基于托肯重演的并行结构过程模型修复方法

Repair method for process models with concurrent structures based on token replay

过程挖掘可以根据企业信息系统生成的事件日志建立业务过程模型。当实际业务过程发生变化时,过程模型与事件日志之间会产生偏差,这时需要对过程模型进行修正。对于含有并行结构的过程模型修复,由于加入自环和不可见变迁等因素,有些现有的修正方法的精度会降低。因此提出一种基于逻辑Petri网和托肯重演的并行结构过程模型修复方法。首先根据子模型的输入输出库所与日志的关系,确定子模型的插入位置;然后通过托肯重演的方式确定偏差所在位置;最后根据基于逻辑Petri网提出的方法进行过程模型的修复。在ProM平台上进行了仿真实验,验证了该方法的正确性和有效性,并与Fahland等方法进行对比分析。结果表明,所提方法的精度达到85%左右,相比Fahland、Goldratt方法分别提高了17和11个百分点;在简洁度方面该算法没有增加自环和不可见变迁,而Fahland和Goldratt方法均增加了不可见变迁和自环;三种方法的拟合度均在0.9以上,而Goldratt方法略低一些。以上证明用所提方法修正后的模型具有更高的拟合度和精度。

Process mining can build process model according to event logs generated by enterprise information management system.There always exist some deviations between the process model and event logs when the actual business process changes.At this time,the process model needs to be repaired.For the process model with concurrent structures,the precision of some existing repairing methods will be reduced because of the addition of self-loops and invisible transitions.Therefore,a method for repairing process models with concurrent structures was proposed on the basis of logic Petri net and token replay.Firstly,according to the relationship between the input-output places of the sub-model and event logs,the insertion position of the sub-model was determined.Then,the deviation positions were determined by a token replay method.Finally,a method was designed to repair the process models based on logical Petri net.The correctness and effectiveness of this method were verified by carrying out simulations on ProM platform,and the proposed method was compared with Fahland’s and other methods.The results show that the precision of this method is about 85%,which is increased by 17 and 11 percentage points respectively compared with those of Fahland’s and Goldratt methods,In the terms of simplicity,the proposed method does not add any self-loop or invisible transition,while Fahland’s and Goldratt methods add some selfloops and invisible transitions.All of the fitting degrees of the three methods are above 0.9,and the fitting degree of Goldratt method is slightly lower.The above verifies that the model repaired by the proposed method has higher fitness and precision.