Personalized top-k skyline queries in high-dimensional space

  • Authors:

  • Jongwuk Lee;Gae-won You;Seung-won Hwang

  • Affiliations:

  • Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31, hyoja dong nam gu, Pohang 790784, Republic of Korea;Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31, hyoja dong nam gu, Pohang 790784, Republic of Korea;Department of Computer Science and Engineering, Pohang University of Science and Technology, San 31, hyoja dong nam gu, Pohang 790784, Republic of Korea

  • Venue:
  • Information Systems
  • Year:
  • 2009

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Abstract

As data of an unprecedented scale are becoming accessible, it becomes more and more important to help each user identify the ideal results of a manageable size. As such a mechanism, skyline queries have recently attracted a lot of attention for its intuitive query formulation. This intuitiveness, however, has a side effect of retrieving too many results, especially for high-dimensional data. This paper is to support personalized skyline queries as identifying ''truly interesting'' objects based on user-specific preference and retrieval size k. In particular, we abstract personalized skyline ranking as a dynamic search over skyline subspaces guided by user-specific preference. We then develop a novel algorithm navigating on a compressed structure itself, to reduce the storage overhead. Furthermore, we also develop novel techniques to interleave cube construction with navigation for some scenarios without a priori structure. Finally, we extend the proposed techniques for user-specific preferences including equivalence preference. Our extensive evaluation results validate the effectiveness and efficiency of the proposed algorithms on both real-life and synthetic data.