Purpose The significance of business processes has fostered a close collaboration between
academia and industry. Moreover, the business landscape has witnessed continuous …

The rapid development of cutting-edge technologies, the increasing volume of data and also
the availability and processability of new types of data sources has led to a paradigm shift in …

Process event data is a fundamental building block for process mining as event logs portray
the execution trails of business processes from which knowledge and insights can be …

Recent research in Computer Science has investigated the use of Deep Learning (DL)
techniques to complement outcomes or decisions within a Discrete Event Simulation (DES) …

The field of predictive process monitoring focuses on case-level models to predict a single
specific outcome such as a particular objective,(remaining) time, or next activity/remaining …

The detection of undesired behavior is a key task in process mining, supported by
techniques for conformance checking and anomaly detection. A downside of conformance …

Interactions of multiple processes and different objects can be captured using object-centric
event data. Object-centric event data represent process executions as event graphs of …

Process mining aims to analyse business process behaviour by discovering process models
such as Petri nets from process executions recorded as sequential traces in event logs …

Abstract Predictive Process Monitoring (PPM) extends classical process mining techniques
by providing predictive models that can be applied at runtime during the execution of a …

The development of process-driven systems and the advancements in digital twins have led
to the birth of new ways of monitoring and analyzing systems, ie, digital process twins …