基于感知成本的流程模型与事件日志有效对齐

Effective alignment of process model with event logs based on perceived cost

现存的成本函数没有考虑到业务流程中各活动在现实情境中的不同的重要程度,于是在模型与日志的对齐过程中可能会导致对齐成本严重偏离感知成本。针对这一问题,基于业务流程中行为的典型流特征提出了重要同步成本函数的概念,并在该函数下给出一种能够提升效率的对齐方法。首先,基于感知成本的概念定义重要同步成本函数;接着,依据日志迹以及流程模型中行为的典型流特征来确定用以分割流程模型与日志迹的重要匹配子序列;最后,基于重要同步成本函数来对齐分割后的子流程和对应的日志迹子序列,并将分段对齐的结果进行合并得到最终的对齐结果。实验部分从准确率和效率两方面进行验证所提方法:在准确率方面,与现存的标准成本函数和最大同步成本函数相比,所提成本函数下的对齐准确率最高提升了17.44个百分点,且当事件日志包含混合噪声时,所提成本函数下的平均对齐准确率最高,为88.67%;在对齐效率方面则通过比较对齐所耗时间来验证,现存两种函数的平均耗时分别为1.58 s和2.21 s,而所提方法为0.63 s,效率分别提升了150.79%和250.79%。实验结果表明所提方法能在满足准确率需求的同时提升对齐的效率。

The different importance of the activities in the business process in real world is not taken into account by the existing cost functions, so that in the alignment process of model and log, alignment cost may deviates from perceived cost significantly. To solve this problem, a concept of important synchronization cost function was proposed based on the typical flow characteristic of the behaviors in business processes, and an alignment method that can improve efficiency was proposed under this function. Firstly, the important synchronization cost function was defined based on the concept of perceived cost.Then, the important matching sub-sequence to segment the process model and the log trace was determined according to the log trace and the typical flow characteristic of the behaviors in the process model. Finally, based on the important synchronization cost function, the segmented sub-process and the corresponding log trace subsequence were aligned, and the segmented alignment results were combined to obtain the final alignment result. The experiments were carried out to verify the proposed method from the perspectives of accuracy and efficiency. In terms of accuracy, compared with the existing standard cost function and maximum synchronous cost function, the proposed cost function improved the alignment accuracy by up to 17. 44 percentage points, and when the event log contained mixed noise, the proposed cost function had the highest average alignment accuracy of 88. 67%. The efficiency of alignment was verified by comparing the time consumed by alignment. The average time of the existing two functions were 1. 58 s and 2. 21 s respectively, while that of the proposed method was 0. 63 s, which was improved by 150. 79% and 250. 79% respectively. Experimental results show that the proposed method can satisfy the accuracy demand and improve the efficiency of alignment at the same time.