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Conference Papers Year : 2014

Neo4EMF : when big models are no longer an issue


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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hal-01026219 , version 1 (11-04-2023)


  • 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. ⟨hal-01026219⟩
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