Dear colleagues and friends, Due to many requests for extension, we have extended this SI for two weeks.
we apology for possible cross posting, and thank you for distributing this CFP --- *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 15, 2016 (firm, extended) 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: [email protected] 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, [email protected]) or Dr Shui Yu ([email protected] ) -- ----------------------------- 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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