Investigating storage models for various types historical graphs

Our focus in this study revolves around enhancing the storage strategies for evolving graphs, particularly emphasizing the preservation and querying of their complete history. In particular, we review previous research on historical graph databases and storage systems, we introduce a way to generate historical graph datasets and we propose an enhanced methodology for a space-optimal vertex-centric model with MongoDB. This approach prioritizes space optimizations while managing to achieve notable advancements in the execution time of global queries, known for their complexity within entity-centric frameworks. Extensive experimentation was done, employing snapshot-based and interval-based datasets generated with the use of LDBC SNB generator, in order to validate our claims of achieving significant speed enhancements. Consequently, our approach enables the execution of more resource-intensive queries with improved efficiency, reduced client involvement, and less memory requirements.