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Several works have been devoted to this area, either directly or indirectly as characteristics of big data processing, i.e., Velocity and Volume. Given the current industry needs, there are many challenges to be addressed before existing methods can be efficiently applied to real-world problems. In this work, we focus on elucidating the connections among the current stateof- the-art on related fields; and clarifying open challenges in both academia and industry. We treat with special care topics that were not thoroughly investigated in past position and survey papers. 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In ICML, volume 99, pages 200--209, 1999."},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-008-0053-8"},{"key":"e_1_2_1_69_1","first-page":"16","volume-title":"IJCAI Workshop on Learning from Temporal and Spatial Data","author":"Klinkenberg R.","year":"2001","unstructured":"R. Klinkenberg . Using labeled and unlabeled data to learn drifting concepts . In IJCAI Workshop on Learning from Temporal and Spatial Data , pages 16 -- 24 , 2001 . R. Klinkenberg. Using labeled and unlabeled data to learn drifting concepts. In IJCAI Workshop on Learning from Temporal and Spatial Data, pages 16--24, 2001."},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.5555\/951949.952136"},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-013-0340-z"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7840687"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/2674026.2674028"},{"key":"e_1_2_1_74_1","first-page":"390","volume-title":"AIAP'07","author":"Kuncheva L. I.","year":"2007","unstructured":"L. I. Kuncheva . A stability index for feature selection. In International Multi-Conference: Artificial Intelligence and Applications , AIAP'07 , pages 390 -- 395 , 2007 . L. I. Kuncheva. A stability index for feature selection. In International Multi-Conference: Artificial Intelligence and Applications, AIAP'07, pages 390--395, 2007."},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46128-1_6"},{"key":"e_1_2_1_76_1","volume-title":"Vowpal Wabbit","author":"Langford J.","year":"2007","unstructured":"J. Langford , L. Li , and A. Strehl . Vowpal Wabbit , 2007 . J. Langford, L. Li, and A. Strehl. Vowpal Wabbit, 2007."},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23241-1_10"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCSW.2007.35"},{"key":"e_1_2_1_79_1","volume-title":"Ray rllib: A composable and scalable reinforcement learning library. arXiv preprint arXiv:1712.09381","author":"Liang E.","year":"2017","unstructured":"E. Liang , R. Liaw , R. Nishihara , P. Moritz , R. Fox , J. Gonzalez , K. Goldberg , and I. Stoica . 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Bifet, T. Abdessalem, E. Ayguade, and J. Herrero. Echo state hoeffding tree learning. In R. J. Durrant and K.-E. Kim, editors, Asian Conference on Machine Learning, volume 63, pages 382--397, 2016."},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2010.61"},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.152"},{"key":"e_1_2_1_86_1","first-page":"2886","volume-title":"Advances in Neural Information Processing Systems","author":"Mitliagkas I.","year":"2013","unstructured":"I. Mitliagkas , C. Caramanis , and P. Jain . Memory limited, streaming pca . In Advances in Neural Information Processing Systems , pages 2886 -- 2894 , 2013 . I. Mitliagkas, C. Caramanis, and P. Jain. Memory limited, streaming pca. In Advances in Neural Information Processing Systems, pages 2886--2894, 2013."},{"issue":"72","key":"e_1_2_1_87_1","article-title":"Scikit-multiflow: A multi-output streaming framework","volume":"19","author":"Montiel J.","year":"2018","unstructured":"J. Montiel , J. Read , A. Bifet , and T. Abdessalem . Scikit-multiflow: A multi-output streaming framework . Journal of Machine Learning Research , 19 ( 72 ), 2018 . J. Montiel, J. Read, A. Bifet, and T. Abdessalem. Scikit-multiflow: A multi-output streaming framework. Journal of Machine Learning Research, 19(72), 2018.