Given a set of N multi-dimensional points, we study the computation of phi;-quantiles according to a ranking function F, which is provided by the user at runtime. Specifically, F computes a score based on the coordinates of each point; our objective is to report the object whose score is the φN-th smallest in the dataset. φ-quantiles provide a succinct summary about the F-distribution of the underlying data, which is useful for online decision support, data mining, selectivity estimation, query optimization, etc. Assuming that the dataset is indexed by a spatial access method, we propose several algorithms for retrieving a quantile efficiently. Analytical and experimental results demonstrate that a branch-and-bound method is highly effective in practice, outperforming alternative approaches by a significant factor. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Yiu, M. L., Mamoulis, N., & Tao, Y. (2006). Efficient quantile retrieval on multi-dimensional data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3896 LNCS, pp. 167–185). https://doi.org/10.1007/11687238_13
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