Extracting places from traces of locations
Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots
Mining behavioral groups in large wireless LANs
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Eigenplaces: Segmenting Space through Digital Signatures
IEEE Pervasive Computing
Spatiotemporal analysis in virtual environments using eigenbehaviors
Proceedings of the 7th International Conference on Advances in Computer Entertainment Technology
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This paper presents the use of wireless usage data as a research tool for analyzing the routine structure of people. The patterns of wireless usage can infer the routine of student life in campus. In our experiments, we discover the student routine structure from the volume and time of the wireless usage. Without following an individual trace for any particular person, we use the volume and time of the whole accesses for particular time and location in a university campus. The analysis is based on the large wireless LANs, one-year log data of the city campus of Bangkok University (August 2011 - July 2012), and the experiment is focused on the wireless access points provided in important places of student activity such as canteens, classrooms, libraries. The resulting outputs are the location preference vectors and a new calendar based on student routine structure. The results can support the computational and comparative analysis of space through the lens of service management and enhance user-driven facilitates of the university campus.