13 citations · 18 across the 8 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…
Attention Please: What Transformer Models Really Learn for Process Prediction
Martin Käppel, Lars Ackermann, Stefan Jablonski +1
Predictive process monitoring aims to support the execution of a process during runtime with various predictions about the further evolution of a process instance. In the last year…
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…
Recent Advances in Data-Driven Business Process Management
Lars Ackermann, Martin Käppel, Laura Marcus +21
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 sh…
Leveraging Data Augmentation for Process Information Extraction
Julian Neuberger, Leonie Doll, Benedict Engelmann +2
Business Process Modeling projects often require formal process models as a central component. High costs associated with the creation of such formal process models motivated many…