RDF and SPARQL are increasingly used in a broad range of information management scenarios (e.g., governments, corporations, and startups). Scalable SPARQL querying has been the main issue for virtually all the recent RDF triplestores. This paper presents WA-RDF, a middleware that addresses workload-adaptive management of large RDF graphs. Our middleware not only employs all the most used NoSQL data models but also provides a novel RDF data partitioning approach based on a fragmentation strategy that maps RDF data into multiple NoSQL databases. This workload-aware partitioning scheme provides, in turn, efficient processing of SPARQL queries over these NoSQL databases. Our experimental evaluation shows that the solution is promising, outperforming three recent baselines.
CITATION STYLE
Santana, L. H. Z., & dos Santos Mello, R. (2019). Querying in a Workload-Aware Triplestore Based on NoSQL Databases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11707 LNCS, pp. 159–173). Springer. https://doi.org/10.1007/978-3-030-27618-8_12
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