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 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 …

Work in organisations is often structured into business processes, implemented using
process-aware information systems (PAISs). These systems aim to enforce employees to …

Process mining provides techniques to learn models from event data. These models can be
descriptive (eg, Petri nets) or predictive (eg, neural networks). The learned models offer …

Predictive services nowadays play an important role across all business sectors. However,
deployed machine learning models are challenged by changing data streams over time …

Business processes evolve over time to adapt to changing business environments. This
requires continuous monitoring of business processes to gain insights into whether they …

Flexibility and evolution have been a hot topic in the context of business process
management. However, contemporary process mining techniques assume processes to be …

Process mining can help organizations by extracting knowledge from event logs. However,
process mining techniques often assume business processes are stationary, while actual …

The remarkable growth of process mining applications in care pathway monitoring is
undeniable. One of the sub-emerging case studies is the use of patients' location data in …

Real-life business processes may change over time in response to new business
requirements, market changes, new policies or regulations, etc., which is called concept drift …