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 …

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 …

Business processes are prone to unexpected changes, as process workers may suddenly or
gradually start executing a process differently in order to adjust to changes in workload …

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

Online process mining refers to a class of techniques for analyzing in real-time event
streams generated by the execution of business processes. These techniques are crucial in …

Rapidly changing business environments expose companies to high levels of uncertainty.
This uncertainty manifests itself in significant changes that tend to occur over the lifetime of a …

Modern business processes are embedded in a complex environment and, thus, subjected
to continuous changes. While current approaches focus on the control flow only, additional …

Existing business process drift detection methods do not work with event streams. As such,
they are designed to detect inter-trace drifts only, ie drifts that occur between complete …

Predictive process monitoring emerged as a technique to anticipate the outcome of a
running instance of a business process. To this end, it first constructs a forecast model based …

Real-life business processes are complex and show a high degree of variability.
Additionally, due to changing conditions and circumstances, these processes continuously …