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|Title:||Efficient approximation of the maximal preference scores by lightweight cubic views|
|Keywords:||best preference score|
|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|
|Appears in Collections:||Staff Publications|
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