Handling Inconsistent Government Data: From Acquisition to Entity Name Matching and Address Standardization

Davyson S. Ribeiro, Davyson S. Ribeiro, Paulo V. A. Fabrício, Paulo V. A. Fabrício, Rafael R. Pereira, Rafael R. Pereira, Tales P. Nogueira, Tales P. Nogueira, Pedro Oliveira, Pedro Oliveira, Victória T. Oliveira, Victória T. Oliveira, Ismayle S. Santos, Ismayle S. Santos, Rossana Andrade, Rossana Andrade

2025

Abstract

The integration of Data Science and Big Data is essential for managing large-scale data, but challenges such as heterogeneity, inconsistency, and data enrichment complicate this process. This paper presents a flexible architecture designed to support municipal decision-making by integrating data from multiple sources. To address inconsistencies, an entity matching algorithm was implemented, along with an address standardization library, optimizing data processing without compromising quality. The study also evaluates data acquisition methods (APIs, Web Crawlers, HTTPS requests), highlighting their trade-offs. Finally, we demonstrate the system’s practical impact through a case study on health data monitoring, showcasing its role in enhancing data-driven governance.

Paper Citation

in Harvard Style

Ribeiro D., Fabrício P., Pereira R., Nogueira T., Oliveira P., Oliveira V., Santos I. and Andrade R. (2025). Handling Inconsistent Government Data: From Acquisition to Entity Name Matching and Address Standardization. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 286-293. DOI: 10.5220/0013294200003929

in Bibtex Style

@conference{iceis25,
author={Davyson Ribeiro and Paulo Fabrício and Rafael Pereira and Tales Nogueira and Pedro Oliveira and Victória Oliveira and Ismayle Santos and Rossana Andrade},
title={Handling Inconsistent Government Data: From Acquisition to Entity Name Matching and Address Standardization},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={286-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013294200003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Handling Inconsistent Government Data: From Acquisition to Entity Name Matching and Address Standardization
SN - 978-989-758-749-8
AU - Ribeiro D.
AU - Fabrício P.
AU - Pereira R.
AU - Nogueira T.
AU - Oliveira P.
AU - Oliveira V.
AU - Santos I.
AU - Andrade R.
PY - 2025
SP - 286
EP - 293
DO - 10.5220/0013294200003929
PB - SciTePress