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Efficient Processing of Large Spatial Queries Using Interior Approximations

  • Ravi K. Kothuri
  • Siva Ravada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2121)

Abstract

Spatial data in CAD/CAM and geographic information systems involve arbitrarily-shaped 2- and 3-dimensional geometries. Queries on such complex geometry data involve identification of data geometries that interact with a specified query geometry. Since geometry-geometry comparisons are expensive due to the large sizes of the data geometries, spatial engines avoid unnecessary comparisons by first comparing the MBRs and filtering out irrelevant geometries. If the query geometry is large compared to the data geometries, this filtering technique may not be effective in improving the performance. In this paper, we describe how to reduce geometry-geometry comparisons by first filtering using the interior approximations of geometries (in addition to and after comparing the exteriors, i.e., the MBRs). We implemented this technique as part of the R-tree indexes in Oracle Spatial and observed that the query performance improves by more than 50% (or a factor of 2) for most queries on real spatial datasets.

Keywords

Query Time Tile Array Query Performance Query Response Time Interior Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ravi K. Kothuri
    • 1
  • Siva Ravada
    • 1
  1. 1.Spatial TechnologiesNEDC Oracle CorporationNashua

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