Authors: Ryohei Saito; Tetsuji Kuboyama; Hiroshi Yasuda
Addresses: Humming Heads, Inc., 1-2-13 Tukishima, Chuo-ku, Tokyo, Japan ' Gakushuin University, 1-5-1 Mejiro, Toshima-ku, Tokyo, Japan ' Tokyo Denki University, 5 Senju Asahi-cho, Adachi-ku, Tokyo, Japan
Abstract: This paper proposes a novel framework for modelling user behaviour from low-level computer usage logs aiming to find working patterns and behaviours of employees at work. The logs we analyse are recorded in individual computers for employees in a company, and include active window transitions on display. Our framework consists of three levels of abstraction: 1) modelling user behaviour patterns by hidden Markov models; 2) clustering user behaviour models by kernel principal component analysis with a graph kernel; 3) extracting common patterns from clusters. The experimental results show that our method reveals implicit user behaviour at a high level of abstraction, and allows us to understand individual user behaviour among groups, and over time.
Keywords: user behaviour; behaviour modelling; computer usage patterns; hidden Markov models; HMM; graph kernels; kernel PCA; principal component analysis; log analysis; window transition logs; employee behaviour; clustering.
International Journal of Computational Science and Engineering, 2015 Vol.11 No.3, pp.249 - 258
Available online: 23 Oct 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article