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Communication Dans Un Congrès Année : 2019

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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Dates et versions

hal-03619438 , version 1 (25-03-2022)

Identifiants

  • HAL Id : hal-03619438 , version 1
  • OATAO : 24733

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Franck Ravat, Jiefu Song, Olivier Teste, Cassia Trojahn dos Santos. Improving the performance of querying multidimensional RDF data using aggregates. 34th ACM/SIGAPP Symposium on Applied Computing (SAC 2019), Apr 2019, Limassol, Cyprus. pp.2275-2284. ⟨hal-03619438⟩
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