Many organisations now seek to analyse and improve their processes using event logs from
various IT systems supporting their operations. Process mining aims to obtain insights from …

The application of process mining for unstructured data might significantly elevate novel
insights into disciplines where unstructured data is a common data format. To efficiently …

Process mining techniques aim to derive knowledge of the execution of processes, by
means of automated analysis of behaviour recorded in event logs. A well-known challenge …

Reducing waiting times in end-to-end business processes is a recurrent concern in the field
of business process management. The uptake of data-driven approaches in this field in the …

Process mining aims to bridge the gap between data science and process science by
providing a variety of powerful data-driven analyses techniques on the basis of event data …

Process mining techniques rely on the availability of event logs, where events have a certain
granularity that is deemed appropriate for representing business activities. In this paper, we …

Conventional production technology is static by nature, developments in the area of
autonomous driving and communication technology enable a novel type of production line …

Within process mining, discovery techniques make it possible to construct business process
models automatically from event logs. However, results often do not achieve a balance …

To understand and maintain the behavior of a (legacy) software system, one can observe
and study the system's behavior by analyzing event data. For model-driven reverse …

Process mining is a well-established discipline of data analysis focused on the discovery of
process models from information systems' event logs. Recently, an emerging subarea of …