Calls for papers

 

International Journal of Big Data Intelligence

 

Special Issue on: "Big Data Visualisation and Analytics"

Guest Editors:
Lei Yang, University of Nevada, Reno, USA
Xu Chen, University of Goettingen, Germany
Fuhong Lin, University of Science and Technology Beijing, China
Rui Zhang, University of Hawaii, USA

This special issue aims to provide an international platform for experts from both academia and industry to present their latest research findings, ideas, developments and applications in big data visualisation and analytics.

The big data phenomenon has emerged as a result of vast amounts of data that are becoming available across a wide range of application domains across science, business and government. Research on big data visualisation and analytics will be necessary for serving scientists, engineers, educators, citizens and decision makers who have unprecedented amounts and types of data available to them.

We invite the submission of papers describing innovative research on all aspects of big data visualization and analytics.

In addition to regular submissions, this special issue will also include extended versions of selected papers from the Big Data Visualization and Analytics special track at the International Symposium on Visual Computing (ISVC’15).

Subject Coverage
Suitable topics include, but are not limited to, the following:

• Big data visualisation
• Large data set processing for visual computing
• Visual big data analytics
• Scientific and information visualisation
• Image processing and computer vision
• High-resolution displays and virtual environments
• Big data analytics in energy systems/smart grids, cyber-physical systems, mobile networks, internet of things, transportation systems, sensor networks, etc.
• Big data stream modelling and analytics
• Big data and cloud computing, large-scale stream processing on the cloud
• Autonomous, online and incremental learning for big data
• High-dimensional data, feature selection, feature transformation for big data
• Scalable algorithms, kernel methods and statistical learning theory for big data

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).

All papers are refereed through a peer review process.

All papers must be submitted online. To submit a paper, please read our Submitting articles page.

Important Dates

Full paper submission due: 30 January, 2016