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

Authors:
Wan D. Bae;Shayma Alkobaisi;Scott T. Leutenegger
Affiliations:
Department of Computer Science, University of Denver, Denver, CO;Department of Computer Science, University of Denver, Denver, CO;Department of Computer Science, University of Denver, Denver, CO
Venue:
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Year:
2006

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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.