Cost-Aware and Distance-Constrained Collective Spatial Keyword Query

With the proliferation of location-based services, geo-textual data is becoming ubiquitous. Objects involved in geo-textual data include geospatial locations, textual descriptions or keywords, and various attributes (e.g., a point-of-interest has its expenses and users' ratings). Many types of...

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Published in:	IEEE transactions on knowledge and data engineering Vol. 35; no. 2; pp. 1324 - 1336
Main Authors:	Chan, Harry Kai-Ho, Liu, Shengxin, Long, Cheng, Wong, Raymond Chi-Wing
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.02.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Approximation algorithms Cost function Criteria Geospatial analysis Keywords Location based services Mice Queries query processing Search problems Semantics spatial database Spatial databases Spatial keyword queries
ISSN:	1041-4347, 1558-2191
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Abstract	With the proliferation of location-based services, geo-textual data is becoming ubiquitous. Objects involved in geo-textual data include geospatial locations, textual descriptions or keywords, and various attributes (e.g., a point-of-interest has its expenses and users' ratings). Many types of spatial keyword queries have been proposed on geo-textual data. Among them, one prominent type is to find, for a query consisting of a query location and some query keywords, a set of multiple objects such that the objects in the set collectively cover all the query keywords and the object set is of good quality according to some criteria. Existing studies define the criteria either based on the geospatial information of the objects solely or simply treat the geospatial information and the attribute information of the objects together without differentiation though they may have different semantics and scales. As a result, they cannot provide users flexibility to express finer grained preferences on the objects. In this paper, we propose a new criterion which is to find a set of objects where the distance (defined based on the geospatial information) is at most a threshold specified by users and the cost (defined based on the attribute information) is optimized. We develop a suite of two algorithms including an exact algorithm and an approximation algorithm with provable guarantees for the problem. We conducted extensive experiments on real datasets which verified the efficiency and effectiveness of proposed algorithms.
AbstractList	With the proliferation of location-based services, geo-textual data is becoming ubiquitous. Objects involved in geo-textual data include geospatial locations, textual descriptions or keywords, and various attributes (e.g., a point-of-interest has its expenses and users' ratings). Many types of spatial keyword queries have been proposed on geo-textual data. Among them, one prominent type is to find, for a query consisting of a query location and some query keywords, a set of multiple objects such that the objects in the set collectively cover all the query keywords and the object set is of good quality according to some criteria. Existing studies define the criteria either based on the geospatial information of the objects solely or simply treat the geospatial information and the attribute information of the objects together without differentiation though they may have different semantics and scales. As a result, they cannot provide users flexibility to express finer grained preferences on the objects. In this paper, we propose a new criterion which is to find a set of objects where the distance (defined based on the geospatial information) is at most a threshold specified by users and the cost (defined based on the attribute information) is optimized. We develop a suite of two algorithms including an exact algorithm and an approximation algorithm with provable guarantees for the problem. We conducted extensive experiments on real datasets which verified the efficiency and effectiveness of proposed algorithms.
Author	Wong, Raymond Chi-Wing Long, Cheng Chan, Harry Kai-Ho Liu, Shengxin
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SubjectTerms	Algorithms Approximation algorithms Cost function Criteria Geospatial analysis Keywords Location based services Mice Queries query processing Search problems Semantics spatial database Spatial databases Spatial keyword queries
Title	Cost-Aware and Distance-Constrained Collective Spatial Keyword Query
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