Efficient Processing of Large Spatial Queries Using Interior Approximations

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.

[1]  Nick Roussopoulos,et al.  Nearest neighbor queries , 1995, SIGMOD '95.

[2]  Klaus-Uwe Höffgen,et al.  Computing a Maximum Axis-Aligned Rectangle in a Convex Polygon , 1994, Information Processing Letters.

[3]  Hanan Samet,et al.  The Design and Analysis of Spatial Data Structures , 1989 .

[4]  Oliver Günther,et al.  Multidimensional access methods , 1998, CSUR.

[5]  Hanan Samet,et al.  Ranking in Spatial Databases , 1995, SSD.

[6]  Hans-Peter Kriegel,et al.  Efficient processing of spatial joins using R-trees , 1993, SIGMOD Conference.

[7]  Antonin Guttman,et al.  R-trees: a dynamic index structure for spatial searching , 1984, SIGMOD '84.

[8]  Hans-Peter Kriegel,et al.  A Storage and Access Architecture for Efficient Query Processing in Spatial Database Systems , 1993, SSD.

[9]  Walid G. Aref,et al.  On local heuristics to speed up polygon-polygon intersection tests , 1999, GIS '99.

[10]  Hans-Peter Kriegel,et al.  The R*-tree: an efficient and robust access method for points and rectangles , 1990, SIGMOD '90.

[11]  Christos Faloutsos,et al.  The R+-Tree: A Dynamic Index for Multi-Dimensional Objects , 1987, VLDB.

[12]  Max J. Egenhofer,et al.  Reasoning about Binary Topological Relations , 1991, SSD.

[13]  Hans-Peter Kriegel,et al.  Comparison of approximations of complex objects used for approximation-based query processing in spatial database systems , 1993, Proceedings of IEEE 9th International Conference on Data Engineering.

[14]  Christos Faloutsos,et al.  QBIC project: querying images by content, using color, texture, and shape , 1993, Electronic Imaging.

[15]  Andrew U. Frank,et al.  A Topological Data Model for Spatial Databases , 1990, SSD.

[16]  Jay Banerjee,et al.  Indexing medium-dimensionality data in Oracle , 1999, SIGMOD '99.

[17]  Hans-Peter Kriegel,et al.  The X-tree : An Index Structure for High-Dimensional Data , 2001, VLDB.

[18]  Mario A. López,et al.  STR: a simple and efficient algorithm for R-tree packing , 1997, Proceedings 13th International Conference on Data Engineering.