41 citations · 166 across the 31 of their papers we have counts for
10 papers · 1 filter
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…