Introducing Context-Aware Features into Everyday Mobile Applications

  • Mikko Perttunen
  • Jukka Riekki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3479)


We describe our approach of introducing context-awareness into everyday applications to make them more easy-to-use. The approach aims in shortening both the learning curve when introducing new technology to end-users and prototype development time, as well as results in more reliable prototypes. Moreover, we expect that the approach yields better quality user test results. To demonstrate the approach, we have employed context-based availability inference to automatically update the availability of IBM Lotus Sametime Everyplace users. This is likely to result in more reliable availability information and to make the application easier to use. Context inference is done using information from Lotus Notes Calendar and WLAN positioning technology.


Ubiquitous Computing Availability Information Context Recognition Cognitive Support Presence Information 
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.
    Schilit, B.N., Theimer, M.M.: Disseminating active map information to mobile hosts. IEEE Network 8(5), 22–32 (1994)CrossRefGoogle Scholar
  2. 2.
    Weiser, M., Brown, J.S.: The Coming Age of Calm Technology. In: Denning, P.J., Metcalfe, R.M. (eds.) Beyond Calculation: The Next Fifty Years of Computing, pp. 66–75. Springer, New York (1997)Google Scholar
  3. 3.
    Satyanarayanan, M.: Pervasive computing: vision and challenges. IEEE Pers. Commun. 8(4), 10–17 (2001)CrossRefGoogle Scholar
  4. 4.
    Abowd, G.D.: Software engineering issues for ubiquitous computing. In: Proceedings of the 21st international conference on Software engineering, Los Angeles, California, United States, pp. 75–84 (1999)Google Scholar
  5. 5.
    Walenstein, A.: Improving Adoptability by Preserving, Leveraging, and Adding Cognitive Support To Existing Tools and Environments. In: Proceedings of the 3rd International Workshop on Adoption-Centric Software Engineering, pp. 36–41.Google Scholar
  6. 6.
    Ma, J., Kienle, H.M., Kaminski, P., Weber, A., Litoiu, M.: Customizing lotus notes to build software engineering tools. In: Proceedings of the 2003 conference of the Centre for Advanced Studies conference on Collaborative research, Toronto, Ontario, Canada, pp. 211–222 (2003)Google Scholar
  7. 7.
    Raccoon, L.B.S.: A learning curve primer for software engineers. SIGSOFT Softw.Eng.Notes 21(1), 77–86 (1996)CrossRefGoogle Scholar
  8. 8.
    Walenstein, A.: Cognitive Support in Software Engineering Tools: A Distributed Cognition Framework. PhD thesis, School of Computing Science, Simon Fraser University (2002)Google Scholar
  9. 9.
    Dey, A., Abowd, G.D., Salber, D.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human-Computer Interaction 16, 97–166 (2001)CrossRefGoogle Scholar
  10. 10.
    Perttunen, M., Riekki, J.: Inferring presence in a context-aware instant messaging system. In: Aagesen, F.A., Anutariya, C., Wuwongse, V. (eds.) INTELLCOMM 2004. LNCS, vol. 3283, pp. 160–174. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Milewski, A.E., Smith, T.M.: Providing presence cues to telephone users. In: ACM 2000 Conference on Computer Supported Cooperative Work, Philadelphia, Pensylvania, USA, December 2-6, pp. 89–96 (2000)Google Scholar
  12. 12.
    Fogarty, J., Lai, J., Christensen, J.: Presence versus availability: The design and evaluation of a context-aware communication client. International Journal of Human Computer Studies 61(3), 299–317 (2004)CrossRefGoogle Scholar
  13. 13.
    Preece, J., Rogers, Y., Sharp, H.: Interaction Design. John Wiley & Sons, New York (2002)Google Scholar
  14. 14.
    Tamminen, S., Oulasvirta, A., Toiskallio, K., Kankainen, A.: Understanding mobile contexts. Personal Ubiquitous Comput. 8(2), 135–143 (2004)CrossRefGoogle Scholar
  15. 15.
    Pirttikangas, S., Riekki, J., Kaartinen, J., Röning, J.: Context-Recognition and Data Mining Methods for a Health Club Application. In: proceedings of 12th Int. Conf. Intelligent and Adaptive Systems and Software Engineering (IASSE 2003), San Francisco, California, USA, July 9-11, pp. 79–84 (2003)Google Scholar
  16. 16.
    Barkhuus, L.: Context Information vs. Sensor Information: A Model for Categorizing Context in Context-Aware Mobile Computing. In: Symposium on Collaborative Technologies and Systems,pp. 127–133 (2003)Google Scholar
  17. 17.
    Day, M., Rosenberg, J., Sugano, H.: RFC 2778, A Model for Presence And Instant Messaging. IETF (2000)Google Scholar
  18. 18.
    Begole, J., Matsakis, N.E., Tang, J.C.: Lilsys: inferring unavailability using sensors. In: Proceedings of the 2004 ACM conference on Computer supported cooperative work, Chicago, Illinois, USA, pp. 511–514 (2004)Google Scholar
  19. 19.
    Davidyuk, O., Riekki, J., Rautio, V.-., Sun, J.: Context-Aware Middleware for Mobile Multimedia Applications. In: Proc. 3rd International conference on Mobile and Ubiquitous multimedia (MUM 2004), College Park, Maryland, USA, October 27-29, pp. 213–220 (2004)Google Scholar
  20. 20.
    IBM Lotus Sametime 3.1 User GuideGoogle Scholar
  21. 21.
    White Paper: Introducing the Sametime Client Toolkits (2002)Google Scholar
  22. 22.
    Ekahau, Inc.,
  23. 23.
    Karsten, H.: Collaboration and collaborative information technologies: a review of the evidence. SIGMIS Database 30(2), 44–65 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Mikko Perttunen
    • 1
  • Jukka Riekki
    • 1
  1. 1.Department of Electrical and Information Engineering and Infotech OuluUniversity of OuluFinland

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