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---

*IEEE Assess is the most visible journal of IEEE, which will bring
significant citation of your work.*



*Theoretical Foundations for Big Data Applications: Challenges and
Opportunities (*
*http://www.ieee.org/publications_standards/publications/ieee_access/theoretical_foundations_for_big_data.pdf*
<http://www.ieee.org/publications_standards/publications/ieee_access/theoretical_foundations_for_big_data.pdf>*
)*



Submission deadline: March 1, 2016



It is critical to explore theoretical perspective of Big Data to
efficiently and effectively guide its applications. We have witnessed the
significant development in Big Data from various communities, such as the
mining and learning algorithms from the artificial intelligence community,
networking facilities from networking community, and software platforms for
software engineering community. However, Big Data applications introduce
unprecedented challenges to us, and existing theories and techniques have
to be extended, upgraded to serve the forthcoming real Big Data
applications, we even need to invent new tools for Big Data applications.
We desperately desire theoretical work from various disciplines, such as
statistics, machine learning, graph theory, networking, parallel computing,
security and privacy, and so on.



The purpose of this special section is to solicit the latest theoretical
research outputs for Big Data applications. We prefer survey or tutorial
style articles with clear application background for this special section.
The areas of interest include, but are not limited to, the following.



-               Measurement for Big Data

-               Mathematical representation for Big Data

-               Statistics for Big Data

-               Mining and learning theory for Big Data

-               Networking theory for Big Data

-               Security and privacy theory for Big Data

-               Data compression for Big Data

-               Parallel and distributed algorithms for Big Data

-               Software platform design for Big Data

-               Scheduling theory for Big Data

-               Performance modelling for Big Data tools

-               Theoretical challenges in Big Data

-               Theoretical solutions for Big Data

-               Data management for Big Data



We highly recommend the submission of multimedia with each article as it
significantly increases the visibility and usage of articles.



Associate Editor: Dr Shui Yu, Deakin University, Australia. Email:
[log in to unmask]

Guest Editors:

1) Dr Chonggang Wang, Member Technical Staff, InterDigital Communications,
USA

2) Prof Ke Liu, Director of Division of Computer Science, National Natural
Science Foundation of China

3) Prof Albert Y. Zomaya, School of Information Technologies, The
University of Sydney, Australia



IEEE *Access *Editor in Chief: Michael Pecht, Professor and Director,
CALCE, University of Maryland



Paper submission: Contact Associate Editor and submit manuscript to:
http://mc.manuscriptcentral.com/ieee-access



For information regarding IEEE *Access *including its publication policy
and fees, please visit the website http://ieee.org/ieee-access

For Inquiries regarding this special section, please contact: Bora M. Onat,
Managing Editor, IEEE *Access *(Phone: (732) 562-6036, [log in to unmask])
or Dr Shui Yu ([log in to unmask] )

-- 

-----------------------------

Shui YU, PhD, Senior Lecturer

School of Information Technology, Deakin University,

221 Burwood Highway, Burwood, VIC 3125,  Australia.

Telephone:0061 3 9251 7744

http://www.deakin.edu.au/~syu

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