A Frequent Sequential Pattern Based Approach for Discovering Event Correlations
In an IoT environment, event correlation becomes more complex as events usually span over many interrelated sensors. This paper refines event correlations in an IoT environment, and proposes an algorithm to discover event correlations. We transform the event correlation discovery problem into a time-constrained frequent sequence mining problem. Moreover, we apply our approach in anomaly warning in a coal power plant. We have made extensive experiments to verify the effectiveness of our approach.
KeywordsEvent correlation Sensor event Anomaly warning
National Key R&D Plan (No. 2017YFC0804406); National Natural Science Foundation of China (No. 61672042); The Program for Youth Backbone Individual, supported by Beijing Municipal Party Committee Organization Department, “Research of Instant Fusion of Multi-Source and Large-Scale Sensor Data”.
- 2.Cheng, L., Van Dongen, B.F., Van Der Aalst, W.M.P.: Efficient event correlation over distributed systems. In: 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 1–10. Institute of Electrical and Electronics Engineers Inc., Madrid (2017)Google Scholar
- 3.Pourmirza, S., Dijkman, R., Grefen, P.: Correlation mining: mining process orchestrations without case identifiers. In: Barros, A., Grigori, D., Narendra, N.C., Dam, H.K. (eds.) ICSOC 2015. LNCS, vol. 9435, pp. 237–252. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48616-0_15CrossRefGoogle Scholar
- 8.Forkan, A.R.M., Khalil, I.: A probabilistic model for early prediction of abnormal clinical events using vital sign correlations in home-based monitoring. In: 14th IEEE International Conference on Pervasive Computing and Communications, pp. 1–9. Institute of Electrical and Electronics Engineers Inc., Sydney (2016)Google Scholar
- 9.Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11. Association for Computing Machinery, San Diego (2003)Google Scholar