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

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-89802-5
  • Online ISBN 978-3-319-89803-2
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site