Reverse ranking query over imprecise spatial data

Ken C.K. Lee, Mao Ye, Wang Chien Lee

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

2 Scopus citations

Abstract

The reverse rank of a (data) object o with respect to a given query object q (that measures the relative nearness of q to o) is said to be κ when q is the κ-th nearest neighbor of o in a geographical space. Based on the notion of reverse ranks, a Reverse Ranking (RR) query determines t objects with the smallest κ's with respect to a given query object q. In many situations that locations of objects and a query object can be imprecise, objects would receive multiple possible κ's. In this paper, we propose a notion of expected reverse ranks and evaluation of RR queries over imprecise data based on expected reverse ranks. For any object o, an expected reverse rank κ̄ is a weighted average of possible reverse ranks for individual instances of o with respect to different instances of a given query object q by taking their probabilities into account. We devise and present incremental κ̄ computation and two κ̄-Estimating algorithms to efficiently evaluate RR queries over imprecise data. The efficiency of our approach is demonstrated through experiments.

Original languageEnglish (US)
Title of host publicationCOM.Geo 2010 - 1st International Conference and Exhibition on Computing for Geospatial Research and Application
DOIs
StatePublished - Aug 6 2010
Event1st International Conference and Exhibition on Computing for Geospatial Research and Application, COM.Geo 2010 - Washington, DC, United States
Duration: Jun 21 2010Jun 23 2010

Publication series

NameACM International Conference Proceeding Series

Other

Other1st International Conference and Exhibition on Computing for Geospatial Research and Application, COM.Geo 2010
CountryUnited States
CityWashington, DC
Period6/21/106/23/10

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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