Region clustering based evaluation of multiple top-N selection queries

  • Authors:

  • Liang Zhu;Weiyi Meng;Wenzhu Yang;Chunnian Liu

  • Affiliations:

  • School of Mathematics and Computer Science, Hebei University, Baoding, Hebei 071002, China and College of Computer Science and Technology, Beijing University of Technology, Beijing 100022, China;Department of Computer Science, State University of New York at Binghamton, Binghamton, NY 13902, USA;School of Mathematics and Computer Science, Hebei University, Baoding, Hebei 071002, China;College of Computer Science and Technology, Beijing University of Technology, Beijing 100022, China

  • Venue:
  • Data & Knowledge Engineering
  • Year:
  • 2008

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Abstract

In many database applications, there are opportunities for multiple top-N queries to be evaluated at the same time. Often it is more cost effective to evaluate multiple such queries collectively than individually. In this paper, we propose a new method for evaluating multiple top-N queries concurrently over a relational database. The basic idea of this method is region clustering that groups the search regions of individual top-N queries into larger regions and retrieves the tuples from the larger regions. This method avoids having the same region accessed multiple times and reduces the number of random I/O accesses to the underlying databases. Extensive experiments are carried out to measure the performance of this new strategy and the results indicate that it is significantly better than the naive method of evaluating these queries one by one for both low-dimensional (2, 3, and 4) and high-dimensional (25, 50, and 104) data.