Improving the performance of querying multidimensional RDF data using aggregates
Résumé
In this paper, we propose a novel approach to tackle the problem of querying large volume of statistical RDF cubes. Our approach relies on combining pre-aggregation strategies and the performance of NoSQL engines to represent and manage statistical RDF data. Specifically, we define a conceptual modeling solution to represent original RDF data with aggregates in a multidimensional structure. We complete the conceptual modeling with a logical design process based on well-known multidimensional RDF graph and property-graph representations. We implement our proposed model in RDF triple stores and a property-graph NoSQL database, and we compare the querying performance, with and without aggregates. Experimental results, on real-world datasets containing 81.92 million triplets, show that pre-aggregation allows reducing query runtime in both RDF triple stores and property-graph NoSQL databases. Neo4j NoSQL database with aggregates outperforms RDF Jena TDB2 and Virtuoso triple stores, speeding up to 99% query runtime.
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