Efficient validation of large models using the Mogwaï tool

Abstract

Scalable model persistence frameworks have been proposed to handle large (potentially generated) models involved in current industrial processes. They usually rely on databases to store and access the underlying models, and provide a lazy-loading strategy that aims to reduce the memory footprint of model navigation and manipulation. Dedicated query and transformation solutions have been proposed to further improve performances by generating native database queries leveraging the backend's advanced capabilities. However, existing solutions are not designed to specifically target the validation of a set of constraints over large models. They usually rely on low-level modeling APIs to retrieve model elements to validate, limiting the benefits of computing native database queries. In this paper we present an extension of the Mogwaï query engine that aims to handle large model validation efficiently. We show how model constraints are pre-processed and translated into database queries, and how the validation of the model can benefit from the underlying database optimizations. Our approach is released as a set of open source Eclipse plugins and is fully available online.