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_88_1","volume-title":"Learn++.nc: Combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes","author":"Muhlbaier M. D.","year":"2009","unstructured":"M. D. Muhlbaier , A. Topalis , and R. Polikar . Learn++.nc: Combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes . IEEE transactions on neural networks, 20(1):152--168, 2009 . M. D. Muhlbaier, A. Topalis, and R. Polikar. Learn++.nc: Combining ensemble of classifiers with dynamically weighted consult-and-vote for efficient incremental learning of new classes. IEEE transactions on neural networks, 20(1):152--168, 2009."},{"key":"e_1_2_1_89_1","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/7.4.308"},{"key":"e_1_2_1_90_1","first-page":"1","volume-title":"P.-N. Tan, S. Chawla","author":"Nguyen H.-L.","year":"2012","unstructured":"H.-L. Nguyen , Y.-K. Woon , W.-K. Ng , and L. Wan . Heterogeneous ensemble for feature drifts in data streams . In P.-N. Tan, S. Chawla , C. K. Ho, and J. Bailey, editors, Advances in Knowledge Discovery and Data Mining , pages 1 -- 12 , 2012 . H.-L. Nguyen, Y.-K. Woon, W.-K. Ng, and L. Wan. Heterogeneous ensemble for feature drifts in data streams. In P.-N. Tan, S. Chawla, C. K. Ho, and J. Bailey, editors, Advances in Knowledge Discovery and Data Mining, pages 1--12, 2012."},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46227-1_28"},{"key":"e_1_2_1_92_1","first-page":"3238","volume-title":"Advances in Neural Information Processing Systems","author":"Oliver A.","year":"2018","unstructured":"A. Oliver , A. Odena , C. A. Raffel , E. D. Cubuk , and I. Goodfellow . Realistic evaluation of deep semisupervised learning algorithms. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors , Advances in Neural Information Processing Systems , pages 3238 -- 3249 . 2018 . A. Oliver, A. Odena, C. A. Raffel, E. D. Cubuk, and I. Goodfellow. Realistic evaluation of deep semisupervised learning algorithms. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, pages 3238--3249. 2018."},{"key":"e_1_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSMC.2005.1571498"},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2013.37"},{"key":"e_1_2_1_96_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2012.168"},{"key":"e_1_2_1_97_1","volume-title":"AAAI Conference on Artificial Intelligence","author":"Parker B. S.","year":"2015","unstructured":"B. S. Parker and L. Khan . Detecting and tracking concept class drift and emergence in non-stationary fast data streams . In AAAI Conference on Artificial Intelligence , 2015 . B. S. Parker and L. Khan. Detecting and tracking concept class drift and emergence in non-stationary fast data streams. In AAAI Conference on Artificial Intelligence, 2015."},{"key":"e_1_2_1_98_1","volume-title":"et al. Scikit-learn: Machine learning in python. Journal of machine learning research, 12(Oct):2825--2830","author":"Pedregosa F.","year":"2011","unstructured":"F. Pedregosa , G. Varoquaux , A. Gramfort , V. Michel , B. Thirion , O. Grisel , M. Blondel , P. Prettenhofer , R. Weiss , V. Dubourg , et al. Scikit-learn: Machine learning in python. Journal of machine learning research, 12(Oct):2825--2830 , 2011 . F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. Scikit-learn: Machine learning in python. Journal of machine learning research, 12(Oct):2825--2830, 2011."},{"key":"e_1_2_1_99_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-68125-0_27"},{"key":"e_1_2_1_100_1","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA.2017.8227456"},{"key":"e_1_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1109\/EPIA.2005.341288"},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7840877"},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.01.078"},{"key":"e_1_2_1_104_1","volume-title":"Scalable and efficient multi-label classification for evolving data streams. Machine Learning, 88(1--2):243--272","author":"Read J.","year":"2012","unstructured":"J. Read , A. Bifet , G. Holmes , and B. Pfahringer . Scalable and efficient