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An approximate oracle for distance in metric spaces

  • Yanling Yang
  • Kaizhong Zhang
  • Xiong Wang
  • Jason T. L. Wang
  • Dennis Shasha
Session III
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1448)

Abstract

In this paper we present a new data structure for estimating distances in a pseudo-metric space. Given are a database of objects and a distance function for the objects, which is a pseudo-metric. We map the objects to vectors in a pseudo-Euclidean space with a reasonably low dimension while preserving the distance between two objects approximately. Such a data structure can be used as an approximate oracle to process a broad class of pattern-matching based queries. Experimental results on both synthetic and real data show the good performance of the oracle in distance estimation.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Yanling Yang
    • 1
  • Kaizhong Zhang
    • 2
  • Xiong Wang
    • 3
  • Jason T. L. Wang
    • 4
  • Dennis Shasha
    • 5
  1. 1.Department of MathematicsBeijing Institute of Light IndustryBeijingChina
  2. 2.Department of Computer ScienceThe University of Western OntarioLondonCanada
  3. 3.Department of CISNew Jersey Institute of TechnologyNewarkUSA
  4. 4.Department of CISNew Jersey Institute of TechnologyNewarkUSA
  5. 5.Courant Institute of Mathematical SciencesNew York UniversityUSA

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