Abstract Conceptual Models (CMs) are essential for information systems engineering since
they provide explicit and detailed representations of the subject domains at hand. Ontology …

Encoding methods affect the performance of process mining tasks but little work in the
literature focused on quantifying their impact. In this paper, we compare 10 different …

Identifying user requirements and preferences on the basis of the current context, is one of
main challenges of the Internet of Things (IoT) paradigm. Users, services and applications …

Learning meaningful representations of data is an important aspect of machine learning and
has recently been successfully applied to many domains like language understanding or …

In process mining, many tasks use a simplified representation of a single case to perform
tasks like trace clustering, anomaly detection, or subset identification. These representations …

Trace similarity is a prerequisite for several process mining tasks, eg identifying process
variants and anomalies. Many similarity metrics have been presented in the literature, but …

Uncertainty is an unavoidable factor in predictive business process monitoring, especially in
terms of remaining time prediction. However, existing methods only give a precise time as …

Novelty detection refers to the task of finding observations that are new or unusual when
compared to the 'known'behavior. Its practical and challenging nature has been proven in …

This research project focusses on the development of representation learning-based
techniques for business processes. More specifically, both the architectural design as well …