Infrequent behavior recognition is one of the methods to reveal important information about business processes and optimize process models. Existing process discovery methods have overlooked the impact of data influence chains on infrequent behavior, resulting in some infrequent behavior being considered as noise and filtered out directly. To address this issue, this paper proposes a novel infrequent behavior analysis method based on activity recovery sets . Firstly, the event logs are filtered based on the importance of behavior and an initial process model is constructed. Secondly, input and output data items of activities are extracted from transaction logs, and an activity influence chain graph is constructed based on these data items. Activity recovery sets are obtained based on these graphs. Finally, the behavior tolerance of each trace is calculated using the activity recovery sets to distinguish effective infrequent behavior from noise. The experimental results indicate that, compared to other methods, this study effectively distinguishes valid infrequent behaviors from noise and improves the quality of the process model in terms of fitness, precision, and simplicity. This method considers the biases caused by the activity recovery set and successfully identifies valid infrequent behaviors in event logs, thereby optimizing the process model.

[2024-01-30] Accepted Paper /doc/2023.11.0567/v1