D.: Neo4EMF, a Scalable Persistence Layer for EMF Models

Abstract. Several industrial contexts require software engineering meth-ods and tools able to handle large-size artifacts. The central idea of ab-straction makes model-driven engineering (MDE) a promising approach in such contexts, but current tools do not scale to very large models (VLMs): already the task of storing and accessing VLMs from a persist-ing support is currently inefficient. In this paper we propose a scalable persistence layer for the de-facto standard MDE framework EMF. The layer exploits the efficiency of graph databases in storing and accessing graph structures, as EMF models are. A preliminary experimentation shows that typical queries in reverse-engineering EMF models have good performance on such persistence layer, compared to file-based backends.