Abstract
Internet of Things (IoT) is the new paradigm that connects the physical world with the virtual world. The interconnection is generated by the optimal deployment of sensors which continuously generate data and streams it to a data store. The concept drift and data drift are integral characteristics of IoT data. Due to this nature, there is a need to process data from various sources and decipher patterns in them. This process of detecting complex patterns in data is called Complex Event Processing which provides near real-time analytics for various IoT applications. Current CEP deployments have a inherent capability to react to events instantaneously. This leaves room to develop CEPs which are proactive in nature which can take the help of various machine learning (ML) models to work together with CEP. In this paper, the usage of Complex Event Processing (CEP) engine is exhibited that allows the inference of new scenarios out of incoming traffic data. This conversion of historical data into actionable knowledge is undertaken by a Long Short Term Memory (LSTM) model so as to detect the occurrence of an event well before time. The experimental results suggest the rich abilities of Deep Learning to predict events proactively with minimal error. This allows to deal with uncertainties and steps for significant improvement can be made in advance.
The authors would like to thank TCS Foundation for supporting the first author through PhD fellowship.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akbar, A., Khan, A., Carrez, F., Moessner, K.: Predictive analytics for complex IoT data streams. IEEE Internet Things J. 4(5), 1571–1582 (2017)
Brenna, L., et al.: 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.: Complex event processing with T-REX. J. Syst. Softw. 85(8), 1709–1728 (2012)
da Penha, O.S., Nakamura, E.F.: Fusing light and temperature data for fire detection. In: 2010 IEEE Symposium on Computers and Communications (ISCC), pp. 107–112. IEEE (2010)
Engel, Y., Etzion, O.: Towards proactive event-driven computing. In: Proceedings of the 5th ACM International Conference on Distributed Event-Based System, pp. 125–136. ACM (2011)
Etzion, O., Niblett, P., Luckham, D.C.: Event Processing in Action. Manning, Greenwich (2011)
Fülöp, L.J., Beszédes, Á., Tóth, G., Demeter, H., Vidács, L., Farkas, L.: Predictive complex event processing: a conceptual framework for combining complex event processing and predictive analytics. In: Proceedings of the Fifth Balkan Conference in Informatics, pp. 26–31. ACM (2012)
Gyllstrom, D., Wu, E., Chae, H.-J., Diao, Y., Stahlberg, P., Anderson, G.: SASE: complex event processing over streams. arXiv preprint cs/0612128 (2006)
Hofleitner, A., Herring, R., Abbeel, P., Bayen, A.: Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network. IEEE Trans. Intell. Transp. Syst. 13(4), 1679–1693 (2012)
Jie, Y., Pei, J.Y., Jun, L., Yun, G., Wei, X.: Smart home system based on IoT technologies. In: 2013 Fifth International Conference on Computational and Information Sciences (ICCIS), pp. 1789–1791. IEEE (2013)
Jin, X., Lee, X., Kong, N., Yan, B.: Efficient complex event processing over RFID data stream. In: Seventh IEEE/ACIS International Conference on Computer and Information Science, ICIS 2008, pp. 75–81. IEEE (2008)
Kanungo, A., Sharma, A., Singla, C.: Smart traffic lights switching and traffic density calculation using video processing. In: 2014 Recent Advances in Engineering and computational sciences (RAECS), pp. 1–6. IEEE (2014)
Li, J.Z.: A logical agent-based environment monitoring and control system. Master in Engineering Project Report (2011)
Li, Y., Wang, J., Feng, L., Xue, W.: Accelerating sequence event detection through condensed composition. In: 2010 Proceedings of the 5th International Conference on Ubiquitous Information Technologies and Applications (CUTE), pp. 1–6. IEEE (2010)
JDK Oracle. Disponível em. http://www.oracle.com/technetwork/java/javase/downloads/index.html. Acessado em, 8, 2010
Schultz-Moeller, N.P., Migliavacca, M., Pietzuch, P.: Distributed complex event processing with query optimisation. In: International Conference on Distributed Event-Based Systems (DEBS 2009), Nashville, TN, USA. ACM (2009)
Serra, J., Pubill, D., Antonopoulos, A., Verikoukis, C.: Smart HVAC control in IoT: energy consumption minimization with user comfort constraints. Sci. World J. 2014, 1–11 (2014)
Tommasini, R., Bonte, P., Della Valle, E., Mannens, E., De Turck, F., Ongenae, F.: Towards ontology-based event processing. In: Dragoni, M., Poveda-Villalón, M., Jimenez-Ruiz, E. (eds.) OWLED/ORE -2016. LNCS, vol. 10161, pp. 115–127. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54627-8_9
TongKe, F.: Smart agriculture based on cloud computing and IoT. J. Converg. Inf. Technol. 8(2), 210–216 (2013)
Tóth, G., Fülöp, L.J., Vidács, L., Beszédes, Á., Demeter, H., Farkas, L.: Complex event processing synergies with predictive analytics. In: Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems, pp. 95–96. ACM (2010)
Wang, D., Rundensteiner, E.A., Wang, H., Ellison III, R.T.: Active complex event processing: applications in real-time health care. Proc. VLDB Endow. 3(1–2), 1545–1548 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Mishra, S., Jain, M., Siva Naga Sasank, B., Hota, C. (2018). An Ingestion Based Analytics Framework for Complex Event Processing Engine in Internet of Things. In: Mondal, A., Gupta, H., Srivastava, J., Reddy, P., Somayajulu, D. (eds) Big Data Analytics. BDA 2018. Lecture Notes in Computer Science(), vol 11297. Springer, Cham. https://doi.org/10.1007/978-3-030-04780-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-04780-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04779-5
Online ISBN: 978-3-030-04780-1
eBook Packages: Computer ScienceComputer Science (R0)