Mining the most influential k-location set from massive trajectories
Li, Yuhong1; Bao, Jie2; Li, Yanhua3; Wu, Yingcai4; Gong, Zhiguo1; Zheng, Yu2,5,6
Conference Name24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
Source PublicationGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
Conference Date10 31, 2016 - 11 3, 2016
Conference PlaceBurlingame, CA, United states
Author of SourceAssociation for Computing Machinery

Mining the most influential k-location set finds k locations, traversed by the maximum number of unique trajectories, in a given spatial region. These influential locations are valuable for resource allocation applications, such as selecting charging stations for electric automobiles and suggesting locations for placing billboards. This problem is NP-hard and usually calls for an interactive mining processes, e.g., changing the spatial region and k, or removing some locations (from the results in the previous round) that are not eligible for an application according to the domain knowledge. Thus, efficiency is the major concern in addressing this problem. In this paper, we propose a system by using greedy heuristics to expedite the mining process. The greedy heuristic is efficient with performance guarantee. We evaluate the performance of our proposed system based on a taxi dataset of Tianjin, and provide a case study on selecting the locations for charging stations in Beijing. © 2016 ACM.

Indexed BySCI
WOS Research AreaComputer Science ; Remote Sensing
WOS SubjectComputer Science, Information Systems ; Remote Sensing
WOS IDWOS:000403647900051
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Affiliation1.University of Macau, China;
2.Microsoft Research, Beijing, China;
3.Worcester Polytechnic Institute, MA, United States;
4.State Key Lab of CAD and CG, Zhejiang University, Zhejiang, China;
5.Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China;
6.School of Computer Science and Technology, Xidian University, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Li, Yuhong,Bao, Jie,Li, Yanhua,et al. Mining the most influential k-location set from massive trajectories[C]//Association for Computing Machinery,2016.
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