper, we present an approach for making rule-based transformation languages “multi-level aware” so that the semantics of ontological classification as well as linguistic classification can be exploited when writing transformations.

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Notes

  1. In this paper we use the term “ontology” to refer the collection of all the (domain) model elements across all the ontological levels within the \(L_1\) linguistic level.

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Correspondence to Ralph Gerbig.

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Communicated by Prof. Juan de Lara and Prof. Zhenjiang Hu.

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Atkinson, C., Gerbig, R. & Tunjic, C.V. Enhancing classic transformation languages to support multi-level modeling. Softw Syst Model 14, 645–666 (2015). https://doi.org/10.1007/s10270-013-0384-y

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  • DOI: https://doi.org/10.1007/s10270-013-0384-y

Keywords

  • Multi-level transformation
  • Orthogonal classification architecture
  • Ontological classification
  • Linguistic classification