Frederic Stahl, University of Reading, UK
Shared talk: Monday, July 2nd, 14:00-15:30
Title: Exploring Algorithms and Approaches for the Real-Time Analytics of Data Streams
Abstract: Developments in sensor and data acquisition technology and the increase of applications that require processing and analysing a constant, infinite stream of data records challenge our current data mining algorithms. Such applications are for example Internet of Things, Mirco-Blog rule mining (i.e. from Twitter), Sensor Networks, etc. This tutorial introduces the field of Data Stream Mining, relevant algorithms and includes some hands on tasks. Topics covered are:
- Data Stream Concept Drift Detection
- Predictive Data Stream Analytics
- Cluster Analysis on Data Streams
Practical tasks will require the prior installation of the Massive Online Analysis Software Release 2017.06 (https://moa.cms.waikato.ac.nz/downloads/).
Short bio: Frederic Stahl completed his degree at the University of Applied Science in Weihenstephan (Germany) in Bioinformatics. He received the academic grade as Diploma Engineer in 2006 and obtained his Ph.D. from the University of Portsmouth in 2010. The title of his Thesis is “Parallel Rule Induction”. After his Ph.D. Frederic continued working as Senior Research Associate at the University of Portsmouth until February 2012. Frederic joined Bournemouth University as fixed term Lecturer from February 2012 until November 2012 and is currently working as Associate Professor at the University of Reading. His research interests are in the area of data mining of large and complex datasets; parallel and distributed data mining; data stream mining; data mining in resource constraint environments, machine learning and artificial intelligence.