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A Proactive Decision Support System for Online Event Streams

Author

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  • Yongheng Wang

    (College of Information Science and Electronic Engineering, Hunan University, Changsha 410082, China)

  • Xiaozan Zhang

    (College of Information Science and Electronic Engineering, Hunan University, Changsha 410082, China)

  • Zengwang Wang

    (College of Information Science and Electronic Engineering, Hunan University, Changsha 410082, China)

Abstract

In-stream big data processing is an important part of big data processing. Proactive decision support systems can predict future system states and execute some actions to avoid unwanted states. In this paper, we propose a proactive decision support system for online event streams. Based on Complex Event Processing (CEP) technology, this method uses structure varying dynamic Bayesian network to predict future events and system states. Different Bayesian network structures are learned and used according to different event context. A networked distributed Markov decision processes model with predicting states is proposed as sequential decision making model. A Q-learning method is investigated for this model to find optimal joint policy. The experimental evaluations show that this method works well for congestion control in transportation system.

Suggested Citation

  • Yongheng Wang & Xiaozan Zhang & Zengwang Wang, 2018. "A Proactive Decision Support System for Online Event Streams," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1891-1913, November.
    • Handle: RePEc:wsi:ijitdm:v:17:y:2018:i:06:n:s0219622018500463
    DOI: 10.1142/S0219622018500463
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    References listed on IDEAS

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    1. Dehua Shen & Yongjie Zhang & Xiong Xiong & Wei Zhang, 2017. "Baidu index and predictability of Chinese stock returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-8, December.
    2. Michael H. Veatch, 2013. "Approximate Linear Programming for Average Cost MDPs," Mathematics of Operations Research, INFORMS, vol. 38(3), pages 535-544, August.
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