Advertisement

Extraction and Analysis of Knowledge Worker Activities on Intranet

  • Peter Géczy
  • Noriaki Izumi
  • Shotaro Akaho
  • Kôiti Hasida
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4333)

Abstract

Knowledge regarding user browsing behavior on corporate Intranet may shed light on general behavioral principles of users in Intranet spaces, and assist organizations in making more informed decisions involving management, design, and use policies of Intranet resources. The study examines extraction and analysis of knowledge worker browsing behavior from WEB log data. Extraction of navigational primitives enabled us to identify common behavioral features of knowledge workers. Knowledge workers had a significant tendency to form behavioral patterns that were frequently repeated in Intranet environment. As they familiarized with the environment their navigation habituated.

Keywords

Knowledge Worker Average Session Large Data Volume Page Transition Unique Subsequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Schlender, B.: Peter Drucker sets us straight. Fortune (December 29, 2003), http://www.fortune.com
  2. 2.
    Davenport, T.H.: Thinking for a Living - How to Get Better Performance and Results from Knowledge Workers. Harvard Business School Press, Boston (2005)Google Scholar
  3. 3.
    Barabasi, A.L.: The origin of bursts and heavy tails in human dynamics. Nature 435, 207–211 (2005)CrossRefGoogle Scholar
  4. 4.
    Park, Y.-H., Fader, P.S.: Modeling browsing behavior at multiple websites. Marketing Science 23, 280–303 (2004)CrossRefGoogle Scholar
  5. 5.
    Moe, W.W.: Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream. Journal of Consumer Psychology 13, 29–39 (2003)Google Scholar
  6. 6.
    Bucklin, R.E., Sismeiro, C.: A model of web site browsing behavior estimated on clickstream data. Journal of Marketing Research 40, 249–267 (2003)CrossRefGoogle Scholar
  7. 7.
    Deshpande, M., Karypis, G.: Selective markov models for predicting web page accesses. ACM Transactions on Internet Technology 4, 163–184 (2004)CrossRefGoogle Scholar
  8. 8.
    Zukerman, I., Albrecht, D.W.: Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction 11, 5–18 (2001)MATHCrossRefGoogle Scholar
  9. 9.
    Jozefowska, J., Lawrynowicz, A., Lukaszewski, T.: Faster frequent pattern mining from the semantic web. In: Intelligent Information Processing and Web Mining, Advances in Soft Computing, pp. 121–130 (2006)Google Scholar
  10. 10.
    Thakor, M.V., Borsuk, W., Kalamas, M.: Hotlists and web browsing behavior–an empirical investigation. Journal of Business Research 57, 776–786 (2004)CrossRefGoogle Scholar
  11. 11.
    Catledge, L., Pitkow, J.: Characterizing browsing strategies in the world wide web. Computer Networks and ISDN Systems 27, 1065–1073 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Peter Géczy
    • 1
  • Noriaki Izumi
    • 1
  • Shotaro Akaho
    • 1
  • Kôiti Hasida
    • 1
  1. 1.National Institute of Advanced Industrial Science and Technology (AIST)Tsukuba and TokyoJapan

Personalised recommendations