关键词: 过程挖掘, 漂移检测, 切比雪夫不等式, 日志完备性, 业务流程管理

Abstract: The purpose of is to assert whether the model has changed by detecting changes in the given log.However,Aiming at the limitations of existing drift detection methods in process mining that were large feature extraction,detection delay and inability to locate changing region accurately,a novel method for sudden drift detection was proposed.The problem of sudden drift detection was transformed into local completeness computing problem of the given log.Then Chebyshev inequality was used to deduce the expression of local completeness based on direct succession and frequency.To avoid the interference of exclusiveness and concurrency,a training window was adopted to calculate the initial value of local completeness.In addition,the cutting operation was defined to ensure that the algorithm could run iteratively until all traces in log had been detected.Experiments on synthetic logs showed that the proposed method was effective in detecting sudden drift.

Key words: process mining, drift detection, Chebyshe'v inequality, log completeness, business process management