摘要
对齐是一致性检验技术的一种,涉及将建模的流程行为与事件数据中记录的流程行为进行核对。由于硬件故障、软件错误等因素的影响,时间数据记录呈现出多样性,包括不同的精度和误差,导致记录的数据存在时间不确定性。对此,考虑了含有时间不确定性的中缀/后缀轨迹,提出了基于时间不确定性的轨迹片段对齐方法,针对传统轨迹片段对齐方法无法有效处理不确定性,解决了传统对齐由于时间不确定性导致的对齐精度不足和计算效率低的问题。具体而言,首先处理不确定轨迹并生成行为网;其次计算流程模型的标记,构建辅助网;最后构建同步乘积网,计算时间不确定性的轨迹片段对齐。所提方法拓宽了对齐技术的应用范围,使得对齐能够适应和处理含有时间偏差的数据,增强了对齐算法在面对不完美数据时的稳定性和鲁棒性。实验结果表明,所提出的方法在处理不确定性时,相较于传统方法提高了对齐精度并有效减少了计算复杂度。
Alignment is a type of conformance checking technique that involves checking the modeled process behavior against the process behavior recorded in the event data.Due to hardware failures,software errors,and other factors,temporal data recordings show diversity,including different accuracies and errors,leading to temporal uncertainty in the recorded data.This paper consi-ders the infix/postfix traces containing temporal uncertainty,and proposes a trace fragment alignment method based on temporal uncertainty,which addresses the traditional trace fragment alignment method that cannot effectively deal with uncertainty,and solves the problems of insufficient alignment accuracy and low computational efficiency of the traditional alignment due to temporal uncertainty.Specifically,firstly,uncertain traces are processed and behavior net are generated.Secondly,markings of the process model are computed and auxiliary nets are constructed.Finally,synchronous product nets are constructed to compute the trace fragment alignment with time uncertainty.The proposed method broadens the application scope of the alignment technique,enabling the alignment to adapt to and handle data containing temporal deviations,and enhancing the stability and robustness of the alignment algorithm in the face of imperfect data.Experimental results show that the proposed method improves the alignment accuracy and effectively reduces the computational complexity compared to the traditional method when dealing with uncertainty.
作者
高灵婷
叶剑虹
姜文慧
黄一凡
GAO Lingting;YE Jianhong;JIANG Wenhui;HUANG Yifan(College of Computer Science and Technology,Huaqiao University,Xiamen,Fujian 361021,China)
出处
《计算机科学》
北大核心
2025年第S2期595-601,共7页
Computer Science
基金
福建省科技厅引导性项目(2024H0014(2024H01010100))。
关键词
流程挖掘
一致性检查
对齐
不确定数据
Process mining
Conformance checking
Alignment
Uncertain data
