Efficient quantile retrieval on multi-dimensional data

  • Authors:
  • Man Lung Yiu;Nikos Mamoulis;Yufei Tao

  • Affiliations:
  • Department of Computer Science, University of Hong Kong, Hong Kong;Department of Computer Science, University of Hong Kong, Hong Kong;Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

  • Venue:
  • EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
  • Year:
  • 2006

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Abstract

Given a set of N multi-dimensional points, we study the computation of φ-quantiles according to a ranking function F, which is provided by the user at runtime. Specifically, F computes a score based on the coordinates of each point; our objective is to report the object whose score is the φN-th smallest in the dataset. φ-quantiles provide a succinct summary about the F-distribution of the underlying data, which is useful for online decision support, data mining, selectivity estimation, query optimization, etc. Assuming that the dataset is indexed by a spatial access method, we propose several algorithms for retrieving a quantile efficiently. Analytical and experimental results demonstrate that a branch-and-bound method is highly effective in practice, outperforming alternative approaches by a significant factor.