Information Resource Recommendation in Knowledge Processes

  • Tadej Štajner
  • Dunja Mladenić
  • Marko Grobelnik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7117)

Abstract

This paper proposes a framework and an implementation for pro-active just-in-time information resource delivery, based on knowledge processes. We focus on providing a desktop application that presents a ranked list of information resources, such as documents, web sites or e-mail messages, that are considered to be most relevant in that point in time. The paper decomposes the recommendation problam into subproblems and provides evaluation on several event, action and process models. Results show that defining actions based on clustering of events yields the best recommendations.

Keywords

Process modeling Information delivery Clustering Process mining Just-in-time Information Retrieval 

References

  1. 1.
    Warren, P., Kings, N., Thurlow, I., Davies, J., Bürger, T., Simperl, E., Ruiz, C., Gomez-Perez, J., Ermolayev, V., Ghani, R., Tilly, M., Bösser, T., Imtiaz, A.: Improving knowledge worker productivity the ACTIVE approach. BT Technology Journal 26(2) (2009)Google Scholar
  2. 2.
    van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W(E.), Weijters, A.J.M.M.T., van der Aalst, W.M.P.: The ProM Framework: A New Era in Process Mining Tool Support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Holz, H., Maus, H., Bernardi, A., Rostanin, O.: From lightweight, proactive information delivery to business process-oriented knowledge management. Journal of Universal Knowledege Management 2, 101–127 (2005)Google Scholar
  4. 4.
    Lokaiczyk, R., Faatz, A., Beckhaus, A., Goertz, M.: Enhancing just-in-time e- Learning through Machine Learning on Desktop Context Sensors. In: Kokinov, B., Richardson, D.C., Roth-Berghofer, T.R., Vieu, L. (eds.) CONTEXT 2007. LNCS (LNAI), vol. 4635, pp. 330–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Rath, A.S., Devaurs, D., Lindstaedt, S.N.: Studying the Factors Influencing Automatic user Task Detection on the Computer Desktop. In: Wolpers, M., Kirschner, P.A., Scheffel, M., Lindstaedt, S., Dimitrova, V. (eds.) EC-TEL 2010. LNCS, vol. 6383, pp. 292–307. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Štajner, T., Mladenić, D.: Modeling Knowledge Worker Activity: Workshop on Applications of Pattern Analysis, Cumberland Lodge (2010) Google Scholar
  7. 7.
    Štajner, T., Mladenić, D., Grobelnik, M.: Exploring Contexts and Actions in Knowledge Processes. In: Proceedings of the 2nd Workshop on Context, Information and Ontologies (2010)Google Scholar
  8. 8.
    Grobelnik, M., Mladenić, D., Ferlež, J.: Probabilistic Temporal Process Model for Knowledge Processes: Handling a Stream of Linked Text. In: Proceedings of SiKDD 2009 Conference on Data Mining and Data Warehouses (2009)Google Scholar
  9. 9.
    Gomez-Perez, J., Grobelnik, M., Ruiz, C., Tilly, M., Warren, P.: Using task context to achieve effective information delivery. In: Proceedings of the 1st Workshop on Context, Information and Ontologies, pp. 1–6. ACM (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tadej Štajner
    • 1
  • Dunja Mladenić
    • 1
  • Marko Grobelnik
    • 1
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia

Personalised recommendations