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Title: Efficient Approximation of The Maximal Preference Scores by Lightweight Cubic Views

Citation Type: Conference Paper

Publication Year: 2012

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Abstract: Given a multi-features data set, a best preference query(BPQ) computes the maximal preference score (MPS) thatthe tuples in the data set can achieve with respect to a preference function. BPQs are very useful in applications where users want to efficiently check whether many individual data sets contain tuples that are of interest to them. Although a BPQ can be navely answered by issuing a top-1 query and computing the score from the returned tuple, doing so might require to load a larger number of tuples externally. In this paper, we address the problem of efficient processing BPQs by using lightweight cubic(3-dimensional) views. With these in-memory views, the MPSs of BPQs can be efficiently estimated with an error bound guaranteed, by paying only a small number of I/Os. Extensive experimental results over real-life data sets show that our approximate solution can achieve the efficiency of up to three orders of magnitude compared to exact solutions, with certain accuracy guaranteed.

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Authors: Cui, Bin; Tung, Anthony K.H.; Du, Xiaoyong; Chen, Yueguo

Conference Name: EDBT

Publisher Location: Berlin, Germany

Data Collections: IPUMS USA

Topics: Other

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