事件日志的批量迹与过程模型的多视角对齐方法

在业务过程发现的一致性检测中,现有事件日志与过程模型的多视角对齐方法一次只能获得一条迹与过程模型的最优对齐;并且最优对齐求解中的启发函数计算复杂,以致最优对齐的计算效率较低。为此,提出一种基于迹最小编辑距离的、事件日志的批量迹与过程模型的多视角对齐方法。首先选取事件日志中的多条迹组成批量迹,使用过程挖掘算法得到批量迹的日志模型;进而获取日志模型与过程模型的乘积模型及其变迁系统,即为批量迹的搜索空间;然后设计基于Petri网变迁序列集合与剩余迹的最小编辑距离的启发函数来加快A*算法;最后设计可调节数据和资源视角所占权重的多视角代价函数,在乘积模型的变迁系统上提出批量迹中每条迹与过程模型的多视角最优对齐方法。仿真实验结果表明,相比已有工作,在计算批量迹与过程模型间的多视角对齐时,所提方法占用更少的内存空间和使用更少的运行时间。该方法提高了最优对齐的启发函数计算速度,可以一次获得批量迹的所有最优对齐,进而提高了事件日志与过程模型的多视角对齐效率。

Multi-perspective alignment between batch traces of event log and process model

In the consistency detection of business process discovery research, existing methods of multi-perspective alignment between event logs and process models can only obtain the optimal alignment of one trace with the process model at a time. Meanwhile the computation of heuristic function in obtaining the optimal alignment is complex, leading to low computation efficiency. To solve above problems, the paper proposed a multi-perspective alignment between batch traces of event log and process model based on trace minimum edit distance. Firstly, the study selected multiple traces in the event log to form batch traces, and used a process mining algorithm to obtain the log model of the batch traces. Then it obtained the product model of log model and process model and their transition system, which was the search space of batch traces. Then, it designed a heuristic function based on the minimum edit distance between Petri net transition''s sequence set and the remaining traces to speed up the A* algorithm. Finally, the study designed a multi-perspective cost function that could adjust the weight of data and resource perspectives, and proposed a method of multi-perspective optimal alignment between each trace in the batch traces and the process model with the transition system of the product model. Compared with existing work, the simulation results show the proposed method takes up less memory space and uses less running time. This method improves the computation speed of heuristic function for optimal alignment, and obtains all optimal alignments of batch traces at one time, thus improves the efficiency of multi-perspective alignment between event logs and process models.