Personalized top-k skyline queries in high-dimensional space

Jongwuk Lee, Gae won You, Seung won Hwang

Research output: Contribution to journalArticlepeer-review

72 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)45-61
Number of pages17
JournalInformation Systems
Issue number1
Publication statusPublished - 2009 Mar

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture


Dive into the research topics of 'Personalized top-k skyline queries in high-dimensional space'. Together they form a unique fingerprint.

Cite this