URank: Formulation and efficient evaluation of top-k queries in uncertain databases

Mohamed A. Soliman, Ihab F. Ilyas, Kevin Chen Chuan Chang

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

Abstract

Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between query scores and data uncertainty makes traditional techniques inapplicable. We introduce URank, a system that processes new probabilistic formulations of top-k queries inuncertain databases. The new formulations are based on marriage of traditional top-k semantics with possible worlds semantics. URank encapsulates a new processing framework that leverages existing query processing capabilities, and implements efficient search strategies that integrate ranking on scores with ranking on probabilities, to obtain meaningful answers for top-k queries.

Original languageEnglish (US)
Title of host publicationSIGMOD 2007
Subtitle of host publicationProceedings of the ACM SIGMOD International Conference on Management of Data
Pages1082-1084
Number of pages3
DOIs
StatePublished - 2007
EventSIGMOD 2007: ACM SIGMOD International Conference on Management of Data - Beijing, China
Duration: Jun 12 2007Jun 14 2007

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Other

OtherSIGMOD 2007: ACM SIGMOD International Conference on Management of Data
CountryChina
CityBeijing
Period6/12/076/14/07

Keywords

  • Probabilistic data
  • Query processing
  • Ranking
  • Topk
  • Uncertain data

ASJC Scopus subject areas

  • Software
  • Information Systems

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