An incremental refining spatial join algorithm for estimating query results in GIS

Wan D. Bae, Shayma Alkobaisi, Scott T. Leutenegger

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

3 Citations (Scopus)

Abstract

Geographic information systems (GIS) must support large georeferenced data sets. Due to the size of these data sets finding exact answers to spatial queries can be very time consuming. We present an incremental refining spatial join algorithm that can be used to report query result estimates while simultaneously provide incrementally refined confidence intervals for these estimates. Our approach allows for more interactive data exploration. While similar work has been done in relational databases, to the best of our knowledge this is the first work using this approach in GIS. We investigate different sampling methodologies and evaluate them through extensive experimental performance comparisons. Experiments on real and synthetic data show an order of magnitude response time improvement relative to the exact answer obtained when using the R-tree join.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 17th International Conference, DEXA 2006, Proceedings
PublisherSpringer Verlag
Pages935-944
Number of pages10
ISBN (Print)3540378715, 9783540378716
DOIs
Publication statusPublished - 2006
Externally publishedYes
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)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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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