Abstract
This paper proposes a novel method for analyzing PC usage logs aiming to find working patterns and behaviors of employees at work. The logs we analyze are recorded at individual PCs for employees in a company, and include active window transitions. Our method consists of two levels of abstraction: (1) task summarization by HMM; (2) user behavior comparison by kernel principle component analysis based on a graph kernel. The experimental results show that our method reveals implicit user behavior at a high level of abstraction, and allows us to understand individual user behavior among groups, and over time.
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Saito, R., Kuboyama, T., Yamakawa, Y., Yasuda, H. (2011). Understanding User Behavior through Summarization of Window Transition Logs. In: Kikuchi, S., Madaan, A., Sachdeva, S., Bhalla, S. (eds) Databases in Networked Information Systems. DNIS 2011. Lecture Notes in Computer Science, vol 7108. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25731-5_14
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DOI: https://doi.org/10.1007/978-3-642-25731-5_14
Publisher Name: Springer, Berlin, Heidelberg
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