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

Yaokai Feng, Akifumi Makinouchi

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

5 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
ISBN (Print)3540378715, 9783540378716
DOIs
Publication statusPublished - 2006
Event17th International Conference on Database and Expert Systems Applications, DEXA 2006 - Krakow, Poland
Duration: Sept 4 2006Sept 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)0302-9743
ISSN (Electronic)1611-3349

Other

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

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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