A Streaming Data Prediction Method Based on Evolving Bayesian Network

In the Big Data era, large volumes of data are continuously and rapidly generated from sensor networks, social network, the Internet, etc. Learning knowledge from streaming Big Data is an important task since it can support online decision making. Prediction is one of the useful learning task but a fixed model usually does not work well because of the data distribution change over time. In this paper, we propose a streaming data prediction method based on evolving Bayesian network. The Bayesian network model is inferred based on Gaussian mixture model and EM algorithm. To support evolving model structure and parameters based on streaming data, an evolving hill-climbing algorithm is proposed which is based on incremental calculation of score metric when new data is arrived. The experimental evaluations show that this method is effective and it outperforms other popular methods for streaming data prediction.