Abstract: The process mining field focuses on the analysis of data generated by business process execution, aiming to extract operational process knowledge from the data. However, there may be some noise in the low-frequency logs of the model, which may negatively affect the analysis. Therefore, a method based on frequency change rules and string matching is proposed to identify and filter noise from low-frequency event logs. Firstly, based on the directly-follows graph and the eventually-follows graph, invalid direct activity pairs are identified from the event log sequence set according to frequency change rules. Then, combined with an improved string matching algorithm (KMP), the invalid activity sequences are matched with the low-frequency log traces based on the correspondence between the direct relationship of the direct-follow graph and the sequence fragment of the event log, thus filtering the noise in the log and optimizing the mining model. Finally, the effectiveness of the method is verified through specific case analysis and simulation experiments.

Key words: noise, directly-follows graph, eventually-follows graph, KMP, filtering, optimization ,