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Efficient Approximation of Well-Designed SPARQL Queries

  • Zhenyu Song
  • Zhiyong Feng
  • Xiaowang ZhangEmail author
  • Xin Wang
  • Guozheng Rao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)

Abstract

Query response time often influences user experience in the real world. However, the time of answering a SPARQL query with its all exact solutions in large scale RDF dataset possibly exceeds users’ tolerable waiting time, especially when it contains the OPT operations since the OPT operation is the least conventional operator in SPARQL. So it becomes essential to make a trade-off between the query response time and the accuracy of their solutions. That is, partial answers can be provided for users to reduce the response query time within their tolerable waiting time. In this paper, based on the depth of the OPT operation occurring in a query, we propose an approach to obtain its all approximate queries with less depth of the OPT operation. Although queries are approximated in this method, it remains the “non-optional” query patterns from users. This paper mainly discusses those queries with well-designed patterns since the OPT operation in a well-designed pattern is really “optional”. We remove “optional” triple patterns with less depth of the OPT operation and then obtain approximate queries with different depths of the OPT operation. Furthermore, we evaluate the approximate query efficiency and solutions precision with the degree of approximation. It shows that users can keep the balance between query efficiency and solutions precision by changing the degree of approximation.

Keywords

RDF SPARQL Well-designed patterns Approximate queries 

Notes

Acknowledgments

This work is supported by the program of the National Key Research and Development Program of China under 2016YFB1000603 and the National Natural Science Foundation of China (NSFC) under 61502336, 61373035. Xiaowang Zhang is supported by the Tianjin Thousand Young Talents Program.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Zhenyu Song
    • 1
    • 2
  • Zhiyong Feng
    • 1
    • 2
  • Xiaowang Zhang
    • 1
    • 2
    Email author
  • Xin Wang
    • 1
    • 2
  • Guozheng Rao
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.Tianjin Key Laboratory of Cognitive Computing and ApplicationTianjinChina

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