Accelerating Large Semantic Web Databases by Parallel Join Computations of SPARQL Queries

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.
Original languageEnglish
JournalACM Applied Computing Review (ACR)
Volume11
Issue number4
Pages (from-to)60-70
Number of pages11
Publication statusPublished - 2011

Research Areas and Centers

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

DFG Research Classification Scheme

  • 409-06 Information Systems, Process and Knowledge Management
  • 409-04 Operating, Communication, Database and Distributed Systems

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