Personalized trajectory matching in spatial networks

Handle URI:
http://hdl.handle.net/10754/562876
Title:
Personalized trajectory matching in spatial networks
Authors:
Shang, Shuo; Ding, Ruogu; Zheng, Kai; Jensen, Christian Søndergaard; Kalnis, Panos ( 0000-0002-5060-1360 ) ; Zhou, Xiaofang
Abstract:
With the increasing availability of moving-object tracking data, trajectory search and matching is increasingly important. We propose and investigate a novel problem called personalized trajectory matching (PTM). In contrast to conventional trajectory similarity search by spatial distance only, PTM takes into account the significance of each sample point in a query trajectory. A PTM query takes a trajectory with user-specified weights for each sample point in the trajectory as its argument. It returns the trajectory in an argument data set with the highest similarity to the query trajectory. We believe that this type of query may bring significant benefits to users in many popular applications such as route planning, carpooling, friend recommendation, traffic analysis, urban computing, and location-based services in general. PTM query processing faces two challenges: how to prune the search space during the query processing and how to schedule multiple so-called expansion centers effectively. To address these challenges, a novel two-phase search algorithm is proposed that carefully selects a set of expansion centers from the query trajectory and exploits upper and lower bounds to prune the search space in the spatial and temporal domains. An efficiency study reveals that the algorithm explores the minimum search space in both domains. Second, a heuristic search strategy based on priority ranking is developed to schedule the multiple expansion centers, which can further prune the search space and enhance the query efficiency. The performance of the PTM query is studied in extensive experiments based on real and synthetic trajectory data sets. © 2013 Springer-Verlag Berlin Heidelberg.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Springer Science + Business Media
Journal:
The VLDB Journal
Issue Date:
31-Jul-2013
DOI:
10.1007/s00778-013-0331-0
Type:
Article
ISSN:
10668888
Sponsors:
This research is partially supported by the Natural Science Foundation of China (Grant No. 61232006), the National 863 High-tech Program (Grant No. 2012AA011001), the Australian Research Council (Grants No. DP110103423 and No. DP120102829), and the European Union (Grant No. FP7-PEOPLE-2010-ITN-264994). The research was performed when C. S. Jensen was with Aarhus University. Part of Shuo Shang's work was done when he was a research assistant professor in Aalborg University.
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.authorDing, Ruoguen
dc.contributor.authorZheng, Kaien
dc.contributor.authorJensen, Christian Søndergaarden
dc.contributor.authorKalnis, Panosen
dc.contributor.authorZhou, Xiaofangen
dc.date.accessioned2015-08-03T11:13:45Zen
dc.date.available2015-08-03T11:13:45Zen
dc.date.issued2013-07-31en
dc.identifier.issn10668888en
dc.identifier.doi10.1007/s00778-013-0331-0en
dc.identifier.urihttp://hdl.handle.net/10754/562876en
dc.description.abstractWith the increasing availability of moving-object tracking data, trajectory search and matching is increasingly important. We propose and investigate a novel problem called personalized trajectory matching (PTM). In contrast to conventional trajectory similarity search by spatial distance only, PTM takes into account the significance of each sample point in a query trajectory. A PTM query takes a trajectory with user-specified weights for each sample point in the trajectory as its argument. It returns the trajectory in an argument data set with the highest similarity to the query trajectory. We believe that this type of query may bring significant benefits to users in many popular applications such as route planning, carpooling, friend recommendation, traffic analysis, urban computing, and location-based services in general. PTM query processing faces two challenges: how to prune the search space during the query processing and how to schedule multiple so-called expansion centers effectively. To address these challenges, a novel two-phase search algorithm is proposed that carefully selects a set of expansion centers from the query trajectory and exploits upper and lower bounds to prune the search space in the spatial and temporal domains. An efficiency study reveals that the algorithm explores the minimum search space in both domains. Second, a heuristic search strategy based on priority ranking is developed to schedule the multiple expansion centers, which can further prune the search space and enhance the query efficiency. The performance of the PTM query is studied in extensive experiments based on real and synthetic trajectory data sets. © 2013 Springer-Verlag Berlin Heidelberg.en
dc.description.sponsorshipThis research is partially supported by the Natural Science Foundation of China (Grant No. 61232006), the National 863 High-tech Program (Grant No. 2012AA011001), the Australian Research Council (Grants No. DP110103423 and No. DP120102829), and the European Union (Grant No. FP7-PEOPLE-2010-ITN-264994). The research was performed when C. S. Jensen was with Aarhus University. Part of Shuo Shang's work was done when he was a research assistant professor in Aalborg University.en
dc.publisherSpringer Science + Business Mediaen
dc.subjectEfficiencyen
dc.subjectOptimizationen
dc.subjectPersonalized trajectory matchingen
dc.subjectSpatial networksen
dc.subjectSpatiotemporal databasesen
dc.titlePersonalized trajectory matching in spatial networksen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalThe VLDB Journalen
dc.contributor.institutionDepartment of Software Engineering, China University of Petroleum-Beijing, Beijing, Chinaen
dc.contributor.institutionDepartment of Computer Science, Aalborg University, Aalborg, Denmarken
dc.contributor.institutionSchool of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australiaen
kaust.authorDing, Ruoguen
kaust.authorKalnis, Panosen
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