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World Wide Web

, Volume 17, Issue 3, pp 285–309 | Cite as

Efficient top-k similarity join processing over multi-valued objects

  • Wenjie ZhangEmail author
  • Liming Zhan
  • Ying Zhang
  • Muhammad Aamir Cheema
  • Xuemin Lin
Article

Abstract

The top-k similarity joins have been extensively studied and used in a wide spectrum of applications such as information retrieval, decision making, spatial data analysis and data mining. Given two sets of objects \(\mathcal U\) and \(\mathcal V\), a top-k similarity join returns k pairs of most similar objects from \(\mathcal U \times \mathcal V\). In the conventional model of top-k similarity join processing, an object is usually regarded as a point in a multi-dimensional space and the similarity is measured by some simple distance metrics like Euclidean distance. However, in many applications an object may be described by multiple values (instances) and the conventional model is not applicable since it does not address the distributions of object instances. In this paper, we study top-k similarity join over multi-valued objects. We apply two types of quantile based distance measures, ϕ-quantile distance and ϕ-quantile group-base distance, to explore the relative instance distribution among the multiple instances of objects. Efficient and effective techniques to process top-k similarity joins over multi-valued objects are developed following a filtering-refinement framework. Novel distance, statistic and weight based pruning techniques are proposed. Comprehensive experiments on both real and synthetic datasets demonstrate the efficiency and effectiveness of our techniques.

Keywords

Query processing Joins Multi-valued objects 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Wenjie Zhang
    • 1
    Email author
  • Liming Zhan
    • 1
  • Ying Zhang
    • 1
  • Muhammad Aamir Cheema
    • 1
  • Xuemin Lin
    • 1
  1. 1.School of Computer Science & EngineeringUniversity of New South WalesSydneyAustralia

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