Discovery of path nearby clusters in spatial networks

Handle URI:
http://hdl.handle.net/10754/564184
Title:
Discovery of path nearby clusters in spatial networks
Authors:
Shang, Shuo; Zheng, Kai; Jensen, Christian S.; Yang, Bin; Kalnis, Panos ( 0000-0002-5060-1360 ) ; Li, Guohe; Wen, Ji Rong
Abstract:
The discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial districts) with respect to a user-specified travel route. Given a set of spatial objects O (e.g., POIs, geo-tagged photos, or geo-tagged tweets) and a query route q , if a cluster c has high spatial-object density and is spatially close to q , it is returned by the query (a cluster is a circular region defined by a center and a radius). This query aims to bring important benefits to users in popular applications such as trip planning and location recommendation. Efficient computation of the PNC query faces two challenges: how to prune the search space during query processing, and how to identify clusters with high density effectively. To address these challenges, a novel collective search algorithm is developed. Conceptually, the search process is conducted in the spatial and density domains concurrently. In the spatial domain, network expansion is adopted, and a set of vertices are selected from the query route as expansion centers. In the density domain, clusters are sorted according to their density distributions and they are scanned from the maximum to the minimum. A pair of upper and lower bounds are defined to prune the search space in the two domains globally. The performance of the PNC query is studied in extensive experiments based on real and synthetic spatial data. © 2014 IEEE.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Knowledge and Data Engineering
Issue Date:
1-Jun-2015
DOI:
10.1109/TKDE.2014.2382583
Type:
Article
ISSN:
10414347
Sponsors:
This work is partly supported by the National Natural Science Foundation of China (NSFC. 61402532), the Science Foundation of China University of Petroleum-Beijing (No. 2462013YJRC031), the Excellent Talents of Beijing Program (No. 2013D009051000003), and by a grant from the Obel Family Foundation. Guohe Li is the corresponding author.
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorShang, Shuoen
dc.contributor.authorZheng, Kaien
dc.contributor.authorJensen, Christian S.en
dc.contributor.authorYang, Binen
dc.contributor.authorKalnis, Panosen
dc.contributor.authorLi, Guoheen
dc.contributor.authorWen, Ji Rongen
dc.date.accessioned2015-08-03T12:35:31Zen
dc.date.available2015-08-03T12:35:31Zen
dc.date.issued2015-06-01en
dc.identifier.issn10414347en
dc.identifier.doi10.1109/TKDE.2014.2382583en
dc.identifier.urihttp://hdl.handle.net/10754/564184en
dc.description.abstractThe discovery of regions of interest in large cities is an important challenge. We propose and investigate a novel query called the path nearby cluster (PNC) query that finds regions of potential interest (e.g., sightseeing places and commercial districts) with respect to a user-specified travel route. Given a set of spatial objects O (e.g., POIs, geo-tagged photos, or geo-tagged tweets) and a query route q , if a cluster c has high spatial-object density and is spatially close to q , it is returned by the query (a cluster is a circular region defined by a center and a radius). This query aims to bring important benefits to users in popular applications such as trip planning and location recommendation. Efficient computation of the PNC query faces two challenges: how to prune the search space during query processing, and how to identify clusters with high density effectively. To address these challenges, a novel collective search algorithm is developed. Conceptually, the search process is conducted in the spatial and density domains concurrently. In the spatial domain, network expansion is adopted, and a set of vertices are selected from the query route as expansion centers. In the density domain, clusters are sorted according to their density distributions and they are scanned from the maximum to the minimum. A pair of upper and lower bounds are defined to prune the search space in the two domains globally. The performance of the PNC query is studied in extensive experiments based on real and synthetic spatial data. © 2014 IEEE.en
dc.description.sponsorshipThis work is partly supported by the National Natural Science Foundation of China (NSFC. 61402532), the Science Foundation of China University of Petroleum-Beijing (No. 2462013YJRC031), the Excellent Talents of Beijing Program (No. 2013D009051000003), and by a grant from the Obel Family Foundation. Guohe Li is the corresponding author.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectEfficiencyen
dc.subjectOptimizationen
dc.subjectPath Nearby Clusteren
dc.subjectSpatial Networksen
dc.subjectSpatiotemporal Databasesen
dc.titleDiscovery of path nearby clusters in spatial networksen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalIEEE Transactions on Knowledge and Data Engineeringen
dc.contributor.institutionState Key Laboratory of Petroleum Resources and Prospecting, Department of Computer Science, China University of Petroleum, Beijing, Chinaen
dc.contributor.institutionKey Laboratory of Data Engineering and Knowledge Engineering MOE, Renmin University of China, Chinaen
dc.contributor.institutionSchool of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australiaen
dc.contributor.institutionDepartment of Computer Science, Aalborg University, Aalborg East, Denmarken
kaust.authorKalnis, Panosen
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