Understanding Complex and Large Industrial Data 2016, or UCLID, is a workshop which aims to provide an opportunity for academic researchers and industrial practitioners to work together and share ideas on the fast developing field of 'big data' analysis. The workshop has an excellent line-up of academic and industrial speakers covering four main themes: Foundations of Data Science & Big Data, Defence & Security, Health & Bioinformatics and Data Science in Industry. This is a growing area of importance within academia and industry where the potential for new research and economic impact has been recognised.
UCLID 2016 is hosted by the STOR-i Doctoral Training Centre and the Data Science Institute, which are both based at Lancaster University. STOR-i's unique position between academia and industry provides an ideal venue for this event, as this workshop builds upon STOR-i's philosophy of cross-collaboration and implementation of new research within the wider community. The new Data Science Institute at Lancaster aims to act as a catalyst for the field of Data Science and to provide an end-to-end interdisciplinary research capability - from infrastructure and fundamentals through to globally relevant problem domains and the social, legal and ethical issues raised by the use of Data Science
The workshop will take place on the 12-14th September 2016. Registration is required for attendance; details on how to register can be found here.
Over the last few years there have been significant changes in the way we view data. Technological developments such as high speed internet and large data storage devices have developed to the point, where it is now easier and cheaper than ever to collect and store large volumes of data. These significant technological advances have led to what is commonly referred to as the big data revolution. Up until now, most of the new developments in this field have revolved around tackling the practical challenges of big data, in particular designing new hardware and creating new database infrastructures (e.g. Apache Hadoop). The challenge is now to answer questions such as, how is it possible to make sensible inference from big data and how can we use such data to make better decisions?
The new challenges faced by big data span several key areas including operational research (data mining, classification, data reduction), statistics (multivariate analysis, time series, inference) and industry (data collection, data cleaning, decision making). Traditionally, research has been developed in academia and applied to industrial problems, however with the big data revolution many of the new developments have come from industry where practitioners have devised solutions to tackle these challenges. The aim of this workshop is to bridge the gap between academia and industry and to explore the potential for new research collaborations between researchers and practitioners.