Understanding User Behavior through Summarization of Window Transition Logs

  • Ryohei Saito
  • Tetsuji Kuboyama
  • Yuta Yamakawa
  • Hiroshi Yasuda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7108)


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.


user behavior analysis PC usage patterns pattern extraction hidden Markov model graph kernel kernel principal component analysis 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ryohei Saito
    • 1
    • 3
  • Tetsuji Kuboyama
    • 2
  • Yuta Yamakawa
    • 1
    • 3
  • Hiroshi Yasuda
    • 3
  1. 1.Hummingh Heads, Inc.TokyoJapan
  2. 2.Gakushuin UniversityTokyoJapan
  3. 3.Tokyo Denki UniversityTokyoJapan

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