Authors:
Jeferson Lopes
1
;
Giancarlo Lucca
2
;
Rafael Huszcza
1
;
Amanda Mendes
1
;
Eduardo Nunes Borges
1
;
Pablo Guilherme
3
and
Leandro Pereira
4
Affiliations:
1
Centro de Ciências Computacionais (C3), Universidade Federal do Rio Grande (FURG), Rio Grande, Brazil
;
2
Mestrado em Engenharia Eletrônica e Computação, Universidade Católica de Pelotas, Pelotas, Brazil
;
3
Centro de Educação, Humanidades e Ciências Biológicas, Universidade Estadual do Paraná (UNESPAR), Paranaguá, Brazil
;
4
Eixo Tecnológico de Meio Ambiente, Instituto Federal do Paraná (IFPR), Paranaguá, Brazil
Keyword(s):
Data Repository, Machine Learning, Deep Learning, Geographic Information System.
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 w
ork, we highlight the system’s key features, focusing on the datasets repository and the machine learning module as central components of our platform.
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