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 state-of-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, high-lighting the relationship of different subareas and suggesting courses of action when possible.

Heitor Murilo Gomes, Jesse Read, Albert Bifet, Jean Paul Barddal, João Gama: Machine learning for streaming data: state of the art, challenges, and opportunities. SIGKDD Explorations 21(2): 6-22 (2019)

Paper at Research Gate



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