Integrated Data Repository System: Fusion, Learning and Sharing

Jeferson Lopes, Giancarlo Lucca, Rafael Huszcza, Amanda Mendes, Eduardo Nunes Borges, Pablo Guilherme, Leandro Pereira

2024

Abstract

Currently, an enormous volume of data is being generated from diverse sources, including sensors and social media. Effectively managing this unprecedented scale of data and deriving meaningful insights from these extensive datasets present a significant challenge for computer scientists. In this context, this paper outlines the development and documentation of a project dedicated to actively contributing to these critical data-driven initiatives. The described system integrates the features of a scientific data repository with a suite of data science methods, machine learning tools, and resources for geographic data visualization. By consolidating these functionalities on a single platform, users can streamline their workflow and extract insights from data more efficiently. This integrated approach facilitates seamless transitions from data storage to model training and analysis, fostering collaboration and facilitating knowledge sharing among researchers and practitioners. In this work, we highlight the system’s key features, focusing on the datasets repository and the machine learning module as central components of our platform.

Paper Citation

in Harvard Style

Lopes J., Lucca G., Huszcza R., Mendes A., Nunes Borges E., Guilherme P. and Pereira L. (2024). Integrated Data Repository System: Fusion, Learning and Sharing. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 409-416. DOI: 10.5220/0012733700003690

in Bibtex Style

@conference{iceis24,
author={Jeferson Lopes and Giancarlo Lucca and Rafael Huszcza and Amanda Mendes and Eduardo Nunes Borges and Pablo Guilherme and Leandro Pereira},
title={Integrated Data Repository System: Fusion, Learning and Sharing},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={409-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012733700003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Integrated Data Repository System: Fusion, Learning and Sharing
SN - 978-989-758-692-7
AU - Lopes J.
AU - Lucca G.
AU - Huszcza R.
AU - Mendes A.
AU - Nunes Borges E.
AU - Guilherme P.
AU - Pereira L.
PY - 2024
SP - 409
EP - 416
DO - 10.5220/0012733700003690
PB - SciTePress