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An Index Data Structure for Searching in Metric Space Databases

  • Roberto Uribe
  • Gonzalo Navarro
  • Ricardo J. Barrientos
  • Mauricio Marín
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)

Abstract

This paper presents the Evolutionary Geometric Near-neighbor Access Tree (EGNAT) which is a new data structure devised for searching in metric space databases. The EGNAT is fully dynamic, i.e., it allows combinations of insert and delete operations, and has been optimized for secondary memory. Empirical results on different databases show that this tree achieves good performance for high-dimensional metric spaces. We also show that this data structure allows efficient parallelization on distributed memory parallel architectures. All this indicates that the EGNAT is suitable for conducting similarity searches on very large metric space databases.

Keywords

Voronoi Diagram Range Query Computer Science Department Query Object Secondary Memory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Roberto Uribe
    • 1
  • Gonzalo Navarro
    • 2
  • Ricardo J. Barrientos
    • 1
  • Mauricio Marín
    • 1
    • 3
  1. 1.Computer Engineering DepartmentUniversity of MagallanesChile
  2. 2.Computer Science DepartmentUniversity of Chile 
  3. 3.Center for Quaternary Studies, CEQUAChile

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