Efficient approximation of the maximal preference scores by lightweight cubic views

  • Authors:

  • Yueguo Chen;Bin Cui;Xiaoyong Du;Anthony K. H. Tung

  • Affiliations:

  • Renmin University of China, MOE, China;Peking University, Beijing, China;Renmin University of China, MOE, China, and Renmin University of China, Beijing, China;National University of Singapore, Singapore

  • Venue:
  • Proceedings of the 15th International Conference on Extending Database Technology
  • Year:
  • 2012

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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.