Filtering out Noise Logs for Process Modelling Based on Event Dependency

Mining high-quality process models from real-life event logs remains a huge challenge owing to the presence of noises in the logs. Noises lead to unnecessary structures in process models, which further impede their precision and fitness. This paper proposed a novel double granularity method of filtering out noise logs based on event dependency. In this method, local dependency and global dependency of events in recorded logs are coupled as a mixed dependency to conduct fine-grained filtering of noise events based on statistical analysis. Furthermore, a coarse-grained filtering strategy is added to the method for removing specific noise traces that cannot be detected by fine-grained filtering. Comparison experiments on one synthetic and five real-life datasets with two other filtering algorithms verified the superior performance and high precision of the method.