As we delve deeper into the ‘Digital Age’, we witness an explosive
growth in the volume, velocity, and variety of the data available on
the Internet. For example, in 2012 about 2.5 quintillion bytes of data
was created on a daily basis. The data originated from multiple types
of sources including mobile devices, sensors, individual archives,
social networks, Internet of Things, enterprises, cameras, software
logs, health data etc. Such ‘Data Explosions’ has led to one of the
most challenging research issues of the current Information and
Communication Technology (ICT) era: how to effectively and optimally
manage such large amount of data and identify new ways to analyze
large amounts of data for unlocking information. The issue is also
known as the ‘Big Data’ problem, which is defined as the practice of
collecting complex data sets so large that it becomes difficult to
analyze and interpret manually or using on-hand data management
applications. From the perspective of real-world applications, the Big
Data problem has also become a common phenomenon in domain of science,
medicine, engineering, and commerce. Representative applications
include clinical decision support systems, digital agriculture, social
media analytics, high energy physics, earth observation, genomics,
automobile simulations, medical imaging, body area networks,
translational medicine, and the like.
Big data cannot be readily processed using standard techniques, and
addressing big data problems in general relies on High Performance
Computing (HPC) capabilities. Big data computing ususally adopts data
parallel approaches to processing large volumes of data. New
processing paradigms with more flexible data models have emerged for
the needs. Typical examples include the MapReduce architecture
pioneered by Google, Hadoop, its open-source implementation, and
Lustre etc. In long term, big data computing demands increasingly
powerful HPC systems to support data-driven sciences and to execute
data-intensive modeling and simulation problems at larger scale, at
higher resolution, and with more components; the availability of newer
advanced methods to perform advanced analytics on big data in
near-real-time.
The special issue will primarily encompass practical solutions that
advance the research in big data computing, especially the latest
research and technology in the fields of parallel processing,
distributed computing systems and computer networks. The topics of
interest include, but are not limited to:
· Big data computing methodologies and paradigms
· Programming models for big data computing
· Parallel processing algorithms for big data computing
· Infrastructures and systems for big data computing
· Big data mining and analytics
· Big data visualization
· Big data applications
· Innovation computer architectures for big data computing
· Performance characterization, evaluation and optimization for
big data computing
Submitted papers should not have been previously published nor be
currently under consideration for publication elsewhere. The papers
should be submitted via the Manuscript Central website:
https://www.editorialmanager.
"Instructions For Authors" in the menu for formatting the manuscript.
Authors should indicate that you are submitting to the Special Issue
on "S.I.: Big Data Computing" in manuscript central. For additional
questions please send an email to the Guest Editors.
Time Table:
Paper submission: Oct 01, 2014.
Initial notification: Dec 15, 2014.
Rebuttal submission: Jan 01, 2015.
Revision due: Jan 15, 2015.
Final notification: Mar. 01, 2015.
We will kindly inform you about the review results as soon as a
decision was made. Questions regarding the disclosure of the review
results should be addressed to the Guest Editors.
Guest Editors:
Dan Chen (leading guest editor)
Professor
School of Computer
Wuhan University
Email: dan.chen@ieee.org
Rajiv Ranjan
Senior Research Scientist
CSIRO Computational Informatics Division, Australia
Email: rajiv.ranjan@csiro.au
Fang Huang
Associate Professor
University of Electronic Science and Technology of China (UESTC)
Email: hfhbhzp@uestc.edu.cn
Lizhe Wang
Professor
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
Email: lizhe.wang@gmail.com
growth in the volume, velocity, and variety of the data available on
the Internet. For example, in 2012 about 2.5 quintillion bytes of data
was created on a daily basis. The data originated from multiple types
of sources including mobile devices, sensors, individual archives,
social networks, Internet of Things, enterprises, cameras, software
logs, health data etc. Such ‘Data Explosions’ has led to one of the
most challenging research issues of the current Information and
Communication Technology (ICT) era: how to effectively and optimally
manage such large amount of data and identify new ways to analyze
large amounts of data for unlocking information. The issue is also
known as the ‘Big Data’ problem, which is defined as the practice of
collecting complex data sets so large that it becomes difficult to
analyze and interpret manually or using on-hand data management
applications. From the perspective of real-world applications, the Big
Data problem has also become a common phenomenon in domain of science,
medicine, engineering, and commerce. Representative applications
include clinical decision support systems, digital agriculture, social
media analytics, high energy physics, earth observation, genomics,
automobile simulations, medical imaging, body area networks,
translational medicine, and the like.
Big data cannot be readily processed using standard techniques, and
addressing big data problems in general relies on High Performance
Computing (HPC) capabilities. Big data computing ususally adopts data
parallel approaches to processing large volumes of data. New
processing paradigms with more flexible data models have emerged for
the needs. Typical examples include the MapReduce architecture
pioneered by Google, Hadoop, its open-source implementation, and
Lustre etc. In long term, big data computing demands increasingly
powerful HPC systems to support data-driven sciences and to execute
data-intensive modeling and simulation problems at larger scale, at
higher resolution, and with more components; the availability of newer
advanced methods to perform advanced analytics on big data in
near-real-time.
The special issue will primarily encompass practical solutions that
advance the research in big data computing, especially the latest
research and technology in the fields of parallel processing,
distributed computing systems and computer networks. The topics of
interest include, but are not limited to:
· Big data computing methodologies and paradigms
· Programming models for big data computing
· Parallel processing algorithms for big data computing
· Infrastructures and systems for big data computing
· Big data mining and analytics
· Big data visualization
· Big data applications
· Innovation computer architectures for big data computing
· Performance characterization, evaluation and optimization for
big data computing
Submitted papers should not have been previously published nor be
currently under consideration for publication elsewhere. The papers
should be submitted via the Manuscript Central website:
https://www.editorialmanager.
"Instructions For Authors" in the menu for formatting the manuscript.
Authors should indicate that you are submitting to the Special Issue
on "S.I.: Big Data Computing" in manuscript central. For additional
questions please send an email to the Guest Editors.
Time Table:
Paper submission: Oct 01, 2014.
Initial notification: Dec 15, 2014.
Rebuttal submission: Jan 01, 2015.
Revision due: Jan 15, 2015.
Final notification: Mar. 01, 2015.
We will kindly inform you about the review results as soon as a
decision was made. Questions regarding the disclosure of the review
results should be addressed to the Guest Editors.
Guest Editors:
Dan Chen (leading guest editor)
Professor
School of Computer
Wuhan University
Email: dan.chen@ieee.org
Rajiv Ranjan
Senior Research Scientist
CSIRO Computational Informatics Division, Australia
Email: rajiv.ranjan@csiro.au
Fang Huang
Associate Professor
University of Electronic Science and Technology of China (UESTC)
Email: hfhbhzp@uestc.edu.cn
Lizhe Wang
Professor
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences
Email: lizhe.wang@gmail.com
No comments:
Post a Comment