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Call for Contributions on Springer Book: Large-scale Learning from Data Streams in Evolving Environments

25 Avril 2017


Catégorie : Ouvrages


Call for Contributions on Springer Book:

Large-scale Learning from Data Streams in Evolving Environments

Aims and scope

The volume of data is rapidly increasing due to the development of the technology of information and communication. This data comes mostly in the form of streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. In addition, these models must take into account many constraints: (pseudo) real-time processing, high-velocity, and dynamic multi-form change such as concept drift and novelty. Consequently, learning from streams of evolving and unbounded data requires developing new algorithms and methods able to learn under the following constraints: -) random access to observations is not feasible or it has high costs, -) memory is small with respect to the size of data, -) data distribution or phenomena generating the data may evolve over time, which is known as concept drift and -) the number of classes may evolve overtime. Therefore, efficient data streams processing requires particular drivers and learning techniques:

  • Incremental learning in order to integrate the information carried by each new arriving data;
  • Decremental learning in order to forget or unlearn the data samples which are no more useful;
  • Novelty detection in order to learn new concepts.

It is worthwhile to emphasize that streams are very often generated by distributed sources, especially with the advent of Internet of Things and therefore processing them centrally may not be efficient especially if the infrastructure is large and complex. Scalable and decentralized learning algorithms are potentially more suitable and efficient.

This edited Springer book aims to present a set of collected works that address the problem of learning from data streams in evolving environments. The scope of this book covers the following, but not limited to:

  • Online and incremental learning
  • Online classification, clustering and regression
  • Online dimension reduction
  • Data drift and shift handling
  • Novelty detection
  • Online active and semi-supervised learning
  • Online transfer learning
  • Adaptive data pre-processing and knowledge discovery
  • Applications in
    • Monitoring
    • Quality control
    • Fault detection, isolation and prognosis
    • Internet analytics
    • Decision Support Systems
    • Smart energy management
    • Smart grid
    • Smart cities
    • Dynamic demand response operation in micro grids
    • etc.

Please submit a tentative title, names and affiliation of authors and a brief abstract of your proposed contribution in PDF format online via the Easychair submission interface (https://easychair.org/conferences/?conf=learnstream2017).

Important dates

Abstract Deadline: As soon as possible

Chapter submission deadline: July 15, 2017

Notification of acceptance: September 15, 2017

Camera-ready submission deadline: November 1, 2017

Book Editors

Moamar Sayed-Mouchaweh
Computer Science and Automatic Control Labs, High Engineering School of Mines, Douai, France
moamar.sayed-mouchaweh@mines-douai.fr

João Gama
Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal
jgama@fep.up.pt

Hamid Bouchachia
Department of Computing & Informatics, University of Bournemouth, Bournemouth, UK
abouchachia@bournemouth.ac.uk

Edwin Lughofer
University of Linz, Austria
Edwin.Lughofer@jku.at