دانلود رایگان مقاله انگلیسی پردازش رویداد پیچیده پیش بینی بر اساس شبکه های بیزی تکامل یافته - الزویر 2018

ABSTRACT

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.

6. Conclusion and Future Work

In this paper the authors propose a predictive complex event processing method based on evolving Bayesian networks. The Bayesian model has two dimensions: event type and time. The Gaussian mixture model and EM algorithm are used as approximate method to infer the Bayesian model. This method supports calculating score metric incrementally. Bayesian network structure and parameters evolving algorithms are proposed. The evaluation results in road traffic domain with both real application data and simulated data shows that this method is effective for predictive complex event processing and it outperforms other popular methods when processing traffic prediction in intelligent transportation systems. This method can also be used in many other areas, especially in proactive event processing systems which support executing some actions to avoid unwanted states.

The performance of the BN structure evolving method still needs to be improved. In the future, the authors will try to use some heuristic methods in the search algorithm. The authors will also consider using parallel searching to improve the performance. The performance of the parameter updating algorithm is also need to be improved.