Deep-vacuity: A Proposal of a Machine Learning Platform based on High-performance Computing Architecture for Insights on Government of Brazil Official Gazettes

Leonardo R. De Carvalho, Felipe Lopes, Jefferson Chaves, Marcos Lima, Flávio Gomes De Deus, Aletéia von Paungarthem, Flavio Vidal

2022

Abstract

Brazil publishes region information, public tenders for the hire of civil servants, and also government contracts with companies in its Official Gazettes. All this volume of information can contain interesting relationships that reveal unique characteristics of the government, such as the effectiveness of public policies and even the existence of illegal schemes. Establishing these relationships is not a trivial task and requires great effort. Therefore, this work proposes the Deep Vacuity platform, which, by using a High-Performance Computing architecture along with Machine Learning techniques, can collect, depurate, consolidate and analyze the data, offering a friendly interface for decision-makers.

Paper Citation

in Harvard Style

R. De Carvalho L., Lopes F., Chaves J., Lima M., Gomes De Deus F., von Paungarthem A. and Vidal F. (2022). Deep-vacuity: A Proposal of a Machine Learning Platform based on High-performance Computing Architecture for Insights on Government of Brazil Official Gazettes. In Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-613-2, pages 136-143. DOI: 10.5220/0011532500003318

in Bibtex Style

@conference{webist22,
author={Leonardo R. De Carvalho and Felipe Lopes and Jefferson Chaves and Marcos Lima and Flávio Gomes De Deus and Aletéia von Paungarthem and Flavio Vidal},
title={Deep-vacuity: A Proposal of a Machine Learning Platform based on High-performance Computing Architecture for Insights on Government of Brazil Official Gazettes},
booktitle={Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2022},
pages={136-143},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011532500003318},
isbn={978-989-758-613-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Deep-vacuity: A Proposal of a Machine Learning Platform based on High-performance Computing Architecture for Insights on Government of Brazil Official Gazettes
SN - 978-989-758-613-2
AU - R. De Carvalho L.
AU - Lopes F.
AU - Chaves J.
AU - Lima M.
AU - Gomes De Deus F.
AU - von Paungarthem A.
AU - Vidal F.
PY - 2022
SP - 136
EP - 143
DO - 10.5220/0011532500003318