Abstract
Mobile context-aware systems gained huge popularity in recent years due to the rapid evolution of personal mobile devices. Nowadays smartphones are equipped with a variety of sensors that allow for on-line monitoring of user context and reasoning upon it. Contextual information in such systems is very dynamic. It changes rapidly and these changes may have impact on system behaviour. Although there are many machine learning methods like Markov models that allow to handle such dynamics, they do not provide intelligibility features that rule-based systems do. In this paper we propose an extension to XTT2 rule representation that allows for modelling dynamics of the mobile context-aware systems using rules and statistical analysis of historical data. This was achieved by introducing time-based operators to rule conditions and statistical operators to right hand side of the rules.
This work was funded by the National Science Centre, Poland as a part of the KnowMe project (reference number 2014/13/N/ST6/01786).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bobek, S., Nalepa, G.J., Ligęza, A., Adrian, W.T., Kaczor, K.: Mobile context-based framework for threat monitoring in urban environment with social threat monitor. Multimedia Tools and Applications (2014), http://dx.doi.org/10.1007/s11042-014-2060-9
Bobek, S., Nalepa, G.J.: Incomplete and uncertain data handling in context-aware rule-based systems with modified certainty factors algebra. In: Bikakis, A., Fodor, P., Roman, D. (eds.) RuleML 2014. LNCS, vol. 8620, pp. 157–167. Springer, Heidelberg (2014), http://dx.doi.org/10.1007/978-3-319-09870-8_11
Bobek, S., Porzycki, K., Nalepa, G.J.: Learning sensors usage patterns in mobile context-aware systems. In: Proceedings of the FedCSIS 2013 Conference, Krakow, pp. 993–998. IEEE (September 2013)
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)
Dey, A.K.: Providing architectural support for building context-aware applications. Ph.D. thesis, Atlanta, GA, USA (2000), aAI9994400
Domingo-Ferrer, J., Solanas, A.: A measure of variance for hierarchical nominal attributes. Inf. Sci. 178(24), 4644–4655 (2008), http://dx.doi.org/10.1016/j.ins.2008.08.003
Jaroucheh, Z., Liu, X., Smith, S.: Recognize contextual situation in pervasive environments using process mining techniques. J. Ambient Intelligence and Humanized Computing 2(1), 53–69 (2011)
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)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)
Kwon, O.B., Sadeh, N.: Applying case-based reasoning and multi-agent intelligent system to context-aware comparative shopping. Decis. Support Syst. 37(2), 199–213 (2004), http://dx.doi.org/10.1016/S0167-92360300007-1
Lee, J.S., Lee, J.C.: Context awareness by case-based reasoning in a music recommendation system. In: Ichikawa, H., Cho, W.-D., Satoh, I., Youn, H.Y. (eds.) UCS 2007. LNCS, vol. 4836, pp. 45–58. Springer, Heidelberg (2007), http://dblp.uni-trier.de/db/conf/ucs/ucs2007.html#LeeL07
Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Learning and inferring transportation routines. Artif. Intell. 171(5-6), 311–331 (2007)
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)
Lim, B.Y., Dey, A.K., Avrahami, D.: Why and why not explanations improve the intelligibility of context-aware intelligent systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2009, pp. 2119–2128. ACM, New York (2009), http://doi.acm.org/10.1145/1518701.1519023
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)
Nalepa, G.J., Bobek, S., Ligęza, A., Kaczor, K.: HalVA - rule analysis framework for XTT2 rules. In: Bassiliades, N., Governatori, G., Paschke, A. (eds.) RuleML 2011 - Europe. LNCS, vol. 6826, pp. 337–344. Springer, Heidelberg (2011), http://www.springerlink.com/content/c276374nh9682jm6/
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)
Nalepa, G.J., Kluza, K.: UML representation for rule-based application models with XTT2-based business rules. International Journal of Software Engineering and Knowledge Engineering (IJSEKE) 22(4), 485–524 (2012), http://www.worldscientific.com/doi/abs/10.1142/S021819401250012X
Nalepa, G.J., Ligęza, A.: Designing reliable Web security systems using rule-based systems approach. In: Menasalvas, E., Segovia, J., Szczepaniak, P.S. (eds.) AWIC 2003. LNCS (LNAI), vol. 2663, pp. 124–133. Springer, Heidelberg (2003)
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)
Palmer, N., Kemp, R., Kielmann, T., Bal, H.: Swan-song: A flexible context expression language for smartphones. In: Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones, PhoneSense 2012, pp. 12:1–12:5. ACM, New York (2012), http://doi.acm.org/10.1145/2389148.2389160 , doi:10.1145/2389148.2389160
Petersen, A.K.: Challenges in Case-Based Reasoning for Context Awareness in Ambient Intelligent Systems. In: Minor, M. (ed.) 8th European Conference on Case-Based Reasoning, Workshop Proceedings, pp. 287–299. Ölüdeniz/Fethiye, Turkey (2006)
Rabiner, L., Juang, B.H.: An introduction to hidden markov models. IEEE ASSP Magazine 3(1), 4–16 (1986)
Shehzad, A., Ngo, H.Q., Pham, K.A., Lee, S.Y.: Formal modeling in context aware systems. In: Proceedings of the 1st International Workshop on Modeling and Retrieval of Context, MRC 2004 (2004)
Bobek, S., Nalepa, G.J., Ślażyński, M.: Challenges for migration of rule-based reasoning engine to a mobile platform. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2014. CCIS, vol. 429, pp. 43–57. Springer, Heidelberg (2014)
Want, R., Falcao, V., Gibbons, J.: The active badge location system. ACM Transactions on Information Systems 10, 91–102 (1992)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Bobek, S., Ślażyński, M., Nalepa, G.J. (2015). Capturing Dynamics of Mobile Context-Aware Systems with Rules and Statistical Analysis of Historical Data. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9120. Springer, Cham. https://doi.org/10.1007/978-3-319-19369-4_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-19369-4_51
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19368-7
Online ISBN: 978-3-319-19369-4
eBook Packages: Computer ScienceComputer Science (R0)