GraphVault: A Temporal Graph Persistence Engine
Julian Bichl, Thomas Driessen, Melanie Langermeier, Bernhard Bauer · 2024
Graph structures have gained increasing popularity in recent years as they offer comprehensive possibilities for managing and analyzing high interconnected data. In order to facilitate the orchestration of these data, graph databases have been developed enabling graphs to be stored as central entity. However, traditional graph databases and frameworks consider graphs as a inherently valid unit without temporal reference which can limit their ability to perform advanced analysis. This paper presents GraphVault, a graph persistence engine that is capable of efficiently storing graphs and reconstructing labeled property graphs over time. We present our temporal data model, which we mapped to a key-value engine using a purpose-built record design. The performance of our implementation is then compared to that of a conventional graph database.