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Searching and Updating Metric Space Databases Using the Parallel EGNAT

  • Mauricio Marin
  • Roberto Uribe
  • Ricardo Barrientos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4487)

Abstract

The Evolutionary Geometric Near-neighbor Access Tree (EGNAT) is a recently proposed data structure that is suitable for indexing large collections of complex objects. It allows searching for similar objects represented in metric spaces. The sequential EGNAT has been shown to achieve good performance in high-dimensional metric spaces with properties (not found in others of the same kind) of allowing update operations and efficient use of secondary memory. Thus, for example, it is suitable for indexing large multimedia databases. However, comparing two objects during a search can be a very expensive operation in terms of running time. This paper shows that parallel computing upon clusters of PCs can be a practical solution for reducing running time costs. We describe alternative distributions for the EGNAT index and their respective parallel search/update algorithms and concurrency control mechanism.

Keywords

Query Processing Distance Calculation Range Query 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 Berlin Heidelberg 2007

Authors and Affiliations

  • Mauricio Marin
    • 1
    • 2
  • Roberto Uribe
    • 2
  • Ricardo Barrientos
    • 2
  1. 1.Yahoo! Research, SantiagoChile
  2. 2.DCC, University of MagallanesChile

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