Due to improved sensor technology, increasing storage space, and enhanced data availability, digital data sets are rapidly increasing with respect to size, dimensionality, and complexity. On the one hand, big and streaming data sets are becoming more and more popular in complex systems such as industrial manufacturing processes, surveillance, finance, social networks, or health-care. On the other hand, the dimensionality of data can easily reach a few thousand and data sources are often enriched by auxiliary information which gives crucial clues to avoid overfitting. These facts demand for advanced methods and tools which can cope with these big and complex data with respect to not only its sheer size, but also its often challenging statistical properties such as heterogeneous quality, data trends, presence of rare events, and necessity for strong regularisation.
This special session will focus on advanced data analysis for big and streaming data which enable a reliable and computationally feasible access to such data sets. Submissions are encouraged according to the following non-exhaustive list of topics:
- Big data analytics
- Big data visualization
- Reliable machine learning for drift and trend
- Incremenental and lifelong learning
- Security and privacy in big data
- Regularization techniques for very high dimensional data
- Machine learning for heterogeneous and streaming data
- Constant memory algorithms for data analysis
- Analysis of sensor networks and social networks
- Distributed and multiple source machine learning techniques
- Big data applications e.g. in astronomy, health care, sensor networks
- Information and data fusion
- Semi-supervised learning
- Data correlation vs. information diversity
The Special Session will be held within the IEEE World Congress of Computational Intelligence (WCCI 2016), Vancouver Canada in July, 25-29 2016 http://wcci2016.org