33 citations · 34 across the 13 of their papers we have counts for
13 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…
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…
Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text
Phuong Nam Lê, Charlotte Schneider-Depré, Alexandre Goossens +3
Efficient planning, resource management, and consistent operations often rely on converting textual process documents into formal Business Process Model and Notation (BPMN) models.…
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…
Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data
Zhipeng He, Alexander Stevens, Chun Ouyang +3
Adversarial attacks on tabular data present unique challenges due to the heterogeneous nature of mixed categorical and numerical features. Unlike images where pixel perturbations m…
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…
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…
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…