Handling behavioral semantics in persistent meta-modeling systems

Abstract

The increasing number of information systems modeling formalisms raises several problems such as data integration or data exchange. To address these problems, several meta-modeling systems have been proposed. However, few of them use a database as a back-end repository in order to offer a persistent solution for addressing over sized models. Yet, with the growing size of data manipulated in information systems, there is a need to exploit databases properties like scalability and querying capabilities. In this paper, we present persistent meta-modeling systems with their meta-modeling capabilities. We show that these systems support the definition of structural and descriptive semantics of models, but they can not express the behavioral semantics of models. Therefore, the aim of our work is to combine benefits of classic modeling systems, typically their capability to express behavioral semantics of models elements, together with advantages of databases i.e. their scalability and querying capabilities. Our approach focuses on the capability to dynamically introduce new operators that could be exploited by model-based databases exploitation languages. In particular, such operators could be implemented with an external program stored outside the database, or with a web service. As a consequence of this extension, we will be able to perform model transformations in database, trigger web services from relational databases, information exchange and data integration could be also supported in a persistent context.