Workload-Aware RDF Partitioning and SPARQL Query Caching for Massive RDF Graphs stored in NoSQL Databases

• Luiz Henrique Zambom Santana Universidade Federal de Santa Catarina (UFSC)
• Ronaldo dos Santos Mello Universidade Federal de Santa Catarina (UFSC) https://orcid.org/0000-0003-4262-5474

DOI: https://doi.org/10.5753/sbbd.2017.170758

Resumo

Governments, corporations, startups, open data initiatives and other organizations are increasingly considering RDF and SPARQL in a broad range of information management scenarios. To reduce SPARQL querying times has been the main issue for virtually all the recent RDF triplestores, yet SPARQL caching techniques have not been broadly considered. In this paper we present Rendezvous, a middleware that addresses workload-adaptive management of large RDF graphs with a caching strategy for SPARQL query results. Our middleware provides a novel RDF data partitioning approach based on a fragmentation strategy that maps RDF data into multiple NoSQL databases. The focus of this paper is also on Rendezvous caching, which can reduce average response time by up to an order of magnitude. Our experimental evaluation shows that the approach is promising, outperforming a recent key/value-based caching baseline.

Palavras-chave: RDF, SPARQL, Graph, NoSQL

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