Benchmarking the RDF and Property Graph Model in the Temporal Dimension — A Case Study with Finbench

Temporal graph data is highly relevant for capturing the evolving nature of various domains, including financial transactions, social network interactions, supply chain logistics, disease outbreak modeling, and dynamic transportation systems. In this work, we benchmark the performance of temporal data representations in the property graph model (PGM) and RDF model using the FinBench transaction workload. Our contributions are twofold. First, we adapt the FinBench data generator to produce RDF datasets in two formats: (1) the RDF-Reification representation and (2) the RDF-Star extension. Second, we provide queries for three new databases: Memgraph (PGM) and two RDF databases, Virtuoso and GraphDB, with only the latter offering RDF-Star support. Our findings highlight key challenges in representing FinBench data in RDF, like missing functions of the SPARQL language in addressing certain query requirements. These insights provide valuable guidance for query writing and optimizing RDF-based representations for temporal graph workloads.