Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164236
Title: Cost-aware and distance-constrained Collective Spatial Keyword Query
Authors: Chan, Harry Kai-Ho
Liu, Shengxin
Long, Cheng
Wong, Raymond Chi-Wing
Keywords: Engineering::Computer science and engineering::Information systems::Information systems applications
Issue Date: 2021
Source: Chan, H. K., Liu, S., Long, C. & Wong, R. C. (2021). Cost-aware and distance-constrained Collective Spatial Keyword Query. IEEE Transactions On Knowledge and Data Engineering, 1-1. https://dx.doi.org/10.1109/TKDE.2021.3095388
Project: RG20/19 (S)
M4082302
Journal: IEEE Transactions on Knowledge and Data Engineering
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.
URI: https://hdl.handle.net/10356/164236
ISSN: 1041-4347
DOI: 10.1109/TKDE.2021.3095388
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/ 10.1109/TKDE.2021.3095388.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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