Neo4EMF : when big models are no longer an issue

Amine Benelallam 1, 2 Hugo Bruneliere 1, 2
1 ATLANMOD - Modeling Technologies for Software Production, Operation, and Evolution
LINA - Laboratoire d'Informatique de Nantes Atlantique, Département informatique - EMN, Inria Rennes – Bretagne Atlantique
Abstract : Handling effectively big EMF models has often been one of the main barriers that can retain from adopting modeling technologies in very large-scale complex systems. In this talk we present Neo4EMF, a Neo4j-based persistence framework allowing on-demand loading, storage, unloading and actual use of very large EMF models. Neo4EMF provides a No-SQL database persistence framework based on Neo4j, which is a transactional property-graph database that has proved having a remarkable running speed for connected data operations compared to relational databases. In terms of performance, Neo4EMF eases data access and storage not only in a manner to reduce time and memory usage but also to allow big EMF models to fit into a reduced amount of memory. This is made possible through a lightweight and on-demand loading mechanism. Moreover, Neo4EMF comes with a dirty saving mechanism allowing to store huge chunks of data even with limited memory resources.
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Communication dans un congrès
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Contributeur : Hugo Bruneliere <>
Soumis le : lundi 21 juillet 2014 - 10:14:33
Dernière modification le : lundi 3 décembre 2018 - 15:52:01


  • HAL Id : hal-01026219, version 1


Amine Benelallam, Hugo Bruneliere. Neo4EMF : when big models are no longer an issue. EclipseCon France 2014, Jun 2014, Toulouse, France. 2014, 〈〉. 〈hal-01026219〉



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