Towards an analysis of range query performance in spatial data structures
PODS '93 Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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
Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Direct spatial search on pictorial databases using packed R-trees
SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
On Rectangular Partitionings in Two Dimensions: Algorithms, Complexity, and Applications
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Optimal Histograms with Quality Guarantees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Rk-hist: an r-tree based histogram for multi-dimensional selectivity estimation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Sort-based query-adaptive loading of R-trees
Proceedings of the 21st ACM international conference on Information and knowledge management
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Spatial histograms are extremely useful for approximate query processing in large spatial databases. The problem of generating optimal spatial histograms is NP-hard; therefore, many heuristic-based methods have emerged over the last 15 years. Shortcomings of these methods are their complex algorithmic design and their sensitivity to parameter setting, preventing them to be easily integrated into real systems. In this paper, we present a class of spatial histograms derived from the popular family of R-tree indexes. We propose a cost-optimized approach that combines bulk-loading of R-trees and the construction of spatial histograms. This results in a robust histogram method with high accuracy for selectivity estimation of spatial queries. Our method does not require the setting of intuitive parameters at all. In addition, the estimation error continuously decreases with increasing number of histogram buckets, and therefore, our histogram methods can take benefit from large main memories. In an experimental evaluation, we compare the performance of our histograms with state-of-the-art spatial histograms. Our results confirm that our histograms provide low estimation errors and short build-times.