We invite high-quality submissions describing original and unpublished work for the conference. Accepted submissions will be included in the IEEE Xplore Digital Library.

All submissions will be subject to plagiarism check. Papers submitted for consideration should not have been published elsewhere and should not be under review or submitted for review elsewhere during the duration of consideration. At least one author of an accepted paper must register for the conference and present the paper in the conference. Only the presented papers will be included in the proceedings.

Papers that do not make the grade for publication, yet show promise, may be selected for poster presentation instead. If you are specifically interested in submitting a poster, then please ensure that the paper does not exceed 4 pages (including figures, references and appendices).

Topics of Interest
The theme of this conference is use of Computational Intelligence in Big Data. The papers need not be based only on this theme. Topics of interest for the conference include, but are not restricted to:

Topics
Foundations

Probabilistic and statistical models and theories
Machine Learning algorithms for high-velocity streaming data
Scalable analysis and learning
Data pre-processing, sampling and reduction
High dimensional data, feature selection and feature transformation
High performance computing for data analytics
Architecture, management and process for data science
Data analytics, machine learning and knowledge discovery

Knowledge discovery theories, models and systems
Learning for streaming data
Intent and insight learning
Cross-media data analytics
Big data visualization, modeling and analytics
Multimedia/stream/text/visual analytics
Computational Intelligence and Big Data Analytics

Computational theories for big data analysis
Incremental learning – theory, algorithms and applications in big data
Sparse data, feature selection, feature transformation – theory, algorithms and applications for big data
Associative memories
Probabilistic and information-theoretic methods
Supervised, unsupervised and reinforcement learning
Support vector machines and kernel methods
Time series analysis
Algorithms and libraries - Optimization for Big Data Analytics