Special Session on Big Data Analytics and Stream Data Mining (BDASD2017)
co-located with the 23rd International Symposium
on Methodologies for Intelligent Systems (ISMIS 2017)

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Objectives
==========
In the world of today, modern information systems are able to collect
very large data with inherent and increasing complex structure and
dimensionality. Furthermore, new data sources often provide various
heterogeneous representations and also time changing characteristics
with respect to the data. This is particular visible in the rapidly
developing field of Big Data Analytics. Although machine learning and
data mining researchers had already studied mining massive and
complex data, there are significant differences between earlier
efforts and the current trends opening up new problems and
challenges. Indeed, Big Data Analytics opens up new research problems
which were only considered within a limited range. Applications of
Big Data Analytics may also influence human behavior and society in
a significantly higher degree than before – which also requires new
types of research. Furthermore, new Big Data challenges are
particularly relevant in emerging applications where data are
continuously generated at a high rate in the form of data streams,
whose characteristics may also change with time (concept drifting
data). Compared to static, standard environments, processing data
streams implies new computational challenges and requirements for
algorithms and their ability to adapt to such dynamic and complex
contexts.

In order to address these new research challenges concerning both the
analysis of Big Data and mining data streams, respectively, we
organize this special session. We aim to gather researchers from all over the world
coming from different communities being interested in the
aforementioned issues, as well as to present algorithmic foundations
and application aspects of analyzing these new types of data.

Topics of interest
==================
Suggested topics include (but are not limited to) the following:
* Learning from high-dimensional datasets
* Mining non-standard data representations
* Large-scale link and graph mining
* Scaling up learning algorithms
* Distributed data mining approaches
* Knowledge discovery from ubiquitous environments
* Analysis of data from sensors and social media
* Online learning algorithms.
* Detection and adaptation to concept drift
* Evaluation issues of models learned from evolving data streams
* Classification and clustering in data streams
* Privacy in big and stream data analytics
* Societal aspects of applying Big Data
* Applications, especially in scientific data analysis,
computational social science, medicine, text processing, web mining,
image or multimedia analysis, sensor networks, industrial contexts,
bio-informatics, energy management, and related domains.

Special Session Organizers
==========================
Martin Atzmueller, University of Kassel, Germany
Jerzy Stefanowski, Poznan University of Technology, Poland

Important Dates
===============
Paper submission due: January 22, 2017
Notification of review results: March 14, 2017
Camera ready papers due: April 3, 2017

Proceedings
===========
The accepted papers will be published within the ISMIS main
conference proceedings (Springer LNAI Series).

Paper submission
================
Authors are invited to submit their manuscripts using the Springer
LNCS/LNAI style, with a maximum of 10 pages. Detailed instructions
are provided on the conference homepage.

Paper should be submitted in PDF format via the ISMIS 2017
Online Submission System
https://easychair.org/conferences/?conf=ismis2017