[Rdap] [ESIP-all] Call for papers: Springer Journal of Big Data (JOBD) (fwd)

Joe Hourcle oneiros at grace.nascom.nasa.gov
Tue Mar 5 14:38:14 EST 2013

I know that not everyone on here deals with 'big data', but I thought this 
might be of interest to some of the people on the list.

(and they *do* define 'big data' in terms of variety / volume / volume, so 
'small science' with a lot of variety would still fit in their call)

-Joe H.

ps.  Yes, I know some people are boycotting all of the big science
      publishers; if this had been for an Elsevier journal, I'd not have
      bothered forwarding it along.

---------- Forwarded message ----------
Date: Tue, 5 Mar 2013 05:13:13 +0000
From: "Mattmann, Chris A (388J)" <chris.a.mattmann at jpl.nasa.gov>
To: "esip-all at rtpnet.org" <esip-all at rtpnet.org>
Subject: [ESIP-all] Call for papers: Springer Journal of Big Data (JOBD)

Hi Folks,

Please find a solicitation for high quality research papers in the area of
Big Data.

Any questions, feel free to ask.


Journal of Big Data

Borko Furht and Taghi M. Khoshgoftaar
Florida Atlantic University,
Boca Raton, Florida, USA

Editorial Board
Kapil Bakshi, Cisco Systems, CA
Rob Bird, Red Lambda, Orlando, FL
PhilipChan, Florida Institute of Technology, Melbourne, FL
Xue-wen Chen, Wayne State University, Detroit, MI
Wei Ding, University of Massachusetts, Boston, MA
Salvatore Distefano, Politechnico di Milano, Milan, Italy
Dirk Habich, Dresden University of Technology, Dresden, Germany
Vasant Honavar, Iowa State University, Ames, IA
Jun (Luke) Huan, University of Kansas, Lawrence, KS
Nathalie Japkowicz, University of Ottawa, Canada
James Joshi, University of Pittsburgh, PA
Mohan Kankanhalli, National University of Singapore, Singapore
Geng Lin, Dell, IBM Alliance Cisco Systems, San Jose, CA
Muthucumaru Maheswaran, McGill University, Montreal, Canada
Chris Mattmann, NASA Jet Propulsion Laboratory, Pasadena, CA
Edin Muharemagic, LexisNexis, Boca Raton, FL
Sangmi Pallickara, Colorado State University, Fort Collins, CO
Shrideep Pallickara, Colorado State University, Fort Collins, CO
Marek Reformat, University of Alberta, Canada
Naphtali Rishe, Florida International University, Miami, FL
Mei-Ling Shyu, University of Miami, FL
Pradip Srimani, Clemson University, Clemson, SC
Flavio Villanustre, LexisNexis, Atlanta, GA
Jun Wang, University College, London, UK
Hui Xiong, Rutgers, Camden, NJ
Du Zhang, California State University, Sacramento, CA
Kang Zhang, University of Texas at Dallas, TX
Peng Zhang, Chinese Academy of Sciences, Beijing, China
Xingquan Zhu, Florida Atlantic University, Boca Raton, FL
Yelena Yesha, University of Maryland, Baltimore County, MD

In light of the dramatic growth of Big Data analytics and data intensive 
computing and relating technologies, the aim of the Journal is to publish 
state-of-the-art articles in this field. The Journal is intended for a 
wide variety of readers including academicians, developers, educators, 
engineers, practitioners, researchers, and graduate students.

Description of the field
Big Data Analytics is no longer a specialized solution for cutting-edge 
technology companies; it refers to a cost-effective way to store and 
analyze large volume of data across many industries. The applications of 
Big Data include health care and life sciences; supply chain, logistics, 
and manufacturing; online services and Web analytics; financial services; 
energy and utilities; media and telecommunications; and retail and 
consumer products. Big Data can be defined via three Vs: volume, velocity, 
and variety. Volume refers to the raw scale of records, transactions, 
tables, and files; velocity includes batch processing, near time, 
real-time, and streaming; while variety includes structured, unstructured, 
and semi-structured data. The Journal will focus on articles discussing 
Big Data challenges including data capture and storage; search, sharing, 
and analytics; Big Data technologies; and data visualization. A special 
focus will be given to Big Data technologies including architectures for 
massively parallel processing, data mining tools and techniques, machine 
learning algorithms for Big Data, distributed file systems and databases, 
cloud computing platforms, and scalable storage systems.

List of topics
* Big Data technologies
* Data capture and storage
* Extracting knowledge from large datasets
* Architectures for massively parallel data processing
* Data mining tools and techniques
* Scalable storage systems? Hadoop and HPCC (High Performance Computing
* Machine learning algorithms for Big Data
* Cloud computing platforms for Big Data analytics
* Network architectures for Big Data applications
* Distributed file systems and databases
* Data protection and privacy
* Social networks and Big Data
* Visualization of Big Data
* Applications of Big Data to
     - Scientific Applications
     - Bioinformatics
     - Health Care
     - Life sciences
     - Supply Chain
     - Online Services
     - Web analytics
     - Financial Services
     - Large Science discoveries
     - Climate Change
     - Environment
     - Energy and utilities
     - Media and telecommunications
     - Retail and consumer products
     - Commercial applications

Anticipated content

Our intent is to have various types of articles
including research papers, survey and review papers, and application
papers. We
will also encourage and organize special issues on specific topics
relating to
Big Data.

Please, send your contributions to
Borko Furht and Taghi Khoshgoftaar at bfurht at fau.edu

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