Development of a Context-Free Data Ingestion Mechanism for AutoML

Gabriel Mac’Hamilton, Alexandre M. A. Maciel

2025

Abstract

Automated Machine Learning (AutoML) is a technology that simplifies complex data processing and analysis for strategic decision-making by automating machine learning tasks and enhancing the user experience. Data ingestion is a crucial AutoML step that involves collecting external data for machine learning workflows. Typically, AutoML systems include data input modules. However, the lack of a user interface limits the number of users that can utilize it. This work presents the development of a data ingestion mechanism that streamlines and simplifies this machine learning stage into an AutoML framework called FMD. The mechanism underwent three validations: Experimentation in a real-world scenario with two databases from different contexts, evaluation from expert opinions, and usability assessment through a questionnaire using the AttrakDiff method. Following the validations, successful results were achieved in both assessments and in demonstrating the ingestion in various contexts.

Paper Citation

in Harvard Style

Mac’Hamilton G. and Maciel A. (2025). Development of a Context-Free Data Ingestion Mechanism for AutoML. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 581-588. DOI: 10.5220/0013357700003929

in Bibtex Style

@conference{iceis25,
author={Gabriel Mac’Hamilton and Alexandre Maciel},
title={Development of a Context-Free Data Ingestion Mechanism for AutoML},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2025},
pages={581-588},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013357700003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Development of a Context-Free Data Ingestion Mechanism for AutoML
SN - 978-989-758-749-8
AU - Mac’Hamilton G.
AU - Maciel A.
PY - 2025
SP - 581
EP - 588
DO - 10.5220/0013357700003929
PB - SciTePress