multi-label classification for evolving data streams. Machine Learning, 88(1--2):243--272 , 2012 . SIGKDD Explorations Volume 21, Issue 1 Page 22 J. Read, A. Bifet, G. Holmes, and B. Pfahringer. Scalable and efficient multi-label classification for evolving data streams. Machine Learning, 88(1--2):243--272, 2012. SIGKDD Explorations Volume 21, Issue 1 Page 22"},{"key":"e_1_2_1_105_1","volume-title":"Multi-label methods for prediction with sequential data. Pattern Recognition, 63(March):45--55","author":"Read J.","year":"2017","unstructured":"J. Read , L. Martino , and J. Hollm\u00b4en . Multi-label methods for prediction with sequential data. Pattern Recognition, 63(March):45--55 , 2017 . J. Read, L. Martino, and J. Hollm\u00b4en. Multi-label methods for prediction with sequential data. Pattern Recognition, 63(March):45--55, 2017."},{"key":"e_1_2_1_106_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5256-5"},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33486-3_58"},{"key":"e_1_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2882948"},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2004.5"},{"key":"e_1_2_1_110_1","doi-asserted-by":"publisher","DOI":"10.1145\/3229329.3229332"},{"key":"e_1_2_1_111_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116895"},{"key":"e_1_2_1_112_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2882946"},{"key":"e_1_2_1_113_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102455"},{"key":"e_1_2_1_114_1","first-page":"9","volume-title":"Genetic and Evolutionary Computation Conference","author":"Stanley K. O.","year":"2002","unstructured":"K. O. Stanley and R. Miikkulainen . Efficient reinforcement learning through evolving neural network topologies . In Genetic and Evolutionary Computation Conference , page 9 , San Francisco , 2002 . K. O. Stanley and R. Miikkulainen. Efficient reinforcement learning through evolving neural network topologies. In Genetic and Evolutionary Computation Conference, page 9, San Francisco, 2002."},{"key":"e_1_2_1_115_1","volume-title":"A berkeley view of systems challenges for ai. arXiv preprint arXiv:1712.05855","author":"Stoica I.","year":"2017","unstructured":"I. Stoica , D. Song , R. A. Popa , D. Patterson , M. W. Mahoney , R. Katz , A. D. Joseph , M. Jordan , J. M. Hellerstein , J. E. Gonzalez , A berkeley view of systems challenges for ai. arXiv preprint arXiv:1712.05855 , 2017 . I. Stoica, D. Song, R. A. Popa, D. Patterson, M. W. Mahoney, R. Katz, A. D. Joseph, M. Jordan, J. M. Hellerstein, J. E. Gonzalez, et al. A berkeley view of systems challenges for ai. arXiv preprint arXiv:1712.05855, 2017."},{"key":"e_1_2_1_116_1","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502568"},{"key":"e_1_2_1_117_1","volume-title":"Introduction to Reinforcement Learning","author":"Sutton R. S.","year":"1998","unstructured":"R. S. Sutton and A. G. Barto . Introduction to Reinforcement Learning . MIT Press , 1 st edition, 1998 . R. S. Sutton and A. G. Barto. Introduction to Reinforcement Learning. MIT Press, 1st edition, 1998.","edition":"1"},{"key":"e_1_2_1_118_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40669-0_33"},{"key":"e_1_2_1_119_1","volume-title":"The problem of concept drift: definitions and related work. Technical report","author":"Tsymbal A.","year":"2004","unstructured":"A. Tsymbal . The problem of concept drift: definitions and related work. Technical report , 2004 . A. Tsymbal. The problem of concept drift: definitions and related work. Technical report, 2004."},{"key":"e_1_2_1_120_1","first-page":"2018","volume-title":"International Conference on Discovery Science","author":"Veloso B.","unstructured":"B. Veloso , J. Gama , and B. Malheiro . Self hyperparameter tuning for data streams . In International Conference on Discovery Science , page to appear, 2018 . B. Veloso, J. Gama, and B. Malheiro. Self hyperparameter tuning for data streams. In International Conference on Discovery Science, page to appear, 2018."