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MUSE 2014 : Mining Ubiquitous and Social Environments

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Link: https://www.kde.cs.uni-kassel.de/ws/muse2014
 
When Sep 15, 2014 - Sep 15, 2014
Where Nancy, France
Abstract Registration Due Jun 13, 2014
Submission Deadline Jun 20, 2014
Notification Due Jul 11, 2014
Final Version Due Jul 25, 2014
Categories    social media   data mining   social networks   social computing
 

Call For Papers

** Please forward to anyone who might be interested **
--------------------------------------------------------------------------


CALL FOR PAPERS
for the 5th International ECML/PKDD 2014 Workshop
on


*MINING UBIQUITOUS AND SOCIAL ENVIRONMENTS*
* Big Data Analytics *
(MUSE 2014)


http://www.kde.cs.uni-kassel.de/ws/muse2014
September 15th, 2014 - Nancy, France
*** Abstract submission: June 13th, 2014 ***
*** Paper submission deadline: June 20th, 2014 ***
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The emergence of ubiquitous computing has started to create
new environments consisting of small, heterogeneous, and
distributed devices that foster the social interaction of
users in several dimensions. Similarly, the upcoming social
web also integrates the user interactions in social networking
environments.

In typical ubiquitous settings, the mining system can be
implemented inside the small devices and sometimes on central
servers, for real-time applications, similar to common mining
approaches. However, the characteristics of ubiquitous and
social mining in general are quite different from the current
mainstream data mining and machine learning. Unlike in
traditional data mining scenarios, data does not emerge from
a small number of (heterogeneous) data sources, but potentially
from hundreds to millions of different sources. Often there is
only minimal coordination and thus these sources can overlap
or diverge in many possible ways. Steps into this new and
exciting application area are the analysis of this new data,
the adaptation of well known data mining and machine learning
algorithms and finally the development of new algorithms.

Mining big data in ubiquitous and social environments is an
emerging area of research focusing on advanced systems for
data mining in such distributed and network-organized systems.
Therefore, for this workshop, we aim to attract researchers from
all over the world working in the field of data mining and
machine learning with a special focus on analyzing big data
in ubiquitous and social environments.


GOALS AND AUDIENCE
==================
The goal of this workshop is to promote an interdisciplinary
forum for researchers working in the fields of ubiquitous
computing, mobile sensing, social web, Web 2.0, and social
networks which are interested in utilizing data mining in a
ubiquitous setting. The workshop seeks for contributions
adopting state-of-the-art mining algorithms on ubiquitous
social data. Papers combining aspects of the two fields are
especially welcome. In short, we want to accelerate the
process of identifying the power of advanced data mining
operating on data collected in ubiquitous and social
environments, as well as the process of advancing data
mining through lessons learned in analyzing these new data.

TOPICS OF INTEREST
==================
The topics of the workshop are split roughly into four areas which
include, but are not limited to the following topics:

Ubiquitous Mining:
* Analysis of data from sensors and mobile devices
* Resource-aware algorithms for distributed mining
* Scalable and distributed classification, prediction, and clustering
algorithms
* Activity recognition
* Mining continuous streams and ubiquitous data
* Online methods for mining temporal, spatial and spatio-temporal data
* Combining data from different sources

Mining Social Data:
* Analysis of social networks and social media
* Mining techniques for social networks and social media
* Algorithms for inferring semantics and meaning from social data
* How social data can be used to mine and create collective intelligence
* Individual and group behavior in social media and social networks
* Social networks for the collaboration of large communities
* Modeling social behavior
* Novel techniques for mining big data from social media
* Dynamics and evolution patterns of social networks

Ubiquitous and Social Mining
* Personalization and recommendation
* User models and predicting user behavior
* User profiling in ubiquitous and social environments
* Network analysis of social systems
* Discovering social structures and communities
* Mobility mining
* Link prediction
* Analysis of data from crowd-sourcing approaches

Applications:
* Discovering misuse and fraud
* Usage and presentation interfaces for mining and data collection
* Analysis of social and ubiquitous games
* Privacy challenges in ubiquitous and social applications
* Recommenders in ubiquitous and social environments
* Applications of any of the above methods and technologies

We also encourage submissions which relate research results from
other areas to the workshop topics.

SPRINGER BOOK
=============
As in the previous years, it is planned to publish revised selected papers
as a volume in the Springer LNCS/LNAI series

WORKSHOP ORGANIZERS
=============================
* Martin Atzmueller, Knowledge and Data Engineering Group,
University of Kassel, Germany (atzmueller@cs.uni-kassel.de)
* Christoph Scholz, Knowledge and Data Engineering Group,
University of Kassel, Germany (scholz@cs.uni-kassel.de)

PROGRAM COMMITTEE
==================
* Christian Bauckhage, Fraunhofer IAIS, Germany
* Albert Bifet, University of Waikato, New Zealand.
* Ulf Brefeld, TU Darmstadt, Germany
* Ciro Cattuto, ISI Foundation, Italy
* Michelangelo Ceci, University of Bari, Italy
* Joao Gama, University Porto, Portugal
* Andreas Hotho, University of Wuerzburg, Germany
* Kristian Kersting, TU Dortmund University, Germany
* Florian Lemmerich, University of Wuerzburg, Germany
* Dunja Mladenic, Jozef Stefan Institute, Slovenia
* Ion Muslea, SDL Research Labs, USA
* Rasmus Pedersen, Copenhagen Business School, Denmark
* Gerd Stumme, University of Kassel, Germany



SUBMISSIONS AND STYLE
=====================

We invite two types of submissions for this workshop:
* Technical papers in any of the topics of interest of the workshop
(but not limited to them)
* Short position papers in any of the topics of interest of the workshop
(but not limited to them)

Submitted papers will be peer-reviewed and selected on the basis of
these reviews. Accepted papers will be presented at the workshop.

Format requirements for submissions of papers are: Maximum 16
pages, including title page and bibliography for technical
papers. Maximum 8 pages, including title page and bibliography
for short position papers.

All submissions must be entered into the reviewing system:

https://www.easychair.org/conferences/?conf=muse2014

If you have any question please contact the MUSE Organizers. We
recommend to follow the format guidelines of ECML/PKDD (Springer
LNCS), as this will be the required format for accepted papers.

More details can be found on the workshop website:
http://www.kde.cs.uni-kassel.de/ws/muse2014

Important Dates
===============
* Abstract Submission: June 13th, 2014
* Paper Submission Deadline: June 20th, 2014
* Author Notification: July 11th, 2014
* Camera Ready Papers: July 25th, 2014

Related Resources

ECML-PKDD 2017   European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
ICDM 2017   IEEE International Conference on Data Mining 2017
DSAA 2017   The 4th IEEE International Conference on Data Science and Advanced Analytics 2017
IMIS 2017   The 11th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing
KDD 2017   Call for Research Papers
UbiComp 2017   2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing
AJHSS 1-2 2017   Austrian Journal of Humanities and Social Sciences
ISMIS 2017   23rd International Symposium on Methodologies for Intelligent Systems
AIM@EPIA 2017   Artificial Intelligence in Medicine
IE 2017   13th International Conference on Intelligent Environments (IE’17)