Bogdan Gabrys: Abstract

We are currently experiencing an incredible, explosive growth in digital content and information. Research in traditionally qualitative disciplines is fundamentally changing due to the availability of such vast amounts of data. In fact, data-intensive computing has been named as the fourth paradigm of scientific discovery and is expected to be key in unifying the theoretical, experimental and simulation based approaches to science. The commercial world has also been transformed by a focus on Big Data with companies competing on analytics. Data has become a commodity and in recent years has been referred to as the ‘new oil’.

There has been a lot of work done on the subject of intelligent data analysis, data mining and predictive modelling over the last 50 years with notable improvements which have been possible with both the advancements of the computing equipment as well as with the improvement of the algorithms. However, even in the case of the static, non-changing over time data there are still many hard challenges to be solved which are related to the massive amounts, high dimensionality, sparseness or inhomogeneous nature of the data to name just a few. What is also very challenging in today’s applications is the non-stationarity of the data which often change very quickly posing a set of new problems related to the need for robust adaptation and learning over time. In scenarios like these, many of the existing, often very powerful, methods are completely inadequate as they are simply not adaptive and require a lot of maintenance attention from highly skilled experts, in turn reducing their areas of applicability.

In this talk we will discuss the requirements and potential solutions in order to achieve the required high level of adaptability and robustness of the modern predictive systems. The results of extensive testing for many different benchmark problems and various snapshots of interesting results covering the last decade of our research will be shown throughout the presentation.

Organised by BCS SGAI The Specialist Group on Artificial Intelligence http://www.bcs-sgai.org