Abstract
For flow prediction in intelligent traffic system, one certain model cannot get excellent performance under different environments. Predicting models should also be updated according to data stream. In order to resolve these problems, a prediction method based on complex event processing was proposed. With fuzzy ontology to model historical event context and context clustering to partition events, this method could learn Bayesian network models according to different data during complex event processing. Appropriate Bayesian network model or combination of Bayesian network models could be provided by this method for real-time prediction and analysis of current context of events. The experimental result shows that this method can process events stream of Cyber Physical System (CPS) effectively and has favorable prediction performance.
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Acknowledgment
The work of this paper is sponsored by the National Natural Science Foundation of China (Grant No. 61371116) and Natural Science Foundation of Fujian Province (Grant No. 2015J01264).
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Geng, S., Guo, X., Zhang, J., Wang, Y., Li, R., Song, B. (2018). A Prediction Method Based on Complex Event Processing for Cyber Physical System. In: Zhu, L., Zhong, S. (eds) Mobile Ad-hoc and Sensor Networks. MSN 2017. Communications in Computer and Information Science, vol 747. Springer, Singapore. https://doi.org/10.1007/978-981-10-8890-2_20
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DOI: https://doi.org/10.1007/978-981-10-8890-2_20
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