41 citations · 79 across the 11 of their papers we have counts for
12 papers
David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM
Hans Weytjens, Ingo Weber
Next activity prediction (NAP) is a cornerstone of predictive process monitoring (PPM), enabling organizations to move from retrospective analysis to proactive process steering. Th…
SCOPE: Sequential Causal Optimization of Process Interventions
Jakob De Moor, Hans Weytjens, Johannes De Smedt +1
Prescriptive Process Monitoring (PresPM) recommends interventions during running business processes to optimize key performance indicators (KPIs). In realistic settings, interventi…
Uncertainty in Machine Learning
Hans Weytjens, Wouter Verbeke
This book chapter introduces the principles and practical applications of uncertainty quantification in machine learning. It explains how to identify and distinguish between differ…
ProCause: Generating Counterfactual Outcomes to Evaluate Prescriptive Process Monitoring Methods
Jakob De Moor, Hans Weytjens, Johannes De Smedt
Prescriptive Process Monitoring (PresPM) is the subfield of Process Mining that focuses on optimizing processes through real-time interventions based on event log data. Evaluating…
On LLM-Assisted Generation of Smart Contracts from Business Processes
Fabian Stiehle, Hans Weytjens, Ingo Weber
Large language models (LLMs) have changed the reality of how software is produced. Within the wider software engineering community, among many other purposes, they are explored for…
SimBank: from Simulation to Solution in Prescriptive Process Monitoring
Jakob De Moor, Hans Weytjens, Johannes De Smedt +1
Prescriptive Process Monitoring (PresPM) is an emerging area within Process Mining, focused on optimizing processes through real-time interventions for effective decision-making. P…
RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines
Xiwei Xu, Hans Weytjens, Dawen Zhang +3
Recent studies show that 60% of LLM-based compound systems in enterprise environments leverage some form of retrieval-augmented generation (RAG), which enhances the relevance and a…
Timing Process Interventions with Causal Inference and Reinforcement Learning
Hans Weytjens, Wouter Verbeke, Jochen De Weerdt
The shift from the understanding and prediction of processes to their optimization offers great benefits to businesses and other organizations. Precisely timed process intervention…
Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring
Hans Weytjens, Jochen De Weerdt
The inability of artificial neural networks to assess the uncertainty of their predictions is an impediment to their widespread use. We distinguish two types of learnable uncertain…
Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring
Hans Weytjens, Jochen De Weerdt
Advances in AI, and especially machine learning, are increasingly drawing research interest and efforts towards predictive process monitoring, the subfield of process mining (PM) t…
Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes
Hans Weytjens, Jochen De Weerdt
Artificial neural networks will always make a prediction, even when completely uncertain and regardless of the consequences. This obliviousness of uncertainty is a major obstacle t…
Process Outcome Prediction: CNN vs. LSTM (with Attention)
Hans Weytjens, Jochen De Weerdt
The early outcome prediction of ongoing or completed processes confers competitive advantage to organizations. The performance of classic machine learning and, more recently, deep…