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

Xin Lin; Jianliang Xu; Haibo Hu

doi:10.1109/ICDE.2016.7498388

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

Xin Lin, Jianliang Xu, Haibo Hu

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-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 language	English
Title of host publication	2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
Publisher	IEEE
Pages	1488-1489
Number of pages	2
ISBN (Electronic)	9781509020195
DOIs	https://doi.org/10.1109/ICDE.2016.7498388
Publication status	Published - 22 Jun 2016
Event	32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland Duration: 16 May 2016 → 20 May 2016

Conference

Conference	32nd IEEE International Conference on Data Engineering, ICDE 2016
Country/Territory	Finland
City	Helsinki
Period	16/05/16 → 20/05/16

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

More information

10.1109/ICDE.2016.7498388

Cite this

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title = "Reverse keyword search for spatio-textual top-k queries in location-based services",

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.",

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doi = "10.1109/ICDE.2016.7498388",

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Reverse keyword search for spatio-textual top-k queries in location-based services. / Lin, Xin; Xu, Jianliang; Hu, Haibo.
2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. IEEE, 2016. p. 1488-1489 7498388.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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

Abstract

Conference

ASJC Scopus subject areas

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