Skyline ranking for uncertain data with maybe confidence

Hyountaek Yong, Jin Ha Kim, Seung Won Hwang

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

10 Citations (Scopus)

Abstract

Skyline queries have been actively studied lately as they can effectively identify interesting candidate objects with low formulation overhead. In particular, this paper studies supporting skyline queries for the uncertuin data with "maybe" uncertainty, e.g., automatically extracted data. Prior skyline works on uncertain data assumes that every possible value for an uncertain object can be exhaustively enumerated (i.e., "alternatives" uncertainty) which is not applicable in many extraction scenarios. We develop fast algorithms that outperform the baseline approach by orders of magnitude and validate them over extensive evaluations.

Original languageEnglish
Title of host publicationProceedings of the 2008 - IEEE 24th International Conference on Data Engineering Workshop, ICDE'08
Pages572-579
Number of pages8
DOIs
Publication statusPublished - 2008
Event2008 - IEEE 24th International Conference on Data Engineering Workshop, ICDE'08 - Cancun, Mexico
Duration: 2008 Apr 72008 Apr 12

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other2008 - IEEE 24th International Conference on Data Engineering Workshop, ICDE'08
CountryMexico
CityCancun
Period08/4/708/4/12

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

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