Indexing multi-dimensional uncertain data with arbitrary probability density functions

  • Authors:
  • Yufei Tao;Reynold Cheng;Xiaokui Xiao;Wang Kay Ngai;Ben Kao;Sunil Prabhakar

  • Affiliations:
  • City University of Hong Kong, Hong Kong;Hong Kong Polytechnic University, Hung Hom, Hong Kong;City University of Hong Kong, Hong Kong;University of Hong Kong, Hong Kong;University of Hong Kong, Hong Kong;Purdue University, West Lafayette

  • Venue:
  • VLDB '05 Proceedings of the 31st international conference on Very large data bases
  • Year:
  • 2005

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Abstract

In an "uncertain database", an object o is associated with a multi-dimensional probability density function(pdf), which describes the likelihood that o appears at each position in the data space. A fundamental operation is the "probabilistic range search" which, given a value pq and a rectangular area rq, retrieves the objects that appear in rq with probabilities at least pq. In this paper, we propose the U-tree, an access method designed to optimize both the I/O and CPU time of range retrieval on multi-dimensional imprecise data. The new structure is fully dynamic (i.e., objects can be incrementally inserted/deleted in any order), and does not place any constraints on the data pdfs. We verify the query and update efficiency of U-trees with extensive experiments.