Efficient evaluation of partially-dimensional range queries using adaptive R*-tree

Yaokai Feng, Akifumi Makinouchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper is about how to efficiently evaluate partially-dimensional range queries, which are often used in many actual applications. If the existing multidimensional indices are employed to evaluate partially-dimensional range queries, then a great deal of information that is irrelevant to the queries also has to be read from disk. A modification of R*-tree is described in this paper to ameliorate such a situation. Discussions and experiments indicate that the proposed modification can clearly improve the performance of partially-dimensional range queries, especially for large datasets.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 17th International Conference, DEXA 2006, Proceedings
PublisherSpringer Verlag
Pages687-696
Number of pages10
Volume4080 LNCS
ISBN (Print)3540378715, 9783540378716
Publication statusPublished - 2006
Event17th International Conference on Database and Expert Systems Applications, DEXA 2006 - Krakow, Poland
Duration: Sep 4 2006Sep 8 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4080 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other17th International Conference on Database and Expert Systems Applications, DEXA 2006
CountryPoland
CityKrakow
Period9/4/069/8/06

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science

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