Physics

Process mining

Work in process

Event (particle physics)

Business process management

Operating system

Data mining

Engineering

Process management

Operations management

Data science

Computer science

Quantum mechanics

Process modeling

Data stream mining

Process (computing)

Concept drift

Business process

IEEE Transactions on Neural Networks and Learning Systems

Although most business processes change over time, contemporary process mining techniques tend to analyze these processes as if they are in a steady state. Processes may change suddenly or gradually. The drift may be periodic (e.g., because of seasonal influences) or one-of-a-kind (e.g., the effects of new legislation). For the process management, it is crucial to discover and understand such concept drifts in processes. This paper presents a generic framework and specific techniques to detect when a process changes and to localize the parts of the process that have changed. Different features are proposed to characterize relationships among activities. These features are used to discover differences between successive populations. The approach has been implemented as a plug-in of the ProM process mining framework and has been evaluated using both simulated event data exhibiting controlled concept drifts and real-life event data from a Dutch municipality.

Dealing With Concept Drifts in Process Mining