The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Concept drift in process mining (PM) is a challenge as classical methods assume processes
are in a steady-state, ie, events share the same process version. We conducted a systematic …

Process mining techniques can be used to analyse business processes using the data
logged during their execution. These techniques are leveraged in a wide range of domains …

Process Mining is a new kind of Business Analytics and has emerged as a powerful family of
Process Science techniques for analysing and improving business processes. Although …

Most machine learning models are static, but the world is dynamic, and increasing online
deployment of learned models gives increasing urgency to the development of efficient and …

Over the last decade, process mining emerged as a new research field that focuses on the
analysis of processes using event data. Classical data mining techniques such as …

Enabling Flexibility in Process-Aware Information Systems: Challenges, Methods, Technologies
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The growing interest in process mining is fueled by the increasing availability of event data.
Process mining techniques use event logs to automatically discover process models, check …

The execution of processes leaves trails of event data in information systems. These event
data are analyzed to generate insights and improvements for the underlying process …

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 …