HiBISCuS: Hypergraph-Based Source Selection for SPARQL Endpoint Federation

  • Muhammad Saleem
  • Axel-Cyrille Ngonga Ngomo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8465)

Abstract

Efficient federated query processing is of significant importance to tame the large amount of data available on the Web of Data. Previous works have focused on generating optimized query execution plans for fast result retrieval. However, devising source selection approaches beyond triple pattern-wise source selection has not received much attention. This work presents HiBISCuS, a novel hypergraph-based source selection approach to federated SPARQL querying. Our approach can be directly combined with existing SPARQL query federation engines to achieve the same recall while querying fewer data sources. We extend three well-known SPARQL query federation engines with HiBISCus and compare our extensions with the original approaches on FedBench. Our evaluation shows that HiBISCuS can efficiently reduce the total number of sources selected without losing recall. Moreover, our approach significantly reduces the execution time of the selected engines on most of the benchmark queries.

Keywords

#eswc2014Saleem 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Muhammad Saleem
    • 1
  • Axel-Cyrille Ngonga Ngomo
    • 1
  1. 1.IFI/AKSWUniversität LeipzigLeipzigGermany

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