Predictive complex event processing based on evolving Bayesian networks

In the Big Data era, large volumes of data are continuously and rapidly generated from sensor networks, social network, the Internet, etc. Predicting from online event stream is an important task since users usually need to predict some future states and take some actions in advance. Many applications need online prediction models which can evolve automatically with data distribution drift and algorithms which can support single-pass processing of data, which are still faced with many challenges. In this paper, the authors propose a predictive complex event processing method based on evolving Bayesian networks. The Bayesian model is designed based on event type and time with inference method based on Gaussian mixture model and EM algorithm. When learning the structure of Bayesian network from event streams, this method supports calculating score metric incrementally when new data is arrived or edges in the network are changed. Evolving Bayesian network structure is supported based on hill-climbing method. The system can continuously monitor the Bayesian network model and modify it if it is found to be not appropriate for the new incoming data. The method of this paper is evaluated in road traffic domain with both real application data and data produced by a simulated transportation system. The total percentage error is 8.12% for real data and 7.78% for simulated data, while the best result for other methods is 11.79% for real data and 14.59% for simulated data. The experimental evaluations show that this method is effective for predictive complex event processing and it outperforms other popular methods when processing traffic prediction in intelligent transportation systems.