Reverse keyword search for spatio-textual top-k queries in location-based services

Xin Lin, Jianliang Xu, Haibo Hu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

This paper proposes a novel query paradigm, namely reverse keyword search for spatio-textual top-k queries (RST Q). It returns the keywords under which a target object will be a spatio-textual top-k result. To efficiently process the new query, we devise a novel hybrid index KcR-tree to store and summarize the spatial and textual information of objects. To further improve the performance, we propose three query optimization techniques, i.e., KcR∗-tree, lazy upper-bound updating, and keyword set filtering. We also extend RST Q to allow the input location to be a spatial region instead of a point. Experimental results demonstrate the efficiency of our proposed query techniques in terms of both the computational cost and I/O cost.
Original languageEnglish
Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PublisherIEEE
Pages1488-1489
Number of pages2
ISBN (Electronic)9781509020195
DOIs
Publication statusPublished - 22 Jun 2016
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: 16 May 201620 May 2016

Conference

Conference32nd IEEE International Conference on Data Engineering, ICDE 2016
Country/TerritoryFinland
CityHelsinki
Period16/05/1620/05/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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