49 citations · 50 across the 11 of their papers we have counts for
5 papers · 1 filter
Assisted Data Annotation for Business Process Information Extraction from Textual Documents
Julian Neuberger, Han van der Aa, Lars Ackermann +3
Machine-learning based generation of process models from natural language text process descriptions provides a solution for the time-intensive and expensive process discovery phase…
AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation
Lukas Kirchdorfer, Robert Blümel, Timotheus Kampik +2
Business process simulation (BPS) is a versatile technique for estimating process performance across various scenarios. Traditionally, BPS approaches employ a control-flow-first pe…
A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models
Julian Neuberger, Lars Ackermann, Han van der Aa +1
Over the past decade, extensive research efforts have been dedicated to the extraction of information from textual process descriptions. Despite the remarkable progress witnessed i…
Mining Constraints from Reference Process Models for Detecting Best-Practice Violations in Event Logs
Adrian Rebmann, Timotheus Kampik, Carl Corea +1
Detecting undesired process behavior is one of the main tasks of process mining and various conformance-checking techniques have been developed to this end. These techniques typica…
PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances
Keyvan Amiri Elyasi, Han van der Aa, Heiner Stuckenschmidt
We present PGTNet, an approach that transforms event logs into graph datasets and leverages graph-oriented data for training Process Graph Transformer Networks to predict the remai…