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Data stream analysis: Foundations, major tasks and tools
Author(s) -
Bahri Maroua,
Bifet Albert,
Gama João,
Gomes Heitor Murilo,
Maniu Silviu
Publication year - 2021
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1405
Subject(s) - data stream mining , computer science , data science , cluster analysis , key (lock) , state (computer science) , knowledge extraction , volume (thermodynamics) , data mining , the internet , big data , data stream , machine learning , world wide web , telecommunications , physics , computer security , algorithm , quantum mechanics
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 . This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining