Improving Memory Efficiency for Processing Large-Scale Models

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Provided by: RWSoftware
Topic: Software
Format: PDF
Scalability is a main obstacle for applying Model-Driven Engineering (MDE) to reverse engineering, or to any other activity manipulating large models. Existing solutions to persist and query large models are currently inefficient and strongly linked to memory availability. In this paper, the authors propose a memory unload strategy for Neo4EMF, a persistence layer built on top of the Eclipse Modeling Framework (EMF) and based on a Neo4j database backend. Their solution allows the user to partially unload a model during the execution of a query by using a periodical dirty saving mechanism and transparent reloading.
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