Bahri, M., Bifet, A., Gama, J., Gomes, H. M., & Maniu, S, Data stream analysis: Foundations, major tasks and tools. Wiley nterdisciplinary Reviews: Data Mining and Knowledge Discovery, doi: 10.1002/widm.1405, 11 (3), Wiley nterdisciplinary Reviews, 2021
Bifet, A., Gavalda, R., Holmes, G., & Pfahringer, B, Machine learning for data streams: with practical examples in MOA., 978-0-262-03779-2, MIT Press, 201
Gomes, H. M., Read, J., Bifet, A., Barddal, J. P., & Gama, J, Machine learning for streaming data: state of the art, challenges, and opportunities, doi: 10.1145/3373464.3373470, 21(2), 6-22, ACM SIGKDD Explorations Newsletter, 2019
Hoi, S. C., Sahoo, D., Lu, J., & Zhao, P., Online learning: A comprehensive survey, doi: 10.1016/j.neucom.2021.04.112, Volume 459, 12 October 2021, Pages 249-289, Neurocomputing, 2021
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V., Federated learning: Challenges, methods, and future directions, doi: 10.1109/MSP.2020.2975749, Volume: 37 Issue: 3, IEEE signal processing magazine, 2020
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G, Learning under concept drift: A review., doi: 10.1109/TKDE.2018.2876857, Volume: 31, Issue: 12,, IEEE Transactions on Knowledge and DataEngineering, 2019
Orabona, F., A modern introduction to online learning, arXiv:1912.13213, arXivpreprint, 2019
Gama, J., Žliobait, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A., A survey on concept drift adaptation, doi: 10.1145/2523813, Vol. 46, No. 4, ACM computing surveys(CSUR), 2014
Bahri, M., Bifet, A., Gama, J., Gomes, H. M., & Maniu, S, Data stream analysis: Foundations, major tasks and tools. Wiley nterdisciplinary Reviews: Data Mining and Knowledge Discovery, doi: 10.1002/widm.1405, 11 (3), Wiley nterdisciplinary Reviews, 2021