Abstract
Given a set of N multi-dimensional points, we study the computation of φ-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.
Work supported by grants HKU 7380/02E and CityU 1163/04E from Hong Kong RGC, and SRG grant (Project NO: 7001843) from City University of Hong Kong.
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Yiu, M.L., Mamoulis, N., Tao, Y. (2006). Efficient Quantile Retrieval on Multi-dimensional Data. In: Ioannidis, Y., et al. Advances in Database Technology - EDBT 2006. EDBT 2006. Lecture Notes in Computer Science, vol 3896. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11687238_13
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DOI: https://doi.org/10.1007/11687238_13
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