Dr. Frederic Stahl, University of Reading

Building Adaptive Data Mining Models on Streaming Data in Real-Time: an Outlook on Challenges, Approaches and Ongoing Research

Abstract

Advances in hardware and software, in the past two decades have enabled the capturing, recording and processing of potentially large and infinite streaming data. As a consequence the field of research in Data Stream Mining has emerged building Data Mining models, workflows and algorithms enabling the efficient and effective analysis of such streaming data at a large scale. Application areas of Data Stream Mining techniques include real-time telecommunication data, telemetric data from large industrial plants, credit card transactions, social media data, etc. Some applications allow the data to be processed modelled and analysed in batches by traditional Data Mining approaches. However, others require the model building and analytics to take place in real-time as soon as new data becomes available i.e. to accommodate infinite streams and fast changing concepts in the data. This talk discusses challenges, opportunities and recent/current research approaches towards innovative solutions in Data Stream Mining.

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