A framework of loose travelling companion discovery from human trajectories
Autor: | Xiaolong Xu, Elahe Naserian, Yuning Dong, Xinheng Wang |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Forcing (recursion theory)
Theoretical computer science Computer Networks and Communications Computer science Movement (music) Mobile computing 02 engineering and technology Electronic mail Variety (cybernetics) Social group 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Electrical and Electronic Engineering Cluster analysis Software Computer-networking |
ISSN: | 1536-1233 |
Popis: | Through the availability of location-acquisition devices, huge volumes of spatio-temporal data recording the movement of people is provided. Discovery of the group of people who travel together can provide valuable knowledge to a variety of critical applications. Existing studies on this topic mainly focus on the movement of vehicles or animals with forcing the group members to stay always connected. However, the movement of people is different; people might belong to the same main group while they contribute in various sub-groups during their movement. In this paper, we propose a group pattern called loose travelling companion pattern (LTCP), which allows the members of a group to contribute to various sub-groups as long as the community of members does not change during the movement and all of the members stay connected for a few time-slots. In addition, we propose weakly continuous loose travelling companion pattern (WCLTCP) to relax the continuous time constraint in LTCP. Finally, three algorithms have been developed to discover the proposed group patterns: (i) straightforward approach, (ii) smart-and-fast method, and (iii) and opportunistic algorithm. Through the extensive experimental evaluation on both real and experimental datasets, the efficiency and effectiveness of the proposed group discovery approaches are proven. |
Databáze: | OpenAIRE |
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