41 citations · 166 across the 31 of their papers we have counts for
31 papers
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…
Domain Adaptation of LLMs for Process Data
Rafael Seidi Oyamada, Jari Peeperkorn, Jochen De Weerdt +1
In recent years, Large Language Models (LLMs) have emerged as a prominent area of interest across various research domains, including Process Mining (PM). Current applications in P…
Bridging Language Gaps: Enhancing Few-Shot Language Adaptation
Philipp Borchert, Jochen De Weerdt, Marie-Francine Moens
The disparity in language resources poses a challenge in multilingual NLP, with high-resource languages benefiting from extensive data, while low-resource languages lack sufficient…
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…
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…
On the Performance of LLMs for Real Estate Appraisal
Margot Geerts, Manon Reusens, Bart Baesens +2
The real estate market is vital to global economies but suffers from significant information asymmetry. This study examines how Large Language Models (LLMs) can democratize access…
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…
Language Fusion for Parameter-Efficient Cross-lingual Transfer
Philipp Borchert, Ivan Vulić, Marie-Francine Moens +1
Limited availability of multilingual text corpora for training language models often leads to poor performance on downstream tasks due to undertrained representation spaces for lan…
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…
Dynamic and Scalable Data Preparation for Object-Centric Process Mining
Lien Bosmans, Jari Peeperkorn, Alexandre Goossens +3
Object-centric process mining is emerging as a promising paradigm across diverse industries, drawing substantial academic attention. To support its data requirements, existing obje…
Native Design Bias: Studying the Impact of English Nativeness on Language Model Performance
Manon Reusens, Philipp Borchert, Jochen De Weerdt +1
Large Language Models (LLMs) excel at providing information acquired during pretraining on large-scale corpora and following instructions through user prompts. This study investiga…
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning
Philipp Borchert, Jochen De Weerdt, Marie-Francine Moens
Differentiating relationships between entity pairs with limited labeled instances poses a significant challenge in few-shot relation classification. Representations of textual data…
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
Philipp Borchert, Jochen De Weerdt, Kristof Coussement +2
We introduce CORE, a dataset for few-shot relation classification (RC) focused on company relations and business entities. CORE includes 4,708 instances of 12 relation types with c…
Discovering High-Quality Process Models Despite Data Scarcity
Jan Niklas Adams, Jari Peeperkorn, Tobias Brockhoff +6
Process discovery algorithms learn process models from executed activity sequences, describing concurrency, causality, and conflict. Concurrent activities require observing multipl…
Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques
Manon Reusens, Philipp Borchert, Margot Mieskes +2
This paper investigates the transferability of debiasing techniques across different languages within multilingual models. We examine the applicability of these techniques in Engli…
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…
Taught by the Internet, Exploring Bias in OpenAIs GPT3
Ali Ayaz, Aditya Nawalgaria, Ruilian Yin
This research delves into the current literature on bias in Natural Language Processing Models and the techniques proposed to mitigate the problem of bias, including why it is impo…
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…