18 papers
Monotonicity-Guided Bottom-Up Petri Net Discovery: The SPECpp Framework
Leah Tacke genannt Unterberg, Lisa L. Mannel, Wil M. P. van der Aalst
Process discovery is one of the central challenges in process mining. Petri nets are particularly attractive because simple local constructs can express complex behavior, including…
All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams
Marco Pegoraro, Jonas Seng, Dustin Heller +2
Disciplines such as business process management and process mining aid organizations by discovering insights about processes on the basis of recorded event data. However, an obstac…
Hierarchical Decomposition of Separable Workflow-Nets
Humam Kourani, Gyunam Park, Wil M. P. van der Aalst
The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. While early versions…
Revealing Inherent Concurrency in Event Data: A Partial Order Approach to Process Discovery
Humam Kourani, Gyunam Park, Wil M. P. van der Aalst
Process discovery algorithms traditionally linearize events, failing to capture the inherent concurrency of real-world processes. While some techniques can handle partially ordered…
Knowledge-Driven Hallucination in Large Language Models: An Empirical Study on Process Modeling
Humam Kourani, Anton Antonov, Alessandro Berti +1
The utility of Large Language Models (LLMs) in analytical tasks is rooted in their vast pre-trained knowledge, which allows them to interpret ambiguous inputs and infer missing inf…
Neuro-Symbolic Process Anomaly Detection
Devashish Gaikwad, Wil M. P. van der Aalst, Gyunam Park
Process anomaly detection is an important application of process mining for identifying deviations from the normal behavior of a process. Neural network-based methods have recently…