User behaviour modelling by abstracting low-level window transition logs
Ryohei Saito, Tetsuji Kuboyama, Hiroshi Yasuda · International Journal of Computational Science and Engineering · 2015
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.