© 2019

Learning from Data Streams in Evolving Environments

Methods and Applications

  • Moamar Sayed-Mouchaweh

Part of the Studies in Big Data book series (SBD, volume 41)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Moamar Sayed-Mouchaweh
    Pages 1-12
  3. Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, Khaled Ghédira
    Pages 39-61
  4. Markus Endres, Johannes Kastner, Lena Rudenko
    Pages 63-91
  5. Qing Xie, Chaoyi Pang, Xiaofang Zhou, Xiangliang Zhang, Ke Deng
    Pages 93-122
  6. Hossein Ghomeshi, Mohamed Medhat Gaber, Yevgeniya Kovalchuk
    Pages 123-153
  7. Fabíola S. F. Pereira, Shazia Tabassum, João Gama, Sandra de Amo, Gina M. B. Oliveira
    Pages 155-176
  8. Nicolas Kourtellis, Gianmarco De Francisci Morales, Albert Bifet
    Pages 177-207
  9. Sohei Okui, Kaho Osamura, Akihiro Inokuchi
    Pages 223-246
  10. Abdulhakim Qahtan, Suojin Wang, Xiangliang Zhang
    Pages 247-278
  11. Isah Abdullahi Lawal
    Pages 279-296
  12. Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck
    Pages 297-317

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.


Machine Learning Neural Networks and Learning Systems Artificial Intelligence Data streams in non-stationary environments Concept drift and concept evolution in data streams

Editors and affiliations

  • Moamar Sayed-Mouchaweh
    • 1
  1. 1.Institute Mines-Telecom Lille DouaiDouaiFrance

About the editors

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

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