Online Incremental Mining Based on Trusted Behavior Interval

Abstract

Incremental mining improves the quality of process mining by analyzing the differences between event logs and a reference model to obtain valuable information to update the reference model. Existing incremental mining methods focus on offline logs by setting thresholds for analysis, which limits process mining efforts by the domain knowledge, log completeness, and business completion time. Aiming at these problems, a real-time incremental mining algorithm based on the trusted behavior interval is proposed to analyze online event streams for updating the reference model. First, a clustering technique to analyze an existing reference model selects the core structure of the model and calculates the trusted behavior interval. Then, the behavioral and structural relationships between the online event streams and the reference model are analyzed to obtain a valid candidate set. Based on this set, an incremental update algorithm is proposed to optimize the model structure to achieve an online dynamic update of the reference model. The proposed algorithm is implemented in PM4PY and Scikit-learn frameworks; a reasonable number of clusters is determined using the elbow method and validated with artificial and real data. Experimental results show that the algorithm improves the efficiency of incremental mining and enhances the quality of the model with both complete and incomplete data.