Probabilistic spatial queries on existentially uncertain data

Xiangyuan Dai, Man Lung Yiu, Nikos Mamoulis, Yufei Tao, Michail Vaitis

Research output: Journal article publicationConference articleAcademic researchpeer-review

44 Citations (Scopus)

Abstract

We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries and nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.
Original languageEnglish
Pages (from-to)400-417
Number of pages18
JournalLecture Notes in Computer Science
Volume3633
Publication statusPublished - 18 Oct 2005
Externally publishedYes
Event9th International Symposium on Spatial and Temporal Databases, SSTD 2005 - Angra dos Reis, Brazil
Duration: 22 Aug 200524 Aug 2005

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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