1 citations · 2 across the 6 of their papers we have counts for
7 papers
Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring
Kseniya Sahatova, Rafael Seidi Oyamada, Xuefei Lu +1
Predictive process monitoring supports the optimization and control of operational business processes by forecasting the future state or outcome of ongoing cases. While deep neural…
Dynamic Hypergraph Representation Learning for Multivariate Time Series without Prior Knowledge
Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco +1
Hypergraphs have the capacity to capture higher-dimensional relationships among entities across various domains, making them a subject of growing interest within the research commu…
Object-Centric Event Logs: Specifications, Comparative Analysis and Refinement
Alexandre Goossens, Johannes De Smedt, Jan Vanthienen
Process mining aims to comprehend and enhance business processes by analyzing event logs. Recently, object-centric process mining has gained traction by considering multiple object…
Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes
Alexander Stevens, Chun Ouyang, Johannes De Smedt +1
In recent years, various machine and deep learning architectures have been successfully introduced to the field of predictive process analytics. Nevertheless, the inherent opacity…
Extracting Process-Aware Decision Models from Object-Centric Process Data
Alexandre Goossens, Johannes De Smedt, Jan Vanthienen
Organizations execute decisions within business processes on a daily basis whilst having to take into account multiple stakeholders who might require multiple point of views of the…
Signature-Based Community Detection for Time Series
Marco Gregnanin, Johannes De Smedt, Giorgio Gnecco +1
Community detection for time series without prior knowledge poses an open challenge within complex networks theory. Traditional approaches begin by assessing time series correlatio…