Modern technology is generating data at an unprecedented scale. This big data can be considered as the fuel of the modern information society. However, in order to leverage this flood of data we have to extract the useful information, manifesting itself in certain patterns, out of it. While lacking a precise definition, big data problems share typically a set of characteristics: (i) large volume, (ii) high velocity and (iii) wide variety of the datasets. The algorithms confronted with Big Data consume different types of resources, e.g., hardware cost, communication bandwidth and energy. As a research group our interest is in the fundamental limits and tradeoffs, as well as, in the application of efficient techniques for big data considering the constraints mentioned above.

The Big Data Analytics stream focuses on a strong theoretical background in machine learning, text and data mining (including large scale graph mining), visualisations, big data technologies and statistical data analysis. An advanced knowledge and appreciation of non-statistical approaches to data and linked data as well as distributed systems and large scale (NoSQL) databases also form an integral part of this stream. Finally, an appreciation of how the role of a data analyst or scientist fits into the organisational and development processes of a company will be covered. The learning outcomes for this big data stream are:

  • a highly analytical approach to problem solving;
  • ability to extract value and insight from data;
  • ability to analyse and critically evaluate applicability of both machine learning, statistical and  mining approaches;
  • ability to work with big amounts of structured and unstructured data.
Projects/Theses Demos Publications