Kernel-Based skyline cardinality estimation

Zhenjie Zhang, Yin Yang, Ruichu Cai, Dimitris Papadias, Anthony Tung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationSIGMOD-PODS'09 - Proceedings of the International Conference on Management of Data and 28th Symposium on Principles of Database Systems
Pages509-521
Number of pages13
DOIs
Publication statusPublished - 2009
Externally publishedYes
EventInternational Conference on Management of Data and 28th Symposium on Principles of Database Systems, SIGMOD-PODS'09 - Providence, RI, United States
Duration: 29 Jun 20092 Jul 2009

Other

OtherInternational Conference on Management of Data and 28th Symposium on Principles of Database Systems, SIGMOD-PODS'09
CountryUnited States
CityProvidence, RI
Period29/6/092/7/09

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Keywords

  • Cardinality estimation
  • Kernel
  • Non-parametric methods
  • Skyline

ASJC Scopus subject areas

  • Software

Cite this

Zhang, Z., Yang, Y., Cai, R., Papadias, D., & Tung, A. (2009). Kernel-Based skyline cardinality estimation. In SIGMOD-PODS'09 - Proceedings of the International Conference on Management of Data and 28th Symposium on Principles of Database Systems (pp. 509-521) https://doi.org/10.1145/1559845.1559899