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Journals ACM SIGAPP Applied Computing Review Volume 11 Issue 4

Accelerating Large Semantic Web Databases by Parallel Join Computations of SPARQL Queries Jinghua Groppe Institute of Information Systems (IFIS), University of L beck Ratzeburger Allee 160 D-23538 L beck, Germany +49 451 500 5705 Sven Groppe Institute of Information Systems (IFIS), University of L beck Ratzeburger Allee 160 D-23538 L beck, Germany +49 451 500 5706 groppej@ifis.uni-luebeck.de ABSTRACT While a number of optimizing techniques have been developed to efficiently process increasing large Semantic Web databases, these optimization approaches have not fully leveraged the powerful computation capability of modern computers. Today ™s multi-core computers promise an enormous performance boost by providing a parallel computing platform. Although the parallel relational database systems have been well built, parallel query computing in Semantic Web databases have not extensively been studied. In this work, we develop the parallel algorithms for join computations of SPARQL queries. Our performance study shows that the parallel computation of SPARQL queries significantly speeds up querying large Semantic Web databases. 9 groppe@ifis.uni-luebeck.de However, very few parallel approaches can achieve such ideal speedup. Since the existence of non-parallelizable parts, most of them have a near-linear speed-up for small numbers of processing units, and the speedup does not become larger than

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Accelerating large semantic web databases by parallel join computations of SPARQL queries

Groppe, Jinghua; Groppe, Sven
ACM SIGAPP Applied Computing Review , Volume 11 (4)
Association for Computing Machinery – Dec 1, 2011

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