Personalized top-k skyline queries in high-dimensional space
SCIE
SCOPUS
- Title
- Personalized top-k skyline queries in high-dimensional space
- Authors
- Lee, J; You, GW; Hwang, SW
- Date Issued
- 2009-03
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Abstract
- As data of all 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. (c) 2008 Elsevier B.V. All rights reserved.
- Keywords
- Skyline queries; Personalization; Ranking
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/26283
- DOI
- 10.1016/J.IS.2008.04.004
- ISSN
- 0306-4379
- Article Type
- Article
- Citation
- INFORMATION SYSTEMS, vol. 34, no. 1, page. 45 - 61, 2009-03
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