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DMT: A Flexible and Versatile Selectivity Estimation Approach for Graph Query

  • Jianhua Feng
  • Qian Qian
  • Yuguo Liao
  • Guoliang Li
  • Na Ta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3739)

Abstract

Efficient and accurate selectivity estimation in graph-structured data, specifically for complex branching path query, is becoming a challenging and all-important problem for query performance optimization. Precise and flexible statistics summarization about graph-structured data plays a crucial role for graph query selectivity estimation. We propose DMT, Dynamic Markov Table, which is a dynamic graph summarization based on Markov Table by applying flexible combination of 4 Optimized Rules which investigate local forward and backward inclusions. The efficient DMT construction algorithm DMTBuilder and DMT-based statistical methods are introduced for selectivity estimations of various graph queries. Our extensive experiments demonstrate the advantages in accuracy and scalability of DMT by comparing with previously known alternative, as well as the preferred Optimized Rules that would favor different situations.

Keywords

Query Pattern Optimize Rule Path Query Graph Query Path Expression 
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 2005

Authors and Affiliations

  • Jianhua Feng
    • 1
  • Qian Qian
    • 1
  • Yuguo Liao
    • 1
  • Guoliang Li
    • 1
  • Na Ta
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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