Data attribute oriented business process effective infrequency behavior mining method

The current process mining method takes high-frequency behavior as the mainstream behavior, and directly filters out the infrequent logs as noise to obtain a concise business process model. However, effective infrequency behaviors that are important to business processes are often data constrained. From a control flow perspective, it is difficult to accurately capture the effective infrequency behavior. A method for mining effective infrequent behaviors based on data attributes is proposed to solve the above problems. First, the important data attributes of target business processes are obtained by feature combination. Then, attribute assignment rules are set according to the needs of the business process to determine whether it has a beneficial impact on the business process. Lastly, it is suggested that a confidence interval be used instead of the traditional threshold to evaluate and mine effective low-frequency behavior. The experiment results show that compared with other methods, it can significantly improve the fitness of the business process model and can more accurately mine effective infrequency behavior to optimize the business process model.