Context- and Situation-Awareness in Information Logistics

  • Ulrich Meissen
  • Stefan Pfennigschmidt
  • Agnès Voisard
  • Tjark Wahnfried
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3268)


In order to deliver relevant information at the right time to its mobile users, systems such as event notification systems need to be aware of the users’ context, which includes the current time, their location, or the devices they use. Many context frameworks have been introduced in the past few years. However, they usually do not consider the notion of characteristic features of contexts that are invariant during certain time intervals. Knowing the current situation of a user allows the system to better target the information to be delivered. This paper presents a model to handle various contexts and situations in information logistics. A context is defined as a collection of values usually observed by sensors, eg., location or temperature. A situation builds on this concept by introducing semantical aspects defined in an ontology. Our situation awareness proposal has been tested in two projects.


Context Variable Situation Awareness Situation Model Typical Situation Context Data 
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.


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  1. 1.
    Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, p. 304. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Akman, V., Surav, M.: The use of situation theory in context modeling. Computational Intelligence 13(3), 427–438 (1997)CrossRefGoogle Scholar
  3. 3.
    Cooper, R., Mukai, K., Perry, J. (eds.): Situation Theory and its applications. vol. I: CSLI Lecture Notes No. 22. CSLI: Center for the Study of Language and Information, Stanford University, California (1990)Google Scholar
  4. 4.
    Deiters, W., Löffeler, T., Pfennigschmidt, S.: The information logistics approach toward user demand-driven information supply. In: Spinellis, D. (ed.) Cross-Media Service Delivery, pp. 37–48. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  5. 5.
    Dey, A.K.: Providing architectural support for building context-aware applications. PhD thesis, College of Computing, Georgia Institute of Technology (2000)Google Scholar
  6. 6.
    Gruber, T.R.: Towards principles for the design of ontologies used for knowledge sharing. In: Guarino, N., Poli, R. (eds.) Formal Ontology in Conceptual Analysis and Knowledge Representation. Kluwer Academic Publishers, Dordrecht (1993)Google Scholar
  7. 7.
    Gruninger, M., Lee, J.: Ontology applications and design. Communications of the ACM 45(2), 39–41 (2002)CrossRefGoogle Scholar
  8. 8.
    Haseloff, S.: Context gathering – an enabler for information logistics. In: Chamoni, P., Deiters, W., Gronau, N., Kutsche, R.-D., Loos, P., Müller-Merbach, H., Rieger, B., Sandkuhl, K. (eds.) Multikonferenz Wirtschaftsinformatik (MKWI) 2004, Band 2, pp. 204–216. Akademische Verlagsgesellschaft, Berlin (2004)Google Scholar
  9. 9.
    Hinze, A.: An Adaptive Integrating Event Notification Service. PhD thesis, Free University of Berlin, Computer Science Institute (2003)Google Scholar
  10. 10.
    Holtkamp, B., Gartmann, R., Han, Y.: Flame 2008: Personalized web services for the Olympic Games 2008 in Beijing. In: e-2003, e-Challenges Workshop (2003)Google Scholar
  11. 11.
    Lespérance, Y., Levesque, H.J., Lin, F., Marcu, D., Reiter, R., Scherl, R.B.: A logical approach to high level robot programming – a progress report. In: Kuipers, B. (ed.) Working notes of the 1994 AAAI fall symposium on Control of the Physical World by Intelligent Systems, New Orleans, LA (1994)Google Scholar
  12. 12.
    McCarthy, J., Hayes, P.J.: Some philosophical problems from the standpoint of artificial intelligence. Report Memo AI-73, Department of Computer Science, Stanford University, Stanford, California (1968)Google Scholar
  13. 13.
    Meissen, U., Pfennigschmidt, S., Sandkuhl, K., Wahnfried, T.: Situation-based message rating in information logistics and its applicability in collaboration scenarios. In: Proceedings of the 30th Conference on EUROMICRO. IEEE Computer Society Press, Los Alamitos (to appear, 2004)Google Scholar
  14. 14.
    Petrelli, D., Not, E., Zancanaro, M., Strapparava, C., Stock, O.: Modelling and adapting to context. Personal Ubiquitous Computing 5(1), 20–24 (2001)CrossRefGoogle Scholar
  15. 15.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Mellish, C. (ed.) Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 448–453. Morgan Kaufmann, San Francisco (1995)Google Scholar
  16. 16.
    Schiller, J., Voisard, A. (eds.): Location-based Services. Morgan Kaufmann, San Francisco (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ulrich Meissen
    • 1
  • Stefan Pfennigschmidt
    • 1
  • Agnès Voisard
    • 1
  • Tjark Wahnfried
    • 1
  1. 1.Fraunhofer Institute for Software and Systems Engineering (ISST)BerlinGermany

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