},{"key":"e_1_2_1_121_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-014-5441-4"},{"key":"e_1_2_1_122_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2014.123"},{"key":"e_1_2_1_123_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-015-0448-4"},{"key":"e_1_2_1_124_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116900"},{"key":"e_1_2_1_125_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2014.45"},{"key":"e_1_2_1_126_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.08.041"},{"key":"e_1_2_1_127_1","volume-title":"Data mining with big data","author":"Wu X.","year":"2014","unstructured":"X. Wu , X. Zhu , G.-Q. Wu , and W. Ding . Data mining with big data . IEEE transactions on knowledge and data engineering, 26(1):97--107, 2014 . X. Wu, X. Zhu, G.-Q. Wu, and W. Ding. Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1):97--107, 2014."},{"key":"e_1_2_1_128_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.50"},{"key":"e_1_2_1_129_1","first-page":"3350","volume-title":"International Joint Conference on Artificial Intelligence","author":"Yu W.","year":"2017","unstructured":"W. Yu , Y. Gu , and J. Li . Single-pass pca of large highdimensional data . In International Joint Conference on Artificial Intelligence , pages 3350 -- 3356 , 2017 . W. Yu, Y. Gu, and J. Li. Single-pass pca of large highdimensional data. In International Joint Conference on Artificial Intelligence, pages 3350--3356, 2017."},{"key":"e_1_2_1_130_1","doi-asserted-by":"publisher","DOI":"10.1145\/2517349.2522737"},{"key":"e_1_2_1_131_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.39"},{"key":"e_1_2_1_132_1","first-page":"1","volume-title":"Advancing feature selection research. ASU feature selection repository","author":"Zhao Z.","year":"2010","unstructured":"Z. Zhao , F. Morstatter , S. Sharma , S. Alelyani , A. Anand , and H. Liu . Advancing feature selection research. ASU feature selection repository , pages 1 -- 28 , 2010 . Z. Zhao, F. Morstatter, S. Sharma, S. Alelyani, A. Anand, and H. Liu. Advancing feature selection research. ASU feature selection repository, pages 1--28, 2010."},{"key":"e_1_2_1_133_1","first-page":"1453","volume-title":"Artificial intelligence and statistics","author":"Zhou G.","year":"2012","unstructured":"G. Zhou , K. Sohn , and H. Lee . Online incremental feature learning with denoising autoencoders . In Artificial intelligence and statistics , pages 1453 -- 1461 , 2012 . G. Zhou, K. Sohn, and H. Lee. Online incremental feature learning with denoising autoencoders. In Artificial intelligence and statistics, pages 1453--1461, 2012."},{"key":"e_1_2_1_134_1","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1109\/ICDMW.2010.49","volume-title":"IEEE International Conference on Data Mining Workshops","author":"Zliobaite I.","year":"2010","unstructured":"I. Zliobaite . Change with delayed labeling: When is it detectable ? In IEEE International Conference on Data Mining Workshops , pages 843 -- 850 , 2010 . I. Zliobaite. Change with delayed labeling: When is it detectable? In IEEE International Conference on Data Mining Workshops, pages 843--850, 2010."},{"key":"e_1_2_1_135_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-014-5441-4"}],"container-title":["ACM SIGKDD Explorations Newsletter"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3373464.3373470","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3373464.3373470","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:38:17Z","timestamp":1750199897000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3373464.3373470"}},"subtitle":["state of the art, challenges, and opportunities"],"short-title":[],"issued":{"date-parts":[[2019,11,26]]},"references-count":135,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,11,26]]}},"alternative-id":["10.1145\/3373464.3373470"],"URL":"https:\/\/doi.org\/10.1145\/3373464.3373470","relation":{},"ISSN":["1931-0145","1931-0153"],"issn-type":[{"value":"1931-0145","type":"print"},{"value":"1931-0153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,26]]},"assertion":[{"value":"2019-11-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}