41 citations · 166 across the 31 of their papers we have counts for
18 papers · 1 filter
DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining
Johannes De Smedt, Jari Peeperkorn, Artem Polyvyanyy +1
Deep learning has driven many recent advances in process analytics, especially for predictive and prescriptive monitoring. However, standard objectives such as cross-entropy optimi…
Time Series Foundation Models for Process Model Forecasting
Yongbo Yu, Jari Peeperkorn, Johannes De Smedt +1
Process Model Forecasting (PMF) aims to predict how the control-flow structure of a process evolves over time by modeling the temporal dynamics of directly-follows (DF) relations,…
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…
Actor-Enriched Time Series Forecasting of Process Performance
Aurelie Leribaux, Rafael Oyamada, Johannes De Smedt +3
Predictive Process Monitoring (PPM) is a key task in Process Mining that aims to predict future behavior, outcomes, or performance indicators. Accurate prediction of the latter is…
Linking Actor Behavior to Process Performance Over Time
Aurélie Leribaux, Rafael Oyamada, Johannes De Smedt +3
Understanding how actor behavior influences process outcomes is a critical aspect of process mining. Traditional approaches often use aggregate and static process data, overlooking…
Model-driven Stochastic Trace Clustering
Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt
Process discovery algorithms automatically extract process models from event logs, but high variability often results in complex and hard-to-understand models. To mitigate this iss…
Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders
Alexander Stevens, Jari Peeperkorn, Johannes De Smedt +1
In predictive process monitoring, predictive models are vulnerable to adversarial attacks, where input perturbations can lead to incorrect predictions. Unlike in computer vision, w…
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…
Can recurrent neural networks learn process model structure?
Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt
Various methods using machine and deep learning have been proposed to tackle different tasks in predictive process monitoring, forecasting for an ongoing case e.g. the most likely…
Predicting student performance using sequence classification with time-based windows
Galina Deeva, Johannes De Smedt, Cecilia Saint-Pierre +2
A growing number of universities worldwide use various forms of online and blended learning as part of their academic curricula. Furthermore, the recent changes caused by the COVID…
Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors
Jarne Vandenabeele, Gilles Vermaut, Jari Peeperkorn +1
Reliable remaining time prediction of ongoing business processes is a highly relevant topic. One example is order delivery, a key competitive factor in e.g. retailing as it is a ma…
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…
Can deep neural networks learn process model structure? An assessment framework and analysis
Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt
Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event…
Expert-driven Trace Clustering with Instance-level Constraints
Pieter De Koninck, Klaas Nelissen, Seppe vanden Broucke +3
Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups. Typically, this partitioni…
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 Model Forecasting Using Time Series Analysis of Event Sequence Data
Johannes De Smedt, Anton Yeshchenko, Artem Polyvyanyy +2
Process analytics is an umbrella of data-driven techniques which includes making predictions for individual process instances or overall process models. At the instance level, vari…
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…
Predictive Process Model Monitoring using Recurrent Neural Networks
Johannes De Smedt, Jochen De Weerdt
The field of predictive process monitoring focuses on case-level models to predict a single specific outcome such as a particular objective, (remaining) time, or next activity/rema…