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.
Highlights
The goal of process mining is to understand and improve business processes, and the field consists of three main branches: process discovery, consistency checking, and process enhancement [1]
We evaluated the targets based on synthetic event stream sequences and real logs through three experiments
We take the reference model as the starting point and calculate the distance between the new event stream and the reference model by considering both structural characteristics and behavioral characteristics of the event stream through the activity vector and successor vector. It is updated in real time according to the trusted behavior interval
Summary
The goal of process mining is to understand and improve business processes, and the field consists of three main branches: process discovery, consistency checking, and process enhancement [1]. Camargo et al propose an automated process discovery algorithm that obtains a highly accurate model based on the similarity between the model and the log behavior [7]. Incremental mining techniques locally update the reference model to fit the new structure based on dynamically updated data. This feature can compensate for the shortcomings of traditional methods. Some scholars have introduced incremental techniques into the research of process model mining to improve the utilization of reference models and the adaptability of process mining algorithms to new events.
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