BDASD 2017 : Special Session on Big Data Analytics and Stream Data Mining

Posted on December 14, 2016 in Conferences
Submission Deadline Sunday 22 Jan 2017  ( Deadline extended ? Click here to edit )
Conference & Submission Link http://ismis2017.ii.pw.edu.pl/s_big_data.php
Conference Dates Jun 26, 2017 - Jun 29, 2017
Conference Address Warsaw, Poland
Proceedings indexed by

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

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