Towards an efficient approach to manage graph data evolution : conceptual modelling and experimental
This paper describes a new temporal graph modelling solution to organize and memorize changes in a business application. To do so, we enrich the basic graph by adding the concepts of states and instances.
Our model has first the advantage of representing a complete temporal evolution of the graph, at the level of: (i) the graph structure, (ii) the attribute set of entities/relationships and (iii) the attributes’ value of entities/relationships.
Then, it has the advantage of memorizing in an optimal manner evolution traces of the graph and retrieving easily temporal information about a graph component. To validate the feasibility of our proposal, we implement our proposal in Neo4j, a data store based on property graph model.
We then compare its performance in terms of storage and querying time to the classical modelling approach of temporal graph. Our results show that our model outperforms the classical approach by reducing disk usage by 12 times and saving up to 99% queries’ runtime.