VID join: mapping trajectories to points of interest to support location-based services

Shang, Shuo, Xie, Kexin, Zheng, Kai, Liu, Jiajun and Wen, Ji-Rong (2015) VID join: mapping trajectories to points of interest to support location-based services. Journal of Computer Science and Technology, 30 4: 725-744. doi:10.1007/s11390-015-1557-7

Author Shang, Shuo
Xie, Kexin
Zheng, Kai
Liu, Jiajun
Wen, Ji-Rong
Title VID join: mapping trajectories to points of interest to support location-based services
Journal name Journal of Computer Science and Technology   Check publisher's open access policy
ISSN 1000-9000
Publication date 2015-07
Sub-type Article (original research)
DOI 10.1007/s11390-015-1557-7
Volume 30
Issue 4
Start page 725
End page 744
Total pages 20
Place of publication New York, NY, United States
Publisher Springer New York
Collection year 2016
Language eng
Formatted abstract
Variable influence duration (VID) join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects (e.g., travelers), and spatial points are points of interest (POIs, e.g., restaurants). VID join returns all pairs of (τ s , p) if τ s is spatially close to p for a long period of time, where τ s is a segment of trajectory τT and pP. Each returned (τ s , p) implies that the moving object associated with τ s stayed at p (e.g., having dinner at a restaurant). Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join efficiently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data.
Keyword Trajectory
Spatial database
Spatial join
Spatio-temporal join
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
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