[AISWorld] 2nd CFP SI Big Data Analytics: Fundamentals and applications in J NMNC

Zhaohao Sun zhaohao.sun at gmail.com
Thu Apr 2 21:14:03 EDT 2015


Special Issue of the Journal of New Mathematics & Natural Computing

Big Data Analytics: Fundamentals and applications

The Journal of New Mathematics & Natural Computing is published by World
Scientific Publishing (http://www.worldscientific.com/worldscinet/nmnc)
with ISSN:1793-0057 [print] and ISSN:1793-7027 [electronic]. This journal
is covered in Cabell's Computer Science directory, and is abstracted and
indexed in Scopus, Mathematical Reviews, Zentralblatt MATH, RePEC, and
listed by ERA Australia.

Big data have become a strategic resource for industry, business, and
national security. Big Data are also a key enabler of exploring business
insights and economics of services. Big Data are generated from various
instruments, sensors, Internet transactions, emails, videos, click streams,
social networking services and other sources, characterized with at least
eight bigs: big volume, big velocity, big variety, big value, big veracity,
big challenge, big opportunity and big difficulty to manage using
traditional (database) tools and technologies.

Big Data Analytics has been drawing increasing attention in academia of
computer science, information technology, mathematics, business, management
and industry of healthcare, medical science. Big Data Analytics is an
emerging science and technology armed by thoroughly multidisciplinary
advancement of information and communication technology (ICT), mathematics,
operations research (OR), and decision sciences for big data. The main
components of Big Data Analytics include descriptive analytics, predictive
analytics and prescriptive analytics, which correspondingly address the
three questions in related to big data: when and what happened? What is
likely to happen? And what should happen with the best outcome under
uncertainty? All these questions are often encountered in almost every part
of science, technology, business, management, organization and industry.
Mathematics, optimization, machine learning, data mining, cloud computing,
statistical modelling, as well as visualization technology, to name a few,
are proved fundamentals for research and development of Big Data Analytics.

Big Data Analytics generates new opportunities and challenges for the
research and development of business, management, decision science,
mathematics and ICT including machine learning, visualization techniques.
Big Data Analytics with applications are changing the world and society,
how people and organizations are doing things and making decisions in the
future. Big Data Analytics is also changing business models, revenue
models, management and decision making processes in companies and
organizations, and affecting usage of resources including data in creating
products and services.

There is significant need for better understanding of Big Data Analytics
through an in-depth investigation of mathematics, optimization, statistical
modelling, machine learning, data mining, cloud computing and visualization
technology for big data with applications because much of fundamental
research in big data analytics remains to be done at the moment.
1       Objective and topics

The objective of this Special Issue in *Journal of New Mathematics and
Natural Computing* is to present the current state of art research and
practical experiences on Big Data Analytics from a viewpoint of
mathematics, statistics, graph theory, optimization, ICT, intelligent
systems, machine learning, decision science and economics and beyond.

Topics of interest include, but are not limited to, the following:

1.      Fundamentals of Big Data Analytics

·         Big Data Analytics  as a science

·         Big Data Analytics  as a technology

·         Big Data Analytics  as a service

·         Big Data Science and Foundations

·         New Computational Models for Big Data

·         Mathematical fundamentals of Big Data Analytics

·         Graph theory for Big Data Analytics

·         ICT  fundamentals for Big Data Analytics

·         Visualization techniques for Big Data Analytics

·         Decision science for Big Data Analytics

·         Statistical modelling for Big Data Analytics

·         Machine learning for Big Data Analytics

·         Optimization techniques for Big Data Analytics

·         Research methodology for Big Data Analytics

·         Data mining for Big Data Analytics

·         Business models  for Big Data Analytics

·         Real-time algorithms for  Big Data Analytics

·         Statistical thinking and computing thinking for Big Data

2.      Applications of Big Data Analytics

·  Big Data Analytics based services innovation

·  Big Data Analytics in business ecosystems

·  Big Data Analytics with public and open data

·  Big Data Analytics and data markets

·  Big Data Analytics for e-commerce

·  Big Data Analytics for web services

·  Big Data Analytics in business decision making

·  Big Data Analytics in healthcare

·  Big Data Analytics in banking industry

·  Big Data Analytics in social networking services

·  Visualization Analytics for Big Data

·  Big data intelligence

·  Big Data Search and Mining

·  Big Data Security & Privacy

·  Big Data processing and management

·  Big Data analytics for risk management

     3.    Challenges on Big Data Analytics

1.       Challenges for Big Data Analytics research

2.       Challenges for Big Data Analytics applications

3.       Challenges for Big Data Analytics tools

4.       Challenges for Big Data Analytics methodologies
2        Notes for Intending Authors

 We are seeking original, genuine, innovative, scientifically rigorous
research papers on fundamentals and applications of Big Data Analytics.
Empirical research, case studies or theory based qualitative and
quantitative studies on Big Data Analytics are also welcome.

 Submitted papers should not have been previously published nor be
currently under consideration for publication elsewhere.

Author guidelines can be found at:
http://www.worldscinet.com/style_files/nmnc/202-readme_2e.shtml. All
submissions will be refereed by at least three reviewers. Submissions and
submission intentions should be directed by email to zsun at dbs.unitech.ac.pg
, zhaohao.sun at gmail.com and ppw at ee.duke.edu

For more information, please contact zsun at dbs.unitech.ac.pg,
zhaohao.sun at gmail.com andppw at ee.duke.edu.
3        Important dates

·         Full paper submission:                     June 30, 2015

·         Notification of acceptance:              September 15, 2015

·         Revised submission:                                    October
20, 2015

·         Final acceptance notification:          November 20, 2015

·         Camera ready version of paper:       December 20, 2015

·         Publication:                                      January – May,

4           Editors

Associate Prof. Dr Zhaohao Sun, Ph.D.

Department of Business Studies, PNG University of Technology, Lae, PNG,

Email: zsun at dbs.unitech.ac.pg


Honorary Senior Research Fellow

School of Engineering and Information Technology, Federation University
Australia, Ballarat, Vic 3353, AUSTRALIA

Email: z.sun at federation.edu.au, or zhaohao.sun at gmail.com

Prof. Dr Paul P.Wang, PhD

Duke University, USA

e-mail: ppw at ee.duke.edu
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