Abstract
Processing SPARQL queries on single node is obviously not scalable, considering the rapid growth of RDF knowledge bases. This calls for scalable solutions of SPARQL query processing over Web-scale RDF data. There have been attempts for applying SPARQL query processing techniques in MapReduce environments. However, no study has been conducted on finding optimal partitioning and indexing schemes for distributing RDF data in MapReduce. In this paper, we investigate RDF data partitioning technique that provides effective indexing schemes to support efficient SPARQL query processing in MapReduce. Our extensive experiments over a huge real-life RDF dataset show the performance of the proposed partitioning and indexing schemes for efficient SPARQL query processing. © 2012 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Nie, Z., Du, F., Chen, Y., Du, X., & Xu, L. (2012). Efficient SPARQL query processing in mapreduce through data partitioning and indexing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7235 LNCS, pp. 628–635). https://doi.org/10.1007/978-3-642-29253-8_58
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