[hpc-announce] [Deadline Extension] Special Issue: Big Data, Analytics, and High Performance Computing, JBDR, Elsevier
Paul D Yoo
paul.d.yoo at ieee.org
Sat Nov 22 23:40:00 CST 2014
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DEADLINE IS EXTENDED TO 22 DECEMBER 2014
CALL FOR PAPERS
JOURNAL OF BIG DATA RESEARCH, ELSEVIER SCIENCE, SPECIAL ISSUE ON
"Big Data, Analytics, and High Performance Computing"
SUBMISSION DEADLINE: November 24th, 2014 (Extended to 22nd December)
Prof. Paul D. Yoo. Khalifa University, UAE
Prof. Albert Y. Zomaya, University of Sydney, Australia
Aims and Scope
We live in an era of data deluge. Given the unprecedented amount of data that has been produced, collected, and stored in the coming years, one of the technology industry’s great challenges is how to benefit from it. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. The sheer volume of data makes it often impossible to run analytics using a central processor and storage, and distributed processing with parallelized multi-processors is preferred while the data themselves are stored in the cloud. In addition, as the size of data grows exponentially, current algorithms are not efficient or scalable enough to deal with such large volumes of data. Designing more accurate intelligent models so as to satisfy the market needs will hence bring huge opportunities as well as challenges to these communities. We believe this special issue will offer a timely collection of novel research results to benefit the researchers and practitioners working in these communities. This special issue focuses on all aspects of big data and targets a mixed audience of researchers from several communities including analytics, machine learning and data mining, distributed and high performance computing, etc.
Topics of interest include (but are not limited to):
Theoretical foundations and algorithms for big data analytics
Compressive sampling, matrix completion, low-rank models, and dimensionality reduction
Efficient learning and clustering
Robustness to outliers; convergence and complexity issues; performance analysis
Scalable, online, active, decentralized, deep learning and optimization
Architectures and applications for large-scale data analysis
Scalable, distributed computing, MapReduce on
Multi-Core, GPU, hybrid distributed environments
Opportunistic / heterogeneous computing
Systems biology, genomics, bioinformatics, health, medical, semantics, sentiment and natural language processing
Green energy and smart power grid analytics; climate; astronomical; geoscience
Cyber security inc. intrusion/botnet detection systems, security and privacy in cloud
Industrial and systems engineering
Sensors, mobile and wireless communications
Articles submitted to this special issue must contain significant relevance to Big Data. All submissions will be peer reviewed according to the Elsevier guidelines. Submitted articles should not have been published or under review elsewhere. Submissions to this special issue of the Elsevier Journal of Big Data Research should have significant tutorial value. Manuscripts should be submitted online at http://www.journals.elsevier.com/big-data-research/ <http://www.journals.elsevier.com/big-data-research/> using the Elsevier Editorial System. The authors must select "SI: BDA-HPC" as Article Type when they reach the Article Type step in the submission process. Submissions are expected to not exceed 20 pages (including figures, tables, and references) in the journal’s single-column format using 11 point font. Prospective authors should consult the site "Guide for Authors" at the above link for guidelines and information on paper submission.
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