Abstract
Efficient SPARQL query evaluation is a significant challenge when the database contains billions of RDF triples, which is very common for many existing Web-scale RDF data sources. We address this challenge by 1) effectively partitioning the whole RDF dataset into small partitions according to the schemas of the RDF subjects, and 2) elaborately placing the partitions within clusters so that, on each local partition, we can make the most advantage of the state-of-the-art SPARQL query processing engine, and across the partitions, we can exploit the power of parallel databases for achieving scalable query evaluation of massive RDF data. This paper introduces the data partitioning and placement strategies, as well as the SPARQL query evaluation and optimization techniques in a cluster environment. Experiments are conducted over a synthesized dataset and a real dataset containing billions of triples. The results demonstrate that better query evaluation performance over the baseline can be achieved.
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