On nearby-fit spatial keyword queries

Geo-textual data is ubiquitous nowadays, where each object has a location and is associated with some keywords. Many types of queries based on geo-textual data, termed as spatial keyword queries, have been proposed, and are to find optimal object(s) in terms of both its (their) location(s) and keywo...

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Main Authors: Wei, Victor Junqiu, Wong, Raymond Chi-Wing, Long, Cheng, Hui, Pan
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/148146
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1481462021-04-26T03:23:31Z On nearby-fit spatial keyword queries Wei, Victor Junqiu Wong, Raymond Chi-Wing Long, Cheng Hui, Pan School of Computer Science and Engineering Engineering::Computer science and engineering::Information systems::Database management Spatial Keyword Query Approximation Algorithms Geo-textual data is ubiquitous nowadays, where each object has a location and is associated with some keywords. Many types of queries based on geo-textual data, termed as spatial keyword queries, have been proposed, and are to find optimal object(s) in terms of both its (their) location(s) and keywords. In this paper, we propose a new type of query called nearby-fit spatial keyword query (NSKQ), where an optimal object is defined based not only on the location and the keywords of the object itself, but also on those of the objects nearby. For example, in an application of finding a hotel, not only the location of a hotel but also the objects near the hotel (e.g., shopping malls, restaurants, and bus stops nearby) might need to be taken into consideration. The query is proved to be NP-hard, and in order to perform the query efficiently, we developed two approximate algorithms with small constant approximation factors equal to 1.155 and 1.79. We conducted extensive experiments based on both real and synthetic datasets, which verified our algorithms. Nanyang Technological University Accepted version The research of Cheng Long is partially supported by NTU SUG M4082302.020. 2021-04-26T03:23:31Z 2021-04-26T03:23:31Z 2020 Journal Article Wei, V. J., Wong, R. C., Long, C. & Hui, P. (2020). On nearby-fit spatial keyword queries. IEEE Transactions On Knowledge and Data Engineering, 32(11), 2198-2212. https://dx.doi.org/10.1109/TKDE.2019.2915295 1558-2191 https://hdl.handle.net/10356/148146 10.1109/TKDE.2019.2915295 2-s2.0-85092519804 11 32 2198 2212 en START-UP GRANT IEEE Transactions on Knowledge and Data Engineering © 2020 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.2019.2915295 application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering::Information systems::Database management
Spatial Keyword Query
Approximation Algorithms
spellingShingle Engineering::Computer science and engineering::Information systems::Database management
Spatial Keyword Query
Approximation Algorithms
Wei, Victor Junqiu
Wong, Raymond Chi-Wing
Long, Cheng
Hui, Pan
On nearby-fit spatial keyword queries
description Geo-textual data is ubiquitous nowadays, where each object has a location and is associated with some keywords. Many types of queries based on geo-textual data, termed as spatial keyword queries, have been proposed, and are to find optimal object(s) in terms of both its (their) location(s) and keywords. In this paper, we propose a new type of query called nearby-fit spatial keyword query (NSKQ), where an optimal object is defined based not only on the location and the keywords of the object itself, but also on those of the objects nearby. For example, in an application of finding a hotel, not only the location of a hotel but also the objects near the hotel (e.g., shopping malls, restaurants, and bus stops nearby) might need to be taken into consideration. The query is proved to be NP-hard, and in order to perform the query efficiently, we developed two approximate algorithms with small constant approximation factors equal to 1.155 and 1.79. We conducted extensive experiments based on both real and synthetic datasets, which verified our algorithms.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Wei, Victor Junqiu
Wong, Raymond Chi-Wing
Long, Cheng
Hui, Pan
format Article
author Wei, Victor Junqiu
Wong, Raymond Chi-Wing
Long, Cheng
Hui, Pan
author_sort Wei, Victor Junqiu
title On nearby-fit spatial keyword queries
title_short On nearby-fit spatial keyword queries
title_full On nearby-fit spatial keyword queries
title_fullStr On nearby-fit spatial keyword queries
title_full_unstemmed On nearby-fit spatial keyword queries
title_sort on nearby-fit spatial keyword queries
publishDate 2021
url https://hdl.handle.net/10356/148146
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