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Learning-Based SPARQL Query Performance Prediction

  • Wei Emma ZhangEmail author
  • Quan Z. Sheng
  • Kerry Taylor
  • Yongrui Qin
  • Lina Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10041)

Abstract

According to the predictive results of query performance, queries can be rewritten to reduce time cost or rescheduled to the time when the resource is not in contention. As more large RDF datasets appear on the Web recently, predicting performance of SPARQL query processing is one major challenge in managing a large RDF dataset efficiently. In this paper, we focus on representing SPARQL queries with feature vectors and using these feature vectors to train predictive models that are used to predict the performance of SPARQL queries. The evaluations performed on real world SPARQL queries demonstrate that the proposed approach can effectively predict SPARQL query performance and outperforms state-of-the-art approaches.

Keywords

SPARQL Feature modeling Prediction 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Wei Emma Zhang
    • 1
    Email author
  • Quan Z. Sheng
    • 1
  • Kerry Taylor
    • 2
  • Yongrui Qin
    • 3
  • Lina Yao
    • 4
  1. 1.School of Computer ScienceThe University of AdelaideAdelaideAustralia
  2. 2.Research School of Computer ScienceAustralian National UniversityCanberraAustralia
  3. 3.School of Computing and EngineeringUniversity of HuddersfieldHuddersfieldUK
  4. 4.School of Computer Science and EngineeringUNSW AustraliaSydneyAustralia

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