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Return to Spatial and Nearest Neighbor Queries
Spatial database operations are typically performed in two steps. In the filtering step, indexes and the minimum bound- ing rectangles (MBRs) of the objects are used to quickly determine a set of candidate objects, and in the refinement step, the actual geometries of the objects are retrieved and compared to the query geometry or each other. Because of the complexity of the computational geometry algorithms involved, the CPU cost of the refinement step is usually the dominant cost of the operation for complex geometries such as polygons. In this paper, we propose a novel ap- proach to address this problem using efficient rendering and searching capabilities of modern graphics hardware. This approach does not require expensive pre-processing of the data or changes to existing storage and index structures, and it applies to both intersection and distance predicates. Our experiments with real world datasets show that by combining hardware and software methods, the overall computational cost can be reduced substantially for both spatial selections and joins.
@inproceedings {DBLP:conf/sigmod/SunAA03, author = {Chengyu Sun and Divyakant Agrawal and Amr El Abbadi}, booktitle = {SIGMOD Conference}, title = {Hardware Acceleration for Spatial Selections and Joins.}, pages = {455-466}, year = {2003}, url = {db/conf/sigmod/sigmod2003.html#SunAA03}, ee = {http://www.acm.org/sigmod/sigmod03/eproceedings/papers/r17p03.pdf}, crossref = {conf/sigmod/2003}, bibsource = {DBLP, http://dblp.uni-trier.de} }  ©2004 Association for Computing Machinery |
