Discovery of accessible locations using region-based geo-social data

Handle URI:
http://hdl.handle.net/10754/627374
Title:
Discovery of accessible locations using region-based geo-social data
Authors:
Wang, Yan; Li, Jianmin; Zhong, Ying; Zhu, Shunzhi; Guo, Danhuai; Shang, Shuo
Abstract:
Geo-social data plays a significant role in location discovery and recommendation. In this light, we propose and study a novel problem of discovering accessible locations in spatial networks using region-based geo-social data. Given a set Q of query regions, the top-k accessible location discovery query (k ALDQ) finds k locations that have the highest spatial-density correlations to Q. Both the spatial distances between locations and regions and the POI (point of interest) density within the regions are taken into account. We believe that this type of k ALDQ query can bring significant benefit to many applications such as travel planning, facility allocation, and urban planning. Three challenges exist in k ALDQ: (1) how to model the spatial-density correlation practically, (2) how to prune the search space effectively, and (3) how to schedule the searches from multiple query regions. To tackle the challenges and process k ALDQ effectively and efficiently, we first define a series of spatial and density metrics to model the spatial-density correlation. Then we propose a novel three-phase solution with a pair of upper and lower bounds of the spatial-density correlation and a heuristic scheduling strategy to schedule multiple query regions. Finally, we conduct extensive experiments on real and synthetic spatial data to demonstrate the performance of the developed solutions.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Wang Y, Li J, Zhong Y, Zhu S, Guo D, et al. (2018) Discovery of accessible locations using region-based geo-social data. World Wide Web. Available: http://dx.doi.org/10.1007/s11280-018-0538-5.
Publisher:
Springer Nature
Journal:
World Wide Web
Issue Date:
17-Mar-2018
DOI:
10.1007/s11280-018-0538-5
Type:
Article
ISSN:
1386-145X; 1573-1413
Sponsors:
This paper is partly supported by Natural Science Foundation of P.R.China (No. 61373147, and No. 61672442), Fujian Province Science and Technology Plan Project (No. 2016Y0079, No. 2017J01783), the Education and Scientific Research Project for Youth and Middle-aged Teachers in Fujian (No. JA15365), the Open Research Fund Program of Guangdong Province Key Laboratory of Popular High Performance Computers of Shenzhen University, and the Open Research Fund Program of Guangdong Provincial Big Data Collaborative Innovation Center, Shenzhen University.
Additional Links:
http://link.springer.com/article/10.1007/s11280-018-0538-5
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Yanen
dc.contributor.authorLi, Jianminen
dc.contributor.authorZhong, Yingen
dc.contributor.authorZhu, Shunzhien
dc.contributor.authorGuo, Danhuaien
dc.contributor.authorShang, Shuoen
dc.date.accessioned2018-03-21T13:56:06Z-
dc.date.available2018-03-21T13:56:06Z-
dc.date.issued2018-03-17en
dc.identifier.citationWang Y, Li J, Zhong Y, Zhu S, Guo D, et al. (2018) Discovery of accessible locations using region-based geo-social data. World Wide Web. Available: http://dx.doi.org/10.1007/s11280-018-0538-5.en
dc.identifier.issn1386-145Xen
dc.identifier.issn1573-1413en
dc.identifier.doi10.1007/s11280-018-0538-5en
dc.identifier.urihttp://hdl.handle.net/10754/627374-
dc.description.abstractGeo-social data plays a significant role in location discovery and recommendation. In this light, we propose and study a novel problem of discovering accessible locations in spatial networks using region-based geo-social data. Given a set Q of query regions, the top-k accessible location discovery query (k ALDQ) finds k locations that have the highest spatial-density correlations to Q. Both the spatial distances between locations and regions and the POI (point of interest) density within the regions are taken into account. We believe that this type of k ALDQ query can bring significant benefit to many applications such as travel planning, facility allocation, and urban planning. Three challenges exist in k ALDQ: (1) how to model the spatial-density correlation practically, (2) how to prune the search space effectively, and (3) how to schedule the searches from multiple query regions. To tackle the challenges and process k ALDQ effectively and efficiently, we first define a series of spatial and density metrics to model the spatial-density correlation. Then we propose a novel three-phase solution with a pair of upper and lower bounds of the spatial-density correlation and a heuristic scheduling strategy to schedule multiple query regions. Finally, we conduct extensive experiments on real and synthetic spatial data to demonstrate the performance of the developed solutions.en
dc.description.sponsorshipThis paper is partly supported by Natural Science Foundation of P.R.China (No. 61373147, and No. 61672442), Fujian Province Science and Technology Plan Project (No. 2016Y0079, No. 2017J01783), the Education and Scientific Research Project for Youth and Middle-aged Teachers in Fujian (No. JA15365), the Open Research Fund Program of Guangdong Province Key Laboratory of Popular High Performance Computers of Shenzhen University, and the Open Research Fund Program of Guangdong Provincial Big Data Collaborative Innovation Center, Shenzhen University.en
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/article/10.1007/s11280-018-0538-5en
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s11280-018-0538-5en
dc.subjectLocation discoveryen
dc.subjectRegionen
dc.subjectRecommendationen
dc.subjectGeo-social dataen
dc.titleDiscovery of accessible locations using region-based geo-social dataen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalWorld Wide Weben
dc.eprint.versionPost-printen
dc.contributor.institutionXiamen University of Technology, Xiamen, Chinaen
dc.contributor.institutionGuangdong Provincial Big Data Collaborative Innovation Center, Shenzhen University CNIC, Chinese Academy of Sciences, Beijing, Chinaen
dc.contributor.institutionGuangdong Province Key Laboratory of Popular High Performance Computers of Shenzhen University, Shenzhen, Chinaen
kaust.authorShang, Shuoen
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