Overview
- Provides multiple examples to facilitate the understanding data streams in non-stationary environments
- Presents several application cases to show how the methods solve different real world problems
- Discusses the links between methods to help stimulate new research and application directions
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Big Data (SBD, volume 41)
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Table of contents (13 chapters)
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Front Matter
Keywords
About this book
This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field.
- Provides multiple examples to facilitate the understanding data streams in non-stationary environments;
- Presents several application cases to show how the methods solve different real world problems;
- Discusses the links between methods to help stimulate new research and application directions.
Editors and Affiliations
About the editor
Moamar Sayed-Mouchaweh received his PhD from the University of Reims-France. He was working as Associated Professor in Computer Science, Control and Signal processing at the University of Reims-France in the Research centre in Sciences and Technology of the Information and the Communication. In December 2008, he obtained the Habilitation to Direct Research (HDR) in Computer science, Control and Signal processing. Since September 2011, he is working as a Full Professor in the High National Engineering School of Mines Telecom Lille Douai (France), Department of Computer Science and Automatic Control. He edited and wrote several Springer books and served as a guest editor of several special issues of international journals. He also served as IPC Chair and conference Chair of several international workshops and conferences. He is serving as a member of the Editorial Board of several international Journals.
Bibliographic Information
Book Title: Learning from Data Streams in Evolving Environments
Book Subtitle: Methods and Applications
Editors: Moamar Sayed-Mouchaweh
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-89803-2
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2019
Hardcover ISBN: 978-3-319-89802-5Published: 09 August 2018
Softcover ISBN: 978-3-030-07862-1Published: 14 December 2018
eBook ISBN: 978-3-319-89803-2Published: 28 July 2018
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: VIII, 317
Number of Illustrations: 36 b/w illustrations, 95 illustrations in colour
Topics: Communications Engineering, Networks, Quality Control, Reliability, Safety and Risk, Data Mining and Knowledge Discovery, Control and Systems Theory