Skyline query processing for uncertain data

Mohamed E. Khalefa, Mohamed F Mokbel, Justin J. Levandoski

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

27 Scopus citations

Abstract

Recently, several research efforts have addressed answering skyline queries efficiently over large datasets. However, this research lacks methods to compute these queries over uncertain data, where uncertain values are represented as a range. In this paper, we define skyline queries over continuous uncertain data, and propose a novel, efficient framework to answer these queries. Query answers are probabilistic, where each object is associated with a probability value of being a query answer. Typically, users specify a probability threshold, that each returned object must exceed, and a tolerance value that defines the allowed error margin in probability calculation to reduce the computational overhead. Our framework employs an efficient two-phase query processing algorithm.

Original languageEnglish (US)
Title of host publicationCIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops
Pages1293-1296
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10 - Toronto, ON, Canada
Duration: Oct 26 2010Oct 30 2010

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other19th International Conference on Information and Knowledge Management and Co-located Workshops, CIKM'10
CountryCanada
CityToronto, ON
Period10/26/1010/30/10

Keywords

  • Algorithms
  • Design

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    Khalefa, M. E., Mokbel, M. F., & Levandoski, J. J. (2010). Skyline query processing for uncertain data. In CIKM'10 - Proceedings of the 19th International Conference on Information and Knowledge Management and Co-located Workshops (pp. 1293-1296). (International Conference on Information and Knowledge Management, Proceedings). https://doi.org/10.1145/1871437.1871604