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research-article

Machine learning for streaming data: state of the art, challenges, and opportunities

Authors:

Heitor Murilo Gomes,

Jean Paul Barddal,

João GamaAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 21, Issue 2

Pages 6 - 22

https://doi.org/10.1145/3373464.3373470

Published: 26 November 2019 Publication History

Abstract

Incremental learning, online learning, and data stream learning are terms commonly associated with learning algorithms that update their models given a continuous influx of data without performing multiple passes over data. 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. This work aims to evoke discussion and elucidate the current research opportunities, highlighting the relationship of different subareas and suggesting courses of action when possible.

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Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 21, Issue 2

December 2019

100 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/3373464

Editors:
Hanghang Tong
Arizona State University
,
Xin Luna Dong
Google
,
Ankur Teredesai
University of Washington Tacoma
,
Reza Zafarani
Syracuse University

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Published: 26 November 2019

Published in SIGKDD Volume 21, Issue 2

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Pullanikkattil SYerolla RBesta C(2025)Interpretable Machine learning model for predicting Ethane-Ethylene composition in binary distillation processThermal Science and Engineering Progress10.1016/j.tsep.2025.10323658(103236)Online publication date: Feb-2025
https://doi.org/10.1016/j.tsep.2025.103236
Azeem AIsmail IMohani SDanyaro KHussain UShabbir SBin Jusoh R(2025)Mitigating concept drift challenges in evolving smart grids: An adaptive ensemble LSTM for enhanced load forecastingEnergy Reports10.1016/j.egyr.2024.12.07813(1369-1383)Online publication date: Jun-2025
https://doi.org/10.1016/j.egyr.2024.12.078
Botacin MGomes H(2025)Towards more realistic evaluations: The impact of label delays in malware detection pipelinesComputers & Security10.1016/j.cose.2024.104122148(104122)Online publication date: Jan-2025
https://doi.org/10.1016/j.cose.2024.104122
Chambers LGaber MGhomeshi H(2025)Deepstreamensemble: streaming adaptation to concept drift in deep neural networksInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02492-xOnline publication date: 4-Jan-2025
https://doi.org/10.1007/s13042-024-02492-x
Leveni FCassales GPfahringer BBifet ABoracchi GSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Online isolation forestProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693158(27288-27298)Online publication date: 21-Jul-2024
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Ahmad IWan ZAhmad AUllah S(2024)A Hybrid Optimization Model for Efficient Detection and Classification of Malware in the Internet of ThingsMathematics10.3390/math1210143712:10(1437)Online publication date: 7-May-2024
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Han MLi CMeng FHe FZhang R(2024)An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept DriftApplied Sciences10.3390/app1416717614:16(7176)Online publication date: 15-Aug-2024
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Mahapatra SChandola V(2024)Learning manifolds from non-stationary streamsJournal of Big Data10.1186/s40537-023-00872-811:1Online publication date: 23-Mar-2024
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