Big Data Analytics

Jointly organised by Dr Khin Lwin and Professor Alamgir Hossain, Anglia Ruskin IT Research Institute, Cambridge/Chelmsford, UK.

New technologies enable us to collect more data than ever before. With an overwhelming amount of web-based, mobile, and sensor-generated data arriving at a terabyte and even zettabyte scale, new science and insights can be discovered from the highly detailed and domain-specific information which can contain useful information about problems such as national intelligence, cyber security, fraud detection, financial trading, personalised medicine and treatments, personalised information and recommendations and personalised athletic training. Machine learning algorithms, particularly deep learning (evolved from artificial neural networks) plays a vital role in big data analysis. Deep Learning algorithms extracts high-level and complex abstractions by discovering intricate structure in large data sets. Deep learning techniques are nowadays the leading approaches to solve complex machine learning and pattern recognition problems such as speech and image understanding, semantic indexing, data tagging and fast information retrieval. Despite of that, there is a limited understanding of how to design computationally efficient and effective learning algorithms.

This special session focus on all aspects of big data analytics, with a particular emphasis on the analysis and learning of massive volume of unstructured data and developing effective and efficient large-scale learning algorithms. In addition, this session aims to bring leading scientists, researchers and experts (in big data analytics, deep learning and other related areas within mathematics, statistics, artificial intelligence, machine learning, neuroscience, psychology and philosophy) together to discuss and share the current and new research topics and ideas, to provide a platform to present and discuss recent advancements as well as to increase international collaborations between academic institutions and industries.

The papers submitted to this special session might be in a large range of topics that include, but are not limited to:

* Big Data Processing Algorithms

* Knowledge Discovery, Integration and Transformation

* Big Data Discovery and Analysis Patterns

* Big Data Classification Techniques

* Big Data Clustering Algorithms

* Data-Driven Reasoning and Learning

* Data-Driven decision making and planning

* Big Data Mining and Security Intelligence

* Big Data in Cybersecurity, Finance, Healthcare and Transportation Applications

* Innovative Deep Learning Algorithms that Efficiently Handle Large-scale Data for data Representation and Analysis

* Learning in Uncertainty Labelled Data

* Sentiment Analysis and Opinion Mining

* Optimization Methods for Deep Learning

* Deep Learning Applications

* Future Directions and Challenges in Big Data Analytics and Deep Learning