Model transformations are at the heart of model driven engineering (MDE) and can be used in many different application scenarios. For instance, model transformations are used to integrate very large models. As a consequence, they are becoming more and more complex. However, these transformations are still developed manually. Several code patterns are implemented repetitively, increasing the probability of programming errors and reducing code reusability. There is not yet a complete solution that automates the development of model transformations. In this paper we propose a novel approach that uses matching transformations and weaving models to semi-automate the development of transformations. Matching transformations are a special kind of transformations that implement heuristics and algorithms to create weaving models. Weaving models are models that capture different kinds of relationships between models. Our solution enables to rapidly implement and to customize these heuristics. We combine different heuristics, and we propose a new metamodel-based heuristic that exploits metamodel data to automatically produce weaving models. The weaving models are derived into model integration transformations.

No abstract

No abstract

No abstract

Ontologies are formal specifications of conceptualizations. Their designs require to understand the concepts involved in the domain to be mapped. One well-known method to produce ontologies is to extract their concepts from relational databases. We conducted a practical study over a real-world scenario on applying existing rules and we identified open issues to be addressed, such as the utilization of logical metadata as a proper vocabulary, the implementation targeted to specific domains and mappings of hierarchical and self-hierarchical structures. In this paper, we present a novel approach that overcomes these issues. Our solution uses physical and logical models to enrich the terminology produced in the target ontology. It also contains a more comprehensive set of rules, taking into account instances and (self)hierarchies. We validate our approach with 2 experiments from the healthcare domain as input.

No abstract

Interoperability of heterogeneous data sources has been extensively studied in data integration applications. However, the increasing number of tools that produce data with very different formats, such as bug tracking, version control, etc., produces many different kinds of semantic heterogeneities. These semantic heterogeneities can be expressed as mappings between the tools metadata which describe the data manipulated by the tools. However, the semantics of complex mappings (n:1, 1:m and n:m relationships) is hard to support. These mappings are usually directly coded in executable transformations using arithmetic expressions. And there is no mechanism to create and reuse complex mappings. In this paper we propose a novel approach to capture different kinds of complex mappings using correspondence models. The main advantage is to use high level specifications for the correspondence models that enable representing different kinds of mappings. The correspondence models may be used to automatically produce executable transformations. To validate our approach, we provide an experimentation with a real world scenario using bug tracking tools.

Model transformations can be used in many different application scenarios, for instance, to provide interoperability between models of different size and complexity. As a consequence, they are becoming more and more complex. However, model transformations are typically developed manually. Several code patterns are implemented repetitively, thus increasing the probability of programming errors and reducing code reusability. There is not yet a complete solution that automates the development of model transformations. In this paper, we present a novel approach that uses matching transformations and weaving models to semi-automate the development of transformations. Weaving models are models that contain different kinds of relationships between model elements. These relationships capture different transformation patterns. Matching transformations are a special kind of transformations that implement methods that create weaving models. We present a practical solution that enables the creation and the customization of different creation methods in an efficient way. We combine different methods, and present a metamodel-based method that exploits metamodel data to automatically produce weaving models. The weaving models are derived into model integration transformations. To validate our approach, we present an experiment using metamodels with distinct size and complexity, which show the feasibility and scalability of our solution.