Abstract
To overcome scalability and performance issues for process queries over a web-scale RDF data, various studies have proposed RDF SPARQL query processing algorithm using parallel processing manners. However, it is hard to resolve the scalability and performance issues together because the problem of communication overhead between nodes is closely related to the data distribution for parallel processing. For efficient RDF query parallel processing, it is essential to distribute and process data evenly while reducing communication overhead. In this paper, we propose RDF query parallel processing algorithms with RDF data partitioning technique to guarantee evenly distributed data over the cluster. We also propose our in-memory RDF query processing system as a form of Bulk Synchronization Parallel system to reduce network overhead. Our empirical evaluation results show that the proposed system outperforms a popular RDF-3X on LUBM benchmark and UniProt queries from 2.20 to 43.08 times. Especially, the effectiveness of the system improves significantly when the SPARQL queries are complex with high input and select.
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