Advertisement

From Collaborative Scenario Recording to Smart Room Assistance Models

  • Gregor BuchholzEmail author
  • Peter Forbrig
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9189)

Abstract

There is still much to be done in implementing assistance systems for Intelligent Environments. Different approaches exist that aim at providing the user with useful and pleasant functionality. One group of methods uses behavioral models to derive supportive actions from the observation by sensors. This is a promising approach but creating such models is a laborious and error-prone task. Examples of the behavior of persons in intelligent environments and their interactions with the devices are a starting point for the (partial) generation of such models. In this paper we present an approach to record user behavior without the need of real users performing in the real environment. As a special thematic priority we will focus on the preparation phase of collaborative scenario recording and the used notation. Additionally, the paper will explain the generation of models from the recorded traces.

Keywords

Intelligent environments Ubiquitous computing Task models 

References

  1. 1.
    Armentano, M.G., Amandi, A.A.: Recognition of user intentions for interface agents with variable order Markov models. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 173–184. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Baader, F., Brandt, S., Lutz, C.: Pushing the EL envelope. In: Proceedings of the IJCAI 2005, pp. 364–369. Morgan Kaufmann, San Francisco (2005)Google Scholar
  3. 3.
    Barros, G.: Extending ActionSketch for new interaction styles: gestural interfaces and interactive environments. In: Marcus, A. (ed.) DUXU 2014, Part II. LNCS, vol. 8518, pp. 509–520. Springer, Heidelberg (2014)Google Scholar
  4. 4.
    Buchholz, G., Forbrig, P.: Combining design of models for smart environments with pattern-based extraction. In: Kurosu, M. (ed.) HCI 2014, Part I. LNCS, vol. 8510, pp. 285–294. Springer, Heidelberg (2014)Google Scholar
  5. 5.
    Ferilli, S., De Carolis, B., Redavid, D.: Logic-based incremental process mining in smart environments. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds.) IEA/AIE 2013. LNCS, vol. 7906, pp. 392–401. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Kiefer, P.: Mobile intention recognition. In: Kiefer, P. (ed.) Mobile intention recognition, pp. 11–53. Springer, New York (2012)CrossRefGoogle Scholar
  7. 7.
    Krämer, J., Seeger, B.: Semantics and implementation of continuous sliding window queries over data streams. ACM Trans. Database Syst. 34, 4:1–4:49 (2009)CrossRefGoogle Scholar
  8. 8.
    Maulsby, D.: Inductive task modeling for user interface customization. In: Proceedings of the IUI 1997, pp. 233–236. ACM, New York (1997)Google Scholar
  9. 9.
    Ramos, C., Marreiros, G., Santos, R., Freitas, C.F.: Smart offices and intelligent decision rooms. In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds.) Handbook of Ambient Intelligence and Smart Environments, pp. 851–880. Springer, New York (2010)CrossRefGoogle Scholar
  10. 10.
    Robles, R.J., Kim, T.-h.: Context aware systems, methods and trends in smart home technology. In: Kim, T.-h., Stoica, A., Chang, R.-S. (eds.) Security-Enriched Urban Computing and Smart Grid. Communications in Computer and Information Science, pp. 149–158. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Scalmato, A., Sgorbissa, A., Zaccaria, R.: Describing and recognizing patterns of events in smart environments with description logic. IEEE Trans. Cybern. 43, 1882–1897 (2013). IEEECrossRefGoogle Scholar
  12. 12.
    Seyff, N., Maiden, N., Karlsen, K., Lockerbie, J., Grünbacher, P., Graf, F., Ncube, C.: Exploring how to use scenarios to discover requirements. Requirements Eng. 14(2), 91–111 (2009). Springer, HeidelbergCrossRefGoogle Scholar
  13. 13.
    van der Aalst, W.M.: Process mining in the large: a tutorial. In: Zimányi, E. (ed.) eBISS 2013. LNBIP, vol. 172, pp. 33–76. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  14. 14.
    Wurdel, M., Sinnig, D., Forbrig, P.: CTML: Domain and task modeling for collaborative environments. J. Univ. Comput. Sci. 14(19), 3188–3201 (2008). (Special Issue on Human-Computer Interaction)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of RostockRostockGermany

Personalised recommendations