摘要
为了有效分析事件流之间特定的行为关系并将其融入预测过程,提出一种基于事件流行为向量的在线事件流协同过滤推荐算法来预测下一个事件。首先分析事件流之间的结构相似性和行为相似性,以确定事件流的行为轮廓关系,在此基础上捕获事件流的行为依赖关系,将事件流构建为行为向量;然后调整传统的协同过滤推荐算法以分析在线事件流,对下一个事件流进行有效预测;最后,在Pm4py框架中实现相关算法,并在合成日志和真实日志中进行仿真预测。实验结果表明,行为向量能够体现事件流的行为关系,并提高预测的有效性。
Event stream prediction is the process of analyzing known process behavior to anticipate unknown states so that business activities are executed in the desired manner. The existing online event stream prediction methods mainly focus on the state of the event stream itself, and less considers the specific behavioral relationships between the event streams. To effectively analyze these behavioral relationships and incorporate them into the prediction process, an online event stream collaborative filtering recommendation algorithm based on event popularity as a vector was proposed to predict the next event. The structural similarity and behavioral similarity between event streams were first analyzed to determine the behavioral profile relationships of event streams. On this basis, the behavioral dependencies of the event streams were captured and the event streams were constructed into behavioral vectors. Then the traditional collaborative filtering recommendation algorithm was adapted to analyze the online event streams to achieve effective prediction of the next event stream. The relevant algorithms were implemented in the Pm4 py framework and simulated in synthetic logs and real logs for prediction. The experimental results showed that the behavior vectors could reflect the behavioral relationships of the event streams and improve the effectiveness of the prediction.
作者
卢可
方贤文
方娜
LU Ke;FANG Xianwen;FANG Na(School of Mathematics and Big Data,Anhui University of Science and Technology,Huainan 232001,China;The Key Laboratory of Embedded System and Service Computing,Ministry of Education,Tongji University,Shanghai 201804,China)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2022年第10期3052-3063,共12页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(61572035,61402011)
安徽省高校领军骨干人才资助项目(2020-1-12)
安徽省重点研究与开发计划资助项目(2022a05020005)
嵌入式系统与服务计算教育部重点实验开放课题资助项目(ESSCKF2018-04)
安徽省自然科学基金资助项目(2008085QD178)
安徽省学术和技术带头人资助项目(2019H239)。
关键词
事件流预测
行为向量编码
在线
协同过滤
业务流程监控
event stream prediction
behavioral vector encoding
online
collaborative filtering
business process monitor
