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 …

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 …

Concept drift primarily refers to an online supervised learning scenario when the relation
between the input data and the target variable changes over time. Assuming a general …

Purpose This study explores how process managers perceive the adoption, use and
management of process mining in practice. While research in process mining predominantly …

In most challenging data analysis applications, data evolve over time and must be analyzed
in near real time. Patterns and relations in such data often evolve over time, thus, models …

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

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 …

The most popular tools for stream data mining are based on decision trees. In previous 15
years, all designed methods, headed by the very fast decision tree algorithm, relayed on …

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 …