摘要
预测性业务流程监控(PBPM)是业务流程管理(BPM)中的一个重要研究领域,旨在准确预测未来的行为事件。目前,PBPM研究中广泛引用了深度学习方法,但大多数方法只考虑单一的事件-控制流视角,无法将属性-数据流视角与之结合进行流程预测。针对这一问题,提出了一种基于双层BERT神经网络和融合流程多视角行为分析方法(简称FMP框架)。首先,基于第一层BERT学习属性-数据流信息;接着,基于第二层BERT学习事件-行为控制流信息;最后,通过FMP框架融合数据流和控制流实现多维视角流程预测。在真实的事件日志中的实验结果表明,相比其他研究方法,基于FPM框架预测下一个事件的活动精度更高。这证明融合流程多视角的FMP框架能够更全面、更深层次地分析复杂的流程行为,并提高预测的性能。
Predictive business process monitoring(PBPM)represents a vital research field within BPM that aims to accurately predict future behavioral events.At present,deep learning methods are widely used in PBPM research.However,most of these methods consider only a single event-control flow perspective and do not fuse the attribute-data flow perspective for process prediction.To address this issue,this paper proposed a method called the fusion multi-perspective(FMP)framework based on a two-layer BERT neural network.Firstly,the first layer of BERT was used to learn attribute-data flow information.Subsequently,the second layer of BERT learnt event-behavior control flow information.Finally,the FMP framework combined data flow and control flow to achieve multi-perspective process prediction.Experimental results on real event logs demonstrate that,compared to other research methods,the FPM framework yields higher accuracy in predicting the next event activity.This validates that the FPM framework,which merges multi-perspective views of processes,enables a more comprehensive and in-depth analysis of complex process behaviors while enhancing predictive performance.
作者
袁永旺
方贤文
卢可
Yuan Yongwang;Fang Xianwen;Lu Ke(School of Mathematics&Big Data,Anhui University of Science&Technology,Huainan Anhui 232001,China)
出处
《计算机应用研究》
CSCD
北大核心
2024年第6期1790-1796,共7页
Application Research of Computers
基金
国家自然科学基金资助项目(61572035)
安徽省重点研究与开发计划资助项目(2022a05020005)
安徽省自然科学基金资助项目(水科学联合基金2308085US11)。
关键词
业务流程管理
业务流程预测监控
深度学习
注意力机制
数据流视角
控制流视角
business process management(BPM)
business process prediction monitoring
deep learning
attention mechanism
data flow perspective
control flow perspective
