Incomplete and Uncertain Data Handling in Context-Aware Rule-Based Systems with Modified Certainty Factors Algebra

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8620)


Context-aware systems make use of contextual information to adapt their functionality to current environment state, or user needs and habits. One of the major problems concerning them is the fact, that there is no warranty that the contextual information will be available, nor certain at the time when the reasoning should be performed. This may be due to measurement errors, sensor inaccuracy, or semantic ambiguities of modeled concepts. Several approaches were developed to solve uncertainty in context knowledge bases, including probabilistic reasoning, fuzzy logic, or certainty factors. However, handling uncertainties in highly dynamic, mobile environments still requires more consideration. In this paper we perform comparison of application of different uncertainty modeling approaches to mobile context-aware environments. We also present an exemplary solution based on modified certainty factors algebra and logic-based knowledge representation for solving uncertainties caused by the imprecision of context-providers.


context-awareness mobile devices knowledge management uncertainty 


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  1. 1.
    Almeida, A., Lopez-de Ipina, D.: Assessing ambiguity of context data in intelligent environments: Towards a more reliable context managing systems. Sensors 12(4), 4934–4951 (2012)CrossRefGoogle Scholar
  2. 2.
    Benerecetti, M., Bouquet, P., Bonifacio, M., Italia, A.A.: Distributed context-aware systems (2001)Google Scholar
  3. 3.
    Bobek, S., Porzycki, K., Nalepa, G.J.: Learning sensors usage patterns in mobile context-aware systems. In: Proceedings of the FedCSIS 2013 Conference, pp. 993–998. IEEE, Krakow (2013)Google Scholar
  4. 4.
    Buchanan, B.G., Shortliffe, E.H.: Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project. The Addison-Wesley Series in Artificial Intelligence. Addison-Wesley Longman Publishing Co., Inc, Boston (1984)Google Scholar
  5. 5.
    Bui, H.H., Venkatesh, S., West, G.: Tracking and surveillance in wide-area spatial environments using the abstract hidden markov model. Intl. J. of Pattern Rec. and AI 15 (2001)Google Scholar
  6. 6.
    Chen, H., Finin, T.W., Joshi, A.: Semantic web in the context broker architecture. In: PerCom, pp. 277–286. IEEE Computer Society (2004)Google Scholar
  7. 7.
    Dey, A.K., Mankoff, J.: Designing mediation for context-aware applications. ACM Trans. Comput.-Hum. Interact. 12(1), 53–80 (2005)CrossRefGoogle Scholar
  8. 8.
    Fenza, G., Furno, D., Loia, V.: Hybrid approach for context-aware service discovery in healthcare domain. J. Comput. Syst. Sci. 78(4), 1232–1247 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hao, Q., Lu, T.: Context modeling and reasoning based on certainty factor. In: Asia-Pacific Conference on Computational Intelligence and Industrial Applications, PACIIA 2009, vol. 2, pp. 38–41 (November 2009)Google Scholar
  10. 10.
    Heckerman, D.: Probabilistic interpretations for mycin’s certainty factors. In: Proceedings of the First Conference Annual Conference on Uncertainty in Artificial Intelligence, UAI 1985, pp. 9–20. AUAI Press, Corvallis (1985)Google Scholar
  11. 11.
    Hu, H.: ContextTorrent: A Context Provisioning Framewrok for Pervasive Applications. University of Hong Kong (2011)Google Scholar
  12. 12.
    van Kasteren, T., Kröse, B.: Bayesian activity recognition in residence for elders. In: 3rd IET International Conference on Intelligent Environments, IE 2007, pp. 209–212 (2007)Google Scholar
  13. 13.
    Kjaer, K.E.: A survey of context-aware middleware. In: Proceedings of the 25th Conference on IASTED International Multi-Conference: Software Engineering, SE 2007, pp. 148–155. ACTA Press (2007)Google Scholar
  14. 14.
    Krause, A., Smailagic, A., Siewiorek, D.P.: Context-aware mobile computing: Learning context-dependent personal preferences from a wearable sensor array. IEEE Transactions on Mobile Computing 5(2), 113–127 (2006)CrossRefGoogle Scholar
  15. 15.
    Ligęza, A., Nalepa, G.J.: A study of methodological issues in design and development of rule-based systems: Proposal of a new approach. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1(2), 117–137 (2011)Google Scholar
  16. 16.
    Nalepa, G.J., Bobek, S., Ligęza, A., Kaczor, K.: Algorithms for rule inference in modularized rule bases. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) RuleML 2011 - Europe. LNCS, vol. 6826, pp. 305–312. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Nalepa, G.J., Bobek, S.: Rule-based solution for context-aware reasoning on mobile devices. Computer Science and Information Systems 11(1), 171–193 (2014)CrossRefGoogle Scholar
  18. 18.
    Nalepa, G.J., Ligęza, A., Kaczor, K.: Formalization and modeling of rules using the XTT2 method. International Journal on Artificial Intelligence Tools 20(6), 1107–1125 (2011)CrossRefGoogle Scholar
  19. 19.
    Niederliński, A.: rmes, Rule- and Model-Based Expert Systems. Jacek Skalmierski Computer Studio (2008)Google Scholar
  20. 20.
    Parsaye, K., Chignell, M.: Expert systems for experts / Kamran Parsaye, Mark Chignell. Wiley, New York (1988)Google Scholar
  21. 21.
    Parsons, S., Hunter, A.: A review of uncertainty handling formalisms. In: Hunter, A., Parsons, S. (eds.) Applications of Uncertainty Formalisms. LNCS (LNAI), vol. 1455, pp. 8–37. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  22. 22.
    Yuan, B., Herbert, J.: Fuzzy cara - a fuzzy-based context reasoning system for pervasive healthcare. Procedia CS 10, 357–365 (2012)Google Scholar

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

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