TAF 2016 - Special Issue on Theoretical and Algorithmic Foundation for Big Data

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 21, 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