Article Dans Une Revue Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery Année : 2021

Data stream analysis: Foundations, major tasks and tools

Résumé

The significant growth of interconnected Internet-of-Things (IoT) devices, the use of social networks, along with the evolution of technology in different domains, lead to a rise in the volume of data generated continuously from multiple systems. Valuable information can be derived from these evolving data streams by applying machine learning. In practice, several critical issues emerge when extracting useful knowledge from these potentially infinite data, mainly because of their evolving nature and high arrival rate which implies an inability to store them entirely. In this work, we provide a comprehensive survey that discusses the research constraints and the current state-of-the-art in this vibrant framework. Moreover, we present an updated overview of the latest contributions proposed in different stream mining tasks, particularly classification, regression, clustering, and frequent patterns.

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Dates et versions

hal-03190000 , version 1 (05-04-2021)

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Maroua Bahri, Albert Bifet, João Gama, Heitor Murilo Gomes, Silviu Maniu. Data stream analysis: Foundations, major tasks and tools. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2021, 11 (3), pp.e1405. ⟨10.1002/widm.1405⟩. ⟨hal-03190000⟩
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