Abstract: Alignment is a type of conformance checking technique that involves checking the modeled process behavior against the process behavior recorded in the event data.Due to hardware failures,software errors,and other factors,temporal data recordings show diversity,including different accuracies and errors,leading to temporal uncertainty in the recorded data.This paperconsi-ders the infix/postfix traces containing temporal uncertainty,and proposes a trace fragment alignment method based on temporal uncertainty,which addresses the traditional trace fragment alignment method that cannot effectively deal with uncertainty,and solves the problems of insufficient alignment accuracy and low computational efficiency of the traditional alignment due to temporal uncertainty.Specifically,firstly,uncertain traces are processed and behavior net are generated.Secondly,markings of the process model are computed and auxiliary nets are constructed.Finally,synchronous product nets are constructed to compute the trace fragment alignment with time uncertainty.The proposed method broadens the application scope of the alignment technique,enabling the alignment to adapt to and handle data containing temporal deviations,and enhancing the stability and robustness of the alignment algorithm in the face of imperfect data.Experimental results show that the proposed method improves the alignment accuracy and effectively reduces the computational complexity compared to the traditional method when dealing with uncertainty.

Key words: Process mining, Conformance checking, Alignment, Uncertain data