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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hilbert, D., Redmiles, D.: Extracting usability information from user interface events. ACM Computing Surveys (CSUR) 32(4), 384–421 (2000)
Karlson, A., Meyers, B., Jacobs, A., Johns, P., Kane, S.: Working overtime: Patterns of smartphone and pc usage in the day of an information worker. Pervasive Computing, 398–405 (2009)
Begole, J., Tang, J., Smith, R., Yankelovich, N.: Work rhythms: analyzing visualizations of awareness histories of distributed groups. In: Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, pp. 334–343. ACM (2002)
Beauvisage, T.: Computer usage in daily life. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems, pp. 575–584. ACM (2009)
Safer, I., Murphy, G.: Comparing episodic and semantic interfaces for task boundary identification. In: Proceedings of the 2007 Conference of the Center for Advanced Studies on Collaborative Research, pp. 229–243. ACM (2007)
Singh, N., Tomar, D., Roy, B.: An approach to understand the end user behavior through log analysis. International Journal of Computer Applications IJCA 5(11), 9–13 (2010)
Shen, J., Li, L., Dietterich, T.: Real-time detection of task switches of desktop users. In: Proc. of IJCAI, vol. 7, pp. 2868–2873 (2007)
Zaki, M.: Sequence mining in categorical domains: incorporating constraints. In: Proceedings of the Ninth International Conference on Information and Knowledge Management, pp. 422–429. ACM (2000)
Kiernan, J., Terzi, E.: Constructing comprehensive summaries of large event sequences. ACM Transactions on Knowledge Discovery from Data (TKDD)Â 3(4), 21 (2009)
Renaud, K., Gray, P.: Making sense of low-level usage data to understand user activities. In: Proceedings of the 2004 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT Research in Developing Countries, pp. 115–124. South African Institute for Computer Scientists and Information Technologists (2004)
Brdiczka, O., Su, N., Begole, J.: Temporal task footprinting: identifying routine tasks by their temporal patterns. In: Proceeding of the 14th International Conference on Intelligent user Interfaces, pp. 281–284. ACM (2010)
Ferreira, D., Zacarias, M., Malheiros, M., Ferreira, P.: Approaching Process Mining with Sequence Clustering: Experiments and Findings. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 360–374. Springer, Heidelberg (2007)
Wang, P., Wang, H., Liu, M., Wang, W.: An algorithmic approach to event summarization. In: Proceedings of the 2010 International Conference on Management of Data, pp. 183–194. ACM (2010)
Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Kashima, H., Tsuda, K., Inokuchi, A.: Marginalized kernels between labeled graphs. In: Proceedings of 20th International Conference on Machine Learning (ICML), pp. 321–328 (2003)
Schölkopf, B., Smola, A., Müller, K.: Kernel Principal Component Analysis. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 583–588. Springer, Heidelberg (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-25731-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25730-8
Online ISBN: 978-3-642-25731-5
eBook Packages: Computer ScienceComputer Science (R0)