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Towards Efficient Join Processing over Large RDF Graph Using MapReduce

  • Xiaofei Zhang
  • Lei Chen
  • Min Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7338)

Abstract

Existing solutions for answering SPARQL queries in a shared-nothing environment using MapReduce failed to fully explore the substantial scalability and parallelism of the computing framework. In this paper, we propose a cost model based RDF join processing solution using MapReduce to minimize the query responding time as much as possible. After transforming a SPARQL query into a sequence of MapReduce jobs, we propose a novel index structure, called All Possible Join tree (APJ-tree), to reduce the searching space for the optimal execution plan of a set of MapReduce jobs. To speed up the join processing, we employ hybrid join and bloom filter for performance optimization. Extensive experiments on real data sets proved the effectiveness of our cost model. Our solution has as much as an order of magnitude time saving compared with the state of art solutions.

Keywords

Query Processing Cost Model Input Size SPARQL Query Query Processing Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiaofei Zhang
    • 1
  • Lei Chen
    • 1
  • Min Wang
    • 2
  1. 1.Hong Kong University of Science and TechnologyHong Kong
  2. 2.HP LabsBeijingChina

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