基于Petri网的并发事件流程模型修复分析

目前,流程模型可以从大量的事件日志中挖掘出来,以重放大多数的日志.但是,少数偏离流程模型的日志亦是有效的,为了使事件日志与流程模型更加拟合,模型修复是一个很好的方法.提出了基于Petri网的并发事件流程模型修复分析方法.首先,找到事件日志与流程模型的最优对齐,筛选出用于修复的并发事件;其次,利用提出的重构子流程的修复方法,对筛选得到的并发事件进行重构;最后,根据算法嵌入到原始模型中以实现模型修复,并通过一个具体实例说明了该方法的合理有效性.修复后的模型可以完全重放给定的事件日志,并且能够避免因循环造成的多余行为的发生,同时也在最大程度上保留了原始模型的使用价值.

Repair analysis of concurrent event process model based on Petri net

At present, the process model can be mined from a large number of event logs to replay most of the logs. However, a few logs that deviate from the process model are also valid, and model repair is a good way to make the event log fit more with the process model. This paper proposes a repair analysis method of concurrent event process model based on Petri net. Firstly, the optimal alignment between the event log and the process model is found to filter out the concurrent events for repair. Secondly, the filtered concurrent events are reconstructed by using the proposed reconstruction sub-process repair method. Finally, the algorithm is embedded into the original model to realize the model repair, and the reasonable effectiveness of the method is illustrated through a concrete example. The repaired model can completely replay the given event log, and it can avoid the occurrence of redundant behavior caused by the loop, while also preserving the use value of the original model to the maximum extent.