Data stream analysis: Foundations, major tasks and tools

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Bahri, M (Author) Other; Bifet, A (Author) Other; João Gama (Author) FEP; Gomes, HM (Author) Other; Maniu, S (Author) Other

Journal

Title: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Vol. 11 No. 3

Initial page: 1405

ISSN: 1942-4787

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 63 Citations

Scopus - 83 Citations

Other information

Authenticus ID: P-00T-J8G

DOI: 10.1002/widm.1405

Abstract (EN): 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

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 17