RT Journal Article
T1 VERONA: A python library for benchmarking deep learning in business process monitoring
A1 Gamallo Fernández, Pedro
A1 Rama Maneiro, Efrén
A1 Vidal Aguiar, Juan Carlos
A1 Lama Penín, Manuel
K1 Process mining
K1 Predictive process monitoring
K1 Benchmarking
K1 Deep learning
AB Predictive process monitoring is a subfield of process mining that focuses on predicting the future behavior of real-world processes, anticipating constraint violations and bottlenecks, and enabling real-time decision making. Among other machine learning approaches, Deep Learning-based architectures have achieved high levels of prediction accuracy, becoming an increasingly prolific area of research in recent years. However, the variety of datasets, learning techniques, and metrics used makes the comparison of proposals complicated and biased. To address this problem this paper presents VERONA, a Python library designed for the development of the deep learning predictive process monitoring pipeline. Additionally, this library provides a framework for replicating the experimental setup of the state-of-the-art benchmark in the field, enabling streamlined comparison of new approaches and improving the reproducibility of experiments.
PB Elsevier
SN 2352-7110
YR 2024
FD 2024-04-15
LK https://hdl.handle.net/10347/42124
UL https://hdl.handle.net/10347/42124
LA eng
NO SoftwareX Volume 26, May 2024, 101734
NO This work has received financial support from the Consellería de Educación, Universidade e Formación Profesional (accreditation 2019– 2022 ED431G-2019/04), the European Regional Development Fund (ERDF), which acknowledges the CiTIUS - Centro Singular de Investigación en Tecnoloxías Intelixentes da Universidade de Santiago de Compostela as a Research Center of the Galician University System, and the Spanish Ministry of Science and Innovation (grants PDC2021- 121072-C21 and PID2020-112623GB-I00).
DS Minerva
RD 24 abr 2026