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English Information

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Authors
# Name
1 Afonso Sousa Lima(afonso.matheus@usp.br)
2 Matheus Queiroz Mota(matheusqueirozmota@usp.br)
3 Leonardo Cardoso(leo.silva.cardoso@usp.br)
4 Elaine Sousa(parros@icmc.usp.br)

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Reference
# Reference
1 Bartz, E. and Bartz-Beielstein, T. (2024). Online Machine Learning. Springer.
2 Benhamza, K., Benselim, R., Naidja, H., and Seridi, H. (2025). A comprehensive survey of imputation methods in medical missing data analysis. Applied Intelligence, 55(11):778.
3 Bifet, A., Gavalda, R., Holmes, G., and Pfahringer, B. (2023). Machine learning for data streams: with practical examples in MOA. MIT press.
4 Isgut, M., Gloster, L., Choi, K., Venugopalan, J., and Wang, M. D. (2022). Systematic review of advanced ai methods for improving healthcare data quality in post covid-19 era. IEEE Reviews in Biomedical Engineering, 16:53–69.
5 Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., and Muller, P.-A. (2019). Deep learning for time series classification: a review. Data Mining and Knowledge Discovery, 33(4):917–963.
6 Lima, A. M. S. (2026). The impact of missing values on anomaly detection tasks in healthcare wearable data. Journal of Information and Data Management. Accepted for publication; to appear.
7 Lima, A. S. (2025). Assessing the impact of missing value mechanisms on anomaly detection in healthcare wearable data. In Anais do XL Simpósio Brasileiro de Bancos de Dados, pages 781–787, Brasil.
8 Lima, A. S. and Sousa, E. (2024). Handling missing values in data streams: An overview. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 750–756, Brasil.
9 Lukats, D., Zielinski, O., Hahn, A., and Stahl, F. (2025). A benchmark and survey of fully unsupervised concept drift detectors on real-world data streams. International Journal of Data Science and Analytics, 19(1):1–31.
10 Mahdi, O. A., Ali, N., Pardede, E., Alazab, A., Al-Quraishi, T., and Das, B. (2024). Roadmap of concept drift adaptation in data stream mining, years later. IEEE Access, 12.
11 Mangussi, A. D., Santos, M. S., Lopes, F. L., Pereira, R. C., Lorena, A. C., and Abreu, P. H. (2024). mdatagen: A python library for generating missing data. https://arthurmangussi.github.io/pymdatagen/
12 Mishra, T., Wang, M., Metwally, A. A., Bogu, G. K., Brooks, A. W., Bahmani, A., Alavi, A., Celli, A., Higgs, E., Dagan-Rosenfeld, O., et al. (2020). Pre-symptomatic detection of covid-19 from smartwatch data. Nature biomedical engineering, 4(12):1208–1220.
13 Montiel, J., Halford, M., Mastelini, S. M., Bolmier, G., Sourty, R., Vaysse, R., Zouitine, A., Gomes, H. M., Read, J., Abdessalem, T., et al. (2021). River: machine learning for streaming data in python. Journal of Machine Learning Research, 22(110):1–8.
14 Ren, L., Wang, T., Seklouli, A. S., Zhang, H., and Bouras, A. (2023). A review on missing values for main challenges and methods. Information Systems, page 102268.
15 Santos, M. S., Pereira, R. C., Costa, A. F., Soares, J. P., Santos, J., and Abreu, P. H. (2019). Generating synthetic missing data: A review by missing mechanism. IEEE Access, 7:11651–11667.