======================================================================
(Distinguished papers presented at the workshop, after further extension and
revision, will appear in a Special Issue of the SCI-indexed Journal
- Journal of Computer Science and Technology (JCST), Springer-
http://jcst.ict.ac.cn:8080/jcst/EN/volumn/home.shtml)
======================================================================

In conjunction with
IEEE International Conference on Data Mining (ICDM 2013)
(http://icdm2013.rutgers.edu/)


Introduction:
-------------

Big data refers to datasets that exceed the competence of commonly used
IT systems in terms of processing space and/or time. Traditionally,
massive data are mostly produced in scientific fields such as astronomy,
meteorology, genomics physics, biology, and environmental research. Due
to the rapid development of IT technology and the consequent decrease of
cost on collecting and storing data, big data has been generated from
almost every industry and sector as well as governmental department,
including retail, finance, banking, security, audit, electric power,
healthcare, to name a few. Recently, big data over the Web (big Web data
for short), which includes all the context data, such as, user generated
contents, browser/search log data, deep web data, etc., have attracted
extensive interests, as these context data and their analyses help us to
understand what is happening in real life. This can help to enable new
ways for improving user experience by providing more accurate predictions
and recommendations thus creating a personalized smarter internet.

Currently, big data is often on the order of petabytes and even exabytes.
However, big data has become bigger and bigger not only in its size, but
also in its growth rate and variety. The volume of big data often grows
exponentially or even in rates that overwhelm the well-known Moore’s Law.
Meanwhile, big data has been extended from traditional structured data
into semi-structured and completely unstructured data of different types,
such as text, image, audio, video, click streams, log files, etc.
Moreover, big data is often internally interconnected and thus form complex
data/information networks.

Although big data can offer us unprecedented opportunities, they also pose
many grand challenges. Due to the massive volume and inherent complexity,
it is extremely difficult to store, aggregate, manage, and analyze big data
and finally mine valuable information/knowledge from the complex data/
information networks. Therefore, in the presence of big data, the models,
algorithms and methods for traditional data mining become no longer
effective and efficient. For instance, similarity learning, upon which
various similarity-based tasks (e.g., ranking and clustering) can be launched,
is extremely challenging for real applications with big data due to their
typical features such as the data being heterogeneous, time-evolving, sparse
and noisy. On the other hand, some data is generated exponentially or super-
exponentially in a streaming manner. Therefore, how to carry out real-time
analysis on, and deep mining and understanding from big data so as to obtain
dynamical and incremental information/knowledge, is another grand challenge.
In general, at the era of big data, it is expected to develop new models,
algorithms, methods, and even paradigms for mining, analyzing, and
understanding big data.

This workshop aims to provide a networking venue that will bring together
scientists, researchers, professionals, and practitioners from both industry
and academia and from different disciplines (including computer science,
social science, network science, etc.) to exchange ideas, discuss solutions,
share experiences, promote collaborations, and report state-of-the-art
research results and technological innovations on various aspects of mining
and understanding from big data.


Scope and Topics:
-----------------

The topics of interest include, but are not limited to:

- Acquisition, representation, indexing, storage, and management of big data
- Processing, pre-processing, and post-processing of big data
- Models, algorithms, and methods for big data mining and understanding
- Knowledge discovery and semantic-based mining from big data
- Metric/similarity learning for big data
- Visualizing analytics and organization for big data
- Context data mining from big Web data
- Social computing over big Web data (e.g., network analysis, community
detection)
- Industrial and scientific applications of big data mining such as search
and recommendations


Important Dates:
----------------

Submission Deadline: August 3, 2013
Authors Notification: September 24, 2013
Workshop Date: December 8, 2013


Paper Submission Guideline:
---------------------------

All papers need to be submitted electronically through the conference website
(https://wi-lab.com/cyberchair/2013/icdm13/scripts/submit.php?subarea=DM) with
PDF format. The materials presented in the papers should not be published or be
under submission elsewhere. Each paper is limited to 8 pages including figures
and references and follows the IEEE ICDM format requirements
(http://icdm2013.rutgers.edu/author-instructions).

Once accepted, the paper will be included into the conference proceedings published
by IEEE Computer Society Press (indexed by EI). At least one of the authors of
any accepted paper is requested to register the paper at the workshop.

Distinguished papers presented at the workshop, after further extension and
revision, will appear in a Special Issue of the SCI-indexed Journal, namely,
Journal of Computer Science and Technology (JCST), Springer
(http://jcst.ict.ac.cn:8080/jcst/EN/volumn/home.shtml).


Workshop Co-Chairs:
--------------------

- Xueqi Cheng, Institute of Computing Technology, CAS, China, cxq@ict.ac.cn
- Alvin Chin, Nokia, China, alvin.chin@nokia.com
- Charles X. Ling, Western University, Canada, cling@csd.uwo.ca
- Fei Wang, IBM T. J. Watson Research Center, USA, fwang@us.ibm.com


Organizing committee:
---------------------

Jilei Tian Nokia Research Center, China
Guanling Chen University of Massachusetts Lowell, USA,
Enhong Chen University of Science and Technology of China
Jun Wang IBM T.J. Watson Research Center
Peng Cui Tsinghua University, China
Irwin King Chinese University of Hong Kong, China