哈尔滨商业大学学报（自然科学版）2024，Vol.40Issue(2)：157-166,10.
基于行为轮廓的二维傅里叶变换流程预测
2D discrete Fourier transform process prediction based on behavior profiles
摘要
Abstract
Most of the existing predictive process monitoring focuses on deep learning techniques,and few combine the behavior in the process with this.In response to this issue,this article proposed a process prediction method based on two-dimensional discrete Fourier transform,which combined behavioral relationships to predict the next activity of the process.The method preprocessed the data,used data transformation engineering to convert the time data into two-dimensional spatial data including activity channels and performance channels.The method encoded the behavior relationships between activities and performed two-dimensional discrete Fourier transform on the obtained matrix.Finally,the method input it into the CNN network for training and prediction.This paper used simulation event logs and real event logs for evaluation.On the test sets of simulation dataset,Helpdesk data,and BPIC12W data,the prediction accuracy of this method improved by 2.57%,4.63%,and 1.67%compared to the CNN method,respectively.The experimental results demonstrated that the method proposed in this paper can effectively improve the accuracy of process prediction.关键词
预测性流程监控/行为轮廓/数据转换/行为编码/傅里叶变换/活动预测Key words
predictive process monitoring/behavior profile/data conversion/behavior coding/Fourier transform/activity prediction/分类
信息技术与安全科学引用本文复制引用
熊正云..基于行为轮廓的二维傅里叶变换流程预测[J].哈尔滨商业大学学报（自然科学版）,2024,40(2):157-166,10.基金项目
国家自然科学基金资助项目(61402011,61572035) （61402011,61572035）
安徽省自然科学基金资助项目(1508085MF111,1608085QF149) （1508085MF111,1608085QF149）
安徽理工大学研究生创新基金资助项目(2022CX2137). （2022CX2137）
