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https://doi.org/10.1145/2247596.2247626
Title: | Efficient approximation of the maximal preference scores by lightweight cubic views | Authors: | Chen, Y. Cui, B. Du, X. Tung, A.K.H. |
Keywords: | best preference score materialized views preference query top-k query |
Issue Date: | 2012 | Citation: | Chen, Y.,Cui, B.,Du, X.,Tung, A.K.H. (2012). Efficient approximation of the maximal preference scores by lightweight cubic views. ACM International Conference Proceeding Series : 240-251. ScholarBank@NUS Repository. https://doi.org/10.1145/2247596.2247626 | Abstract: | Given a multi-features data set, a best preference query (BPQ) computes the maximal preference score (MPS) that the 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 naïvely 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. © 2012 ACM. | Source Title: | ACM International Conference Proceeding Series | URI: | http://scholarbank.nus.edu.sg/handle/10635/41925 | ISBN: | 9781450307901 | DOI: | 10.1145/2247596.2247626 |
Appears in Collections: | Staff Publications |
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