Abstract
With the emergence of the Internet of Things and smart devices, smart homes are becoming more and more popular. The main goal of this study is to implement an event driven system in a smart home and to extract meaningful information from the raw data collected by the deployed sensors using Complex Event Processing (CEP). These high-level events can then be used by multiple smart home applications in particular situation identification. However, in real life scenarios, low-level events are generally uncertain. In fact, an event may be outdated, inaccurate, imprecise or in contradiction with another one. This can lead to misinterpretation from CEP and the associated applications. To overcome these weaknesses, in this paper, we propose a Fuzzy Semantic Complex Event Processing (FSCEP) model which can represent and reason with events by including domain knowledge and integrating fuzzy logic. It handles multiple dimensions of uncertainty, namely freshness, accuracy, precision and contradiction. FSCEP has been implemented and compared with a well known CEP. The results show how some ambiguities are solved.
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References
Anicic, D., Rudolph, S., Fodor, P., Stojanovic, N.: Stream reasoning and complex event processing in etalis. Semant. Web 3(4), 397–407 (2012)
Artikis, A., Etzion, O., Feldman, Z., Fournier, F.: Event processing under uncertainty. In: Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems, pp. 32–43. ACM (2012)
Brenna, L., Demers, A., Gehrke, J., Hong, M., Ossher, J., Panda, B., Riedewald, M., Thatte, M., White, W.: Cayuga: a high-performance event processing engine. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 1100–1102. ACM (2007)
Cugola, G., Margara, A., Matteucci, M., Tamburrelli, G.: Introducing uncertainty in complex event processing: model, implementation, and validation. Computing 97(2), 103–144 (2015)
DAniello, G., Loia, V., Orciuoli, F.: A multi-agent fuzzy consensus model ina situation awareness framework. Appl. Soft Comput. 30, 430–440 (2015)
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook, Theory, Implementation, and Applications. The DescriptionLogic Handbook, 2nd edn. Cambridge University Press, Cambridge (2010)
Event stream intelligence, E.: Espercomplex event processing (2010)
Lee, O.J., Jung, J.E.: Sequence clustering-based automated rule generation for adaptive complex event processing. Future Gener. Comput. Syst. 66, 100–109 (2016)
Morrell, J., Vidich, S.: Complex event processing with coral8 (2007)
Rodríguez, N.D., Cuéllar, M.P., Lilius, J., Calvo-Flores, M.D.: A fuzzy ontology for semantic modelling and recognition of human behaviour. Knowl. Based Syst. 66, 46–60 (2014)
StreamBase, I.: Streambase: Real-time, low latency data processing with a stream processing engine (2006)
Wang, F., Liu, S., Liu, P., Bai, Y.: Bridging physical and virtual worlds: complex event processing for RFID data streams. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Boehm, K., Kemper, A., Grust, T., Boehm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 588–607. Springer, Heidelberg (2006). doi:10.1007/11687238_36
Wasserkrug, S., Gal, A., Etzion, O.: A model for reasoning with uncertain rules in event composition systems. arXiv preprint (2012). arXiv:1207.1427
Wu, E., Diao, Y., Rizvi, S.: High-performance complex event processing over streams. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, pp. 407–418. ACM (2006)
Ye, J., Dobson, S., McKeever, S.: Situation identification techniques in pervasive computing: A review. Pervasive Mobile Comput. 8(1), 36–66 (2012)
Zhang, H., Diao, Y., Immerman, N.: On complexity and optimization of expensive queries in complex event processing. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 217–228. ACM (2014)
Zhou, Q., Simmhan, Y., Prasanna, V.: Scepter: Semantic complex event processing over end-to-end data flows. Technical Report 12–926. Computer Science Department, University of Southern California (2012)
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Jarraya, A., Ramoly, N., Bouzeghoub, A., Arour, K., Borgi, A., Finance, B. (2016). FSCEP: A New Model for Context Perception in Smart Homes. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_28
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DOI: https://doi.org/10.1007/978-3-319-48472-3_28
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