Distributed conformance checking method based on process model decomposition
Conformance checking is an important task in the field of process mining to discover the differences and commonalities between business process behaviors and observed behaviors.Alignment is one of the current standard conformance checking techniques,which accurately locates deviations between observed and modeled behavior.However,as event logs grow in size and complexity,alignment is often time-consuming and difficult to return in a reasonable amount of time.Therefore,a distributed conformance checking method based on process model decomposition was proposed.All kinds of process models were uniformly transformed into process tree models,and the process tree was decomposed into sub-trees by using the structural characteristics of the process tree to reduce the search space of the alignment method.The optimal alignment of trace and sub-models was calculated on the distributed platform Spark to speed up the calculation of alignment.The experiments were compared on several logs.The proposed meth-od had been implemented in PM4PY and Spark distributed environment,and could be used as a framework in com-bination with other conformance checking methods.The proposed method was compared with the existing alignment method based on A*and Token replay method through public event logs.Experimental results showed that the pro-posed method could improve the efficiency of calculating event log conformance.