[hpc-announce] CFP: Special Issue on Theoretical and Algorithmic Foundation for Big Data on Elsevier Journal of Computer and System Sciences

朱晓敏 xmzhu at nudt.edu.cn
Thu Feb 11 17:34:43 CST 2016

The website:http://www.journals.elsevier.com/journal-of-computer-and-system-sciences/call-for-papers/special-issue-on-theoretical-and-algorithmic-foundation-for/Data has been a major focus in the computer world for a long time, since values, information, and knowledge can be derived consequently. Recently, the data that computers have collected and processed grow dramatically or even exponentially in volume, variety, and velocity. Most data sets come from science, engineering, business, finance, economics, government, social life, and daily life. Such dramatically growing data sets are defined as Big Data. Right now, Big Data has become a major issue in the computer world.

In the early computer world, computers were busy in data generation. However, in the Big Data era, since data sets are big and built too quickly, the focus of computers has been switched to data digestion. However, the capacity of the current computer systems has not been increased proportionally and is insufficient to handle Big Data due to its size and generation speed. Some systems fail to solve problems efficiently, whereas others might even stop working. Both hardware and software designs should be reconsidered.

For Big Data, new theories and algorithms are in demand. Big Data should be maintained and processed efficiently and effectively. Computer limits have to be considered, while data size could be unlimited. The management and processing issues for large data sets such as data collection, transfer, fusion, storage, indexing, security, and algorithmic/analytic processing will be addressed properly. The theoretical and algorithmic foundation for Big Data will be considered specifically, since it might shed light on future computer systems and software design.

This special issue is intended to collect state-of-the-art research results that address key issues and topics related to Theoretical and Algorithmic Foundation for Big Data. Strong mathematical and analytic results are required, whereas survey and simulation-only papers will NOT be considered for this special issue. Along with these requirements, topics of interest include, but are not limited to:

Big Data infrastructure
Big Data capture and acquisition
Representation formats for Big Data
Big Data storage systems
Big Data integration and fusion
Big Data persistence and preservation
Big Data sharing and transferring
Big Data visualization
Data management within and across multiple geographically distributed data centers
Big Data query processing and indexing
Security, privacy and trust in Big Data systems
Collaborative thread detection using Big Data Analytics
Big Data analytic algorithms: cluster analysis, pattern recognition, machine learning, data/text/image mining, and statistics
Self-adaptive and energy-efficient mechanisms for Big Data
High performance computing for Big Data
Cloud computing for Big Data
Big Data in mobile and pervasive computing
Big Data as a service
Streaming and real-time processing
Data-intensive and scalable computing on hybrid infrastructure
Fault-tolerance, dependable, reliable and autonomic computing for Big Data
Big Data economy, QoS and business models
Big Data applications for multi-disciplinary applications (Bioinformatics, Multimedia Industry, Social Networks, Engineering, Finance, Healthcare, Enterprise, Governance and Business)

Submission Guidelines:

Original and unpublished contributions that should not currently be under review by another journal are solicited. All papers submitted to this Special Issue will undergo the standard review procedures of Journal of Computer and System Sciences. All manuscripts should be submitted through the Elsevier Editorial System:ees.elsevier.com/jcss. Please select “SI: Theo. & Algor. Big Data” when reaching the step of selecting article type name in submission process.

Important Dates:

Submission Deadline: Feb 29, 2016
Author Notification: Aug 30, 2016
Final Paper: Sep 15, 2016

Guest Editors:

Prof. Hai Jiang
Arkansas State University, USA
Email: hjiang at astate.edu

Prof. Xiaomin Zhu
National University of Defense Technology, China
Email: xmzhu at nudt.edu.cn

Prof. Laurence T. Yang
St Francis Xavier University, Canada
Email: ltyang at stfx.ca


Xiaomin Zhu, Ph.D. 
Associate Professor
College of Information Systems and Management
National University of Defense Technology, Changsha 410073, China
Email: xmzhu at nudt.edu.cn / xmzhunudt at gmail.com
Office: +86 731-8457-5431 ext 2
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