Kernel-based skyline cardinality estimation

  • Authors:

  • Zhenjie Zhang;Yin Yang;Ruichu Cai;Dimitris Papadias;Anthony Tung

  • Affiliations:

  • National University of Singapore, Singapore, Singapore;Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong;South China University of Technology, Guangzhou, China;Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong;National University of Singapore, Singapore, Singapore

  • Venue:
  • Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
  • Year:
  • 2009

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Abstract

The skyline of a d-dimensional dataset consists of all points not dominated by others. The incorporation of the skyline operator into practical database systems necessitates an efficient and effective cardinality estimation module. However, existing theoretical work on this problem is limited to the case where all d dimensions are independent of each other, which rarely holds for real datasets. The state of the art Log Sampling (LS) technique simply applies theoretical results for independent dimensions to non-independent data anyway, sometimes leading to large estimation errors. To solve this problem, we propose a novel Kernel-Based (KB) approach that approximates the skyline cardinality with nonparametric methods. Extensive experiments with various real datasets demonstrate that KB achieves high accuracy, even in cases where LS fails. At the same time, despite its numerical nature, the efficiency of KB is comparable to that of LS. Furthermore, we extend both LS and KB to the k-dominant skyline, which is commonly used instead of the conventional skyline for high-dimensional data.