Concept drift detection and localization framework based on behavior replacement

Jiuyun Xu, Yue Zhang, Qiang Duan

Research output: Contribution to journalArticlepeer-review


With the application of numerous services or software, process mining has attracted more and more attention. However, concept drift may occur during process mining due to the instability of the process. Sudden and gradual drifts are considered to be two basic modes of change, that may always appear in independent or nested forms. Although the existing methods have studied the detection of two basic modes, they do not consider the nesting of two change modes. We identify the change mode that sudden drifts and gradual drifts do not appear independently as nested drifts. The current drift detection methods can only detect the drift modes that occur independently, but not suitable for nested drift detection. To fill this gap, this paper proposes a business concept drift detection and localization framework called BRDDL (Behavior Replacement-based Drift Detection and Localization) which can not only detect independent drifts such as sudden drifts and gradual drifts, but also detect nested drifts. Firstly, we propose an integrated drift point detection and localization method which can report the location of change points and return the changed behaviors (activity relationship pairs). On this basis, we propose a behavior replacement method by updating the changed traces to restore an unchanged sub log. Then we compare the behaviors in the updated traces with those in the associated unchanged traces to judge the type of drifts. The effectiveness of the method is verified by simulation experiments on the synthetic log.

Original languageEnglish (US)
JournalApplied Intelligence
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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