A Proactive Complex Event Processing Method Based on Parallel Markov Decision Processes

被引：0

作者：

Wang, Yongheng ^{[1
]}

Cao, Kening ^{[1
]}

机构：

[1] Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Hunan, Peoples R China

来源：

WEB-AGE INFORMATION MANAGEMENT, WAIM 2014 | 2014年 / 8485卷

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Large-scale Internet of Things (IoT) produces enormous events. The key issue in IoT application is how to process the events. In this paper a proactive complex event processing method using parallel Markov Decision Processes is proposed for large-scale IoT. Based on a multi-layered adaptive dynamic Bayesian model, an accurate predictive analytics method is proposed. A parallel Markov decision processes model is designed to support proactive event processing. A state partition method and a reward decomposition method are used to support large-scale application. The experimental evaluations show that this method has good accuracy and scalability when used to process complex event proactively in large-scale internet of things.

引用

页码：237 / 241

页数：5

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