Aims and Scope:
The volume, variety, velocity, veracity and value of data and data communication are increasing exponentially. The “Five Vs” are the key features of big data, and also the causes of inherent uncertainties in the representation, processing, and analysis of big data. Big data, by its nature, contains bias, noise and abnormality, which may not be a correct characterisation of the actual system that it is meant to represent. Big data also contains a significant amount of unstructured, uncertain and imprecise data. For example, social media data is inherently uncertain.
Fuzzy sets, logic and systems enable us to efficiently and flexibly handle uncertainties in big data, thus enabling it to better satisfy the needs of real world big data applications and improve the quality of organizational data-based decisions. Successful developments in this area have appeared in many different aspects, such as fuzzy data analysis technique and fuzzy data inference methods. In particular, the linguistic representation and processing power of fuzzy logic is a unique tool for bridging symbolic intelligence and numerical intelligence gracefully. Hence, fuzzy logic can help to extend transfer learning in big data from the numerical data level to the knowledge rule level. It is therefore instructive and vital to gather current trends and provide a high quality forum for the theoretical research results and practical development of fuzzy logic and systems in handling uncertainties in big data.

This special session aims to offer a systematic overview of this new field and provides innovative approaches to handle various uncertainty issues in big data presentation, processing and analysing by applying fuzzy sets, fuzzy logic, fuzzy systems and other computational intelligent techniques.

Topics:

We invite interested authors to submit their original and unpublished work to this special session. The main topics of this special session include, but are not limited to, the following:
• Fuzzy rule-based knowledge representation in big data processing
• Information uncertainty handling in big data processing
• Unstructured big data visualization
• Uncertain information and knowledge modeling in big data sets
• Tools and techniques for big data analytics in uncertain environments
• Context-aware big data processing
• Fuzzy systems for big data analytics
• Uncertain data presentation in big data systems
• Uncertain issues in data-driven decision support systems
• Uncertain issues in recommender systems in big data environments
• Uncertain issues in cloud computing
• Uncertain issues in social network

Special Session organizers:

1. Professor Jie Lu, University of Technology Sydney, Australia
2. Professor Cheng-Ting Lin, National Chiao Tung University, Taiwan
3. Dr Farookh Khadeer Hussain, University of Technology Sydney, Australia
4. Dr Vahid Behbood, University of Technology Sydney, Australia
5. Professor Guangquan Zhang, University of Technology Sydney, Australia