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English Information

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Authors
# Name
1 Dimas Nascimento(dimas.cassimiro@ufape.edu.br)
2 Daliton Silva(daliton.silva@ufape.edu.br)

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Reference
# Reference
1 Abedjan, Z., Chu, X., Deng, D., Fernandez, R. C., Ilyas, I. F., Ouzzani, M., Papotti, P., Stonebraker, M., and Tang, N. (2016). Detecting data errors: Where are we and what needs to be done? Proceedings of the VLDB Endowment, 9(12):993–1004.
2 Batini, C., Cappiello, C., Francalanci, C., and Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3):16:1–16:52.
3 Batini, C. and Scannapieco, M. (2006). Data Quality: Concepts, Methodologies and Techniques. Springer.
4 Bhadauria, D., Harmouch, H., Naumann, F., Srivastava, D., and Ehrlinger, L. (2026). A catalog of data errors. arXiv preprint arXiv:2604.09277.
5 Date, C. J. (2005). Database in depth: The relational model for practitioners. O’Reilly Media.
6 Ehrlinger, L. and Wöß, W. (2022). A survey of data quality measurement and monitoring tools. Frontiers in Big Data, 5:850611.
7 Kim, W., Choi, B.-J., Hong, E.-K., Kim, S.-K., and Lee, D. (2003). A taxonomy of dirty data. Data Mining and Knowledge Discovery, 7(1):81–99.
8 Müller, H. and Freytag, J.-C. (2003). Problems, methods, and challenges in comprehensive data cleansing. Technical Report HUB-IB-164, Humboldt University, Berlin.
9 Nascimento, D. C., da Silva, D., and Pereira, L. F. A. (2025). Análise teórica do impacto de dados faltantes em atributos sensíveis sobre a métrica de fairness p%-rule. In Simpósio Brasileiro de Banco de Dados (SBBD), pages 767–773. SBC.
10 Oliveira, P., Rodrigues, F., and Henriques, P. R. (2005a). A formal definition of data quality problems. In Proceedings of the International Conference on Information Quality (ICIQ).
11 Oliveira, P., Rodrigues, F., Henriques, P. R., and Galhardas, H. (2005b). A taxonomy of data quality problems. In Proceedings of the 2nd International Workshop on Data and Information Quality, pages 219–233.
12 Rahm, E. and Do, H. H. (2000). Data cleaning: Problems and current approaches. IEEE Bulletin of the Technical Committee on Data Engineering, 23(4):3–13.
13 Zhang, S., Huang, Z., and Wu, E. (2025). Data cleaning using large language models. In Proceedings of the IEEE International Conference on Data Engineering Workshops (ICDEW), pages 28–32.