Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Efficient processing of spatial joins using R-trees
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CIKM '93 Proceedings of the second international conference on Information and knowledge management
A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Processing and optimization of multiway spatial joins using R-trees
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Integration of spatial join algorithms for processing multiple inputs
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Spatial join selectivity using power laws
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Complexity of estimating multi-way join result sizes for area skewed spatial data
Information Processing Letters
An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
Selectivity Estimation for Spatial Joins with Geometric Selections
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Cost Models for Join Queries in Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Selectivity Estimation for Spatial Joins
Proceedings of the 17th International Conference on Data Engineering
Estimating the Selectivity of Spatial Queries Using the `Correlation' Fractal Dimension
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Reasoning about Binary Topological Relations
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
A Small Set of Formal Topological Relationships Suitable for End-User Interaction
SSD '93 Proceedings of the Third International Symposium on Advances in Spatial Databases
Selectivity Estimation of Complex Spatial Queries
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Accurate Estimation of the Cost of Spatial Selections
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
ACM Transactions on Database Systems (TODS)
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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Spatial join is a fundamental operation for many spatial queries in Geographical Information Systems (GIS). Therefore, the query optimizer of a GIS needs to evaluate the selectivity of spatial joins, in order to find the best execution plan for a given query. This situation has made it necessary to find good and efficient estimators for spatial join selectivity. In particular, spatial join estimation with respect to sets of rectangles is necessary. Indeed, in GIS sets of rectangles are generated in order to produce a synthetic representation of real geometric values through the Minimum Bounding Rectangles (MBR). Several methods for this estimation have been proposed in literature. One of the best methods is based on precalculated histograms, that describe the distribution of rectangles in the reference space using grid based data structures. The size of an histogram for a given dataset can be comparable to the size of the R-tree built on the same dataset [4]. In this paper we present a new technique for estimating spatial join selectivity considering sets of rectangles as datasets. In particular, we propose a technique that is independent of the distribution of the rectangles in the reference space and produces an auxiliary structure which is an order of magnitude smaller than the corresponding histogram. Indeed, the proposed technique is based on very few statistical parameters and on a unique grid shared by all datasets.