Process mining is a growing and promising study area focused on understanding processes
and to help capture the more significant findings during real execution rather than, those …

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 …

A model is an artefact to represent a specific concept. It maps properties of the concept into
some abstract representation, driven by the purpose of the model. As such, models …

Predictive business process monitoring refers to the act of making predictions about the
future state of ongoing cases of a business process, based on their incomplete execution …

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 …

Healthcare organizations are confronted with challenges including the contention between
tightening budgets and increased care needs. In the light of these challenges, they are …

Process mining refers to a family of algorithms used to computationally reconstruct, analyze
and visualize business processes through event log data. While process mining is …

Event sequence data is increasingly available in various application domains, such as
business process management, software engineering, or medical pathways. Processes in …

Process analytics is a collection of data-driven techniques for, among others, making
predictions for individual process instances or overall process models. At the instance level …