Connectionists: (extended deadline) ESANN special session about Streaming data analysis, concept drift and analysis of dynamic data sets

[Frank-Michael Schleif]

-- Apologies in advance for multiple postings --

        Call for Papers

Special Session
             on

'Streaming data analysis, concept drift and analysis of dynamic data sets '
24-26 April 2019, Bruges, Belgium
https://www.elen.ucl.ac.be/esann/index.php?pg=specsess#streaming


AIMS AND SCOPE

Today many real life data are given in the form of streaming data.
Prominent examples can be found in the context of IoT, in form of
twitter feeds, click stream data, trading data and many other.

Learning from this huge, heterogeneous and growing amount of data
requires flexible learning models
that can adapt over time and are capable to deal with potentially non-i.i.d.,
non-stationary input data. Additionally the underlying algorithms aim on
processing of high-velocity and multi-channel data and have also to deal
with a variety of phenomena like concept drift and novelty detection.

This special session welcomes novel research about learning from data streams
addressing common problem in the field of streaming data analysis.

Computational intelligence methods have the potential to be used for
efficient data streams processing but novel methods and mathematical and
algorithmic approaches are needed.

TOPICS
We encourage submission of papers on novel methods for streaming data processing
and streaming data analysis by means of computational intelligence and
machine learning approaches,
including but not limited to:

    - data analysis and pattern recognition approaches for streaming data
    - preprocessing approaches for streaming data
    - learning of heterogeneous data streams
    - adaptive data pre-processing and knowledge discovery
    - methods employing ex- and implicit data knowledge for non-stationary data
    - representation and modeling of multi-channel streaming data
    - approximation techniques for streaming data
    - online and incremental learning (dimensionality reduction,
classification, clustering and regression, outlier detection)
      with a particular design for streaming data
    - data drift and shift handling, transfer learning
    - graph stream algorithms
    - security and privacy preservation on streaming data
    - active learning for data streams
    - application of deep learning with streaming data
    - particular interesting applications for streaming data analysis
      e.g. in IoT, recommender systems, social networks, sensor
networks, web mining, text processing medicine ...

IMPORTANT DATES
Paper submission deadline : 26 November 2018
Notification of acceptance : 31 January 2019
Deadline for final papers : 20 February 2019
The ESANN 2019 conference : 24-26 April 2019

SPECIAL SESSION ORGANIZERS:
Albert Bifet LTCI, Télécom ParisTech - Université Paris-Saclay Paris, FRANCE
Barbara Hammer, University of Bielefeld, Germany
Frank-Michael Schleif, University of Appl. Sc. Wuerzburg-Schweinfurt,
Germany and University of Birmingham, Birmingham, UK


-- 
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Prof. Dr. rer. nat. habil. Frank-Michael Schleif
School of Computer Science
University of Applied Sciences Würzburg-Schweinfurt
Sanderheinrichsleitenweg 20
Raum I-3.35
Tel.: +49(0) 931 351 18127
97074 Würzburg

Honorable Research Fellow
The University of Birmingham
Edgbaston
Birmingham B15 2TT
United Kingdom
-
email: frank-michael.schleif at fhws.de
http://promos-science.blogspot.de/
https://www.techfak.uni-bielefeld.de/~fschleif/
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