Selectivity estimation in spatial networks
Proceedings of the 2008 ACM symposium on Applied computing
Node and edge selectivity estimation for range queries in spatial networks
Information Systems
Maximizing bichromatic reverse nearest neighbor for Lp-norm in two- and three-dimensional spaces
The VLDB Journal — The International Journal on Very Large Data Bases
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Existing estimation approaches for spatial databases often rely on the assumption that data distribution in a small region is uniform, which seldom holds in practice. Moreover, their applicability is limited to specific estimation tasks under certain distance metric. This paper develops the Power-method, a comprehensive technique applicable to a wide range of query optimization problems under both L驴 and L2 metrics. The Power-method eliminates the local uniformity assumption and is, therefore, accurate even for datasets where existing approaches fail. Furthermore, it performs estimation by evaluating only one simple formula with minimal computational overhead. Extensive experiments confirm that the Power-method outperforms previous techniques in terms of accuracy and applicability to various optimization scenarios.