Parallelizing Join Computations of SPARQL Queries for Large Semantic Web Databases


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.

Original languageEnglish
Title of host publicationProceedings of the 2011 ACM Symposium on Applied Computing
Number of pages6
Place of PublicationNew York, NY, USA
Publication date21.03.2011
ISBN (Print)978-1-4503-0113-8
Publication statusPublished - 21.03.2011
Event26th Annual ACM Symposium on Applied Computing - TaiChung, Taiwan, Province of China
Duration: 21.03.201124.03.2011
Conference number: 85134

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Research Area: Intelligent Systems

DFG Research Classification Scheme

  • 409-04 Operating, Communication, Database and Distributed Systems


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