Alternative Model Repair Based on the Predictable Fitness

The recorded behavior in information system is constantly changing, so the deviation occurs between the event log and the given process model. Two kinds of deviations are produced by the event log, and the percentage of each deviation is uncertain in the total number of deviations. The iterative deviation generated by self-cycling and the non-iterative deviation in the event log is repaired by the same form in existing method. Furthermore, different repair form is alternatively executed under the ideal fitness of 1.Therefore, it is difficult to always ensure the fitness and precision in a reasonable range. To solve this problem, a new repair method is proposed to predict the configured fitness based on the total cost of the iterative observable deviations. Moreover, all the deviations are wholly configured when the predictable fitness meets a given threshold. The variant between the event log and the process model is found by optimal alignment when the predictable fitness does not meet the given threshold. Configuration optimization or form of self-loop insert is used to repair iterative observable deviation in the each variant based on actual condition. Simulation experiment is used to verify different data sets. The results show that the proposed method is beneficial to maximally improve the precision under ensuring reasonable fitness.