上下文敏感的复杂事件预测方法研究  

Research of Context-aware Prediction Method for Complex Event

作　　者：耿少峰 王永恒[1] 李仁发 张佳 宋秉华[3] 郭晓曦[2] GENG Shao-feng;WANG Yong-heng;LI Ren-fa;ZHANG Jia;SONG Bing-hua;GUO Xiao-xi(College of Information Science and Engineering,Hunan University,Changsha 410082,China;College of Computer Engineering,Jimei University,Xiamen 361021,China;College of Business Administration,Hunan University,Changsha 410082,China)

机构地区：[1]湖南大学信息科学与工程学院,长沙410082 [2]集美大学计算机工程学院,福建厦门361021 [3]湖南大学工商管理学院,长沙410082

出　　处：《小型微型计算机系统》2018年第8期1814-1818,共5页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(61371116;61672217)资助;福建省自然科学基金项目(2015J01264)资助

摘　　要：在智能交通系统流量预测的应用背景下,提出一种以复杂事件处理技术为基础的上下文敏感预测方法.首先采用模糊本体对历史事件上下文进行建模,再通过上下文聚类实现数据的划分,针对不同的数据学习对应的贝叶斯网络模型.实时预测时可根据当前事件上下文适应性选取贝叶斯网络模型或模型组合进行预测分析.实验结果显示该方法能有效的处理信息物理融合系统中事件数据流,并具有良好的预测性能.Taken traffic flow prediction in intelligent traffic system as application background,a context-aware prediction method for complex event was proposed. With fuzzy ontology to model the historical event context and context clustering to partition events,this method could learn corresponding Bayesian network models according to different data during complex event processing. Appropriate Bayesian network model or combination of Bayesian network models could be provided by this method for real-time prediction and a- nalysis of current context of events. The experimental result shows that this method can process events stream of cyber physical system effectively and has favorable prediction performance.

关 键 词：信息物理融合系统 大数据 复杂事件处理 贝叶斯网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]