Tagged / data mining

Data Science and Analytics Training for Business

logo-czrneData Science and Analytics Training and Engagement Services for Business – HEIF project

We are experiencing an explosive growth of digital content. According to International Data Corporation, there are currently over 2.7 zetabytes of data. It is estimated that in 2020, the digital universe will be 50 times as big as in 2010 and that from now until 2020 it will double every two years.

The commercial world has been transformed by Big Data with companies competing on analytics. Data has become a commodity referred to as the ‘new oil’. We are entering a new era of predictive analytics and data intensive computing which has been recognised worldwide with various high profile reports. In a recent UK-wide report commissioned by SAS UK (one of our key industrial partners) it has been estimated that there will be about 132,000 big data job opportunities created in the UK economy between 2012 and 2017. McKinsey’s report states that by 2018 the US alone will face a shortage of between 140,000 to 190,000 people with deep analytical skills, while in the UK such shortage will be in the region of 58,000 (e-Skills UK5). Another SAS commissioned report focusing on “data equity” and its impact on the UK, states that increasing adoption of big data analytics will result in cumulative benefits of £216 billion over the years 2012-17.

Following the success of recently launched MSc in Applied Data Analytics, this HEIF project seeks to take advantage of a large demand for and addresses the widening advanced analytics skills gap. Our HEIF project focuses on:

  1. Engagement with industry through a provision of an on-going opportunity for contact, information and advice in the Data Science Surgeries which are open to businesses of all sizes as well as university staff and students. This service is to support the creation of Knowledge Exchange professional network in the Data Science and Analytics area helping to identify potential skillset needed as well as transfer of knowledge and collaborative research opportunities.
  2. Development of a portfolio of CPD/short courses within an area with acute UK-wide shortage of skills and where, within the Data Science community consisting of over 50 academics from four faculties, BU has a wealth of expertise and excellent track record.

Over time, the Data Science Surgeries and CPD courses will facilitate engagement between industry and the broader BU Data Science community, enabling us to build bridges and develop relationships with industry, as well as interdisciplinary research collaborations.  The new perspectives developed through this interdisciplinary collaboration will not only help to give a better understanding of some of the complex problems facing our society, but also help to inform both the teaching and professional practice undertaken by our academics -supporting the vision of Fusion at BU.

Workshop on Evolving Predictive Systems

Workshop on Evolving Predictive Systems

co-located with the 12th International Conference on Parallel Problem Solving From Nature (PPSN-2012)

September 1-5, 2012

Taormina, Italy

In recent years, the data mining scientific community witnessed a very strong demand for predictive systems that will be able to evolve and adapt. The range of tasks fulfilled by evolving predictive systems is very broad and covering many different application areas. Despite the high number of publications dealing with applications, there are still unaddressed pressing issues of evolving predictive systems design and development, such as complexity analysis, ensemble architectures and meta-learning. This workshop is devoted to the discussion of robust, context aware and easy-to-use evolving predictive systems, which improve, adapt and possibly maintain themselves within their respective environments and constraints.  Contributions presenting recent work on ensemble systems, complexity analysis and meta-learning are particularly welcome.

The workshop addresses people from the scientific IT community who are active in the research domain of data-driven systems capable to adapt to changing situations and environments. The considered approaches can include evolutionary algorithms, other nature-inspired methods or heuristic approaches. Special focus will be put on research dealing with ensemble architectures, as well as with complexity issues (size, form and interpretation of the solution formula, time and algorithm complexity) and meta-learning incorporation.

Researchers are invited to submit original work as papers of not more than 10 pages. Authors are encouraged to submit their papers in LaTeX. Papers must be submitted in Springer Verlag’s LNCS style.

Topics of interest

Topics that are in the area of interest of the workshop include, but are not limited to:

•             Advanced Modelling Techniques for Evolving Predictive Systems
•             Evolving Predictive Ensembles
•             Complexity Analysis for Evolving Predictive Systems
•             Advanced Adaptation Mechanisms
•             Meta-learning
•             Applications

Important Dates

Submission of Papers: 2 April 2012

Notification of Acceptance: 1 June 2012

Camera-Ready submission of Papers: 20 June 2012

Early Registration Deadline: 25 June 2012

Conference: 1-5 September 2012

Papers are submitted by direct email to mailto:atsakonas@bournemouth.ac.uk

Organization Committee

Bogdan Gabrys, Bournemouth University, UK, bgabrys@bournemouth.ac.uk

Athanasios Tsakonas, Bournemouth University, UK, atsakonas@bournemouth.ac.uk

Mailing address: Bournemouth University, Poole House, Talbot Campus, Fern Barrow, Poole, Dorset, BH12 5BB, UK