Myra Spiliopoulou and Georg Krempl will present a Tutorial on Mining Multiple Threads of streaming Data at PAKDD 2013, April 14-17, Gold Coast, Australia.
Stream mining is a mature area of research. However, several applications that require adaptive learning from evolving data do not seem to fit to the conventional stream mining paradigm. For example, a bank grants loans to customers and uses their data for model learning; the label (loan-payed-back YES or NO) arrives some years later, though, during which years the market may have changed drastically. Is this a stream mining problem? How many streams are there? We can distinguish between the stream of customers and the stream of their labels, which arrive with a time lag of years.
As another example, a hospital monitors patients with chronical diseases that come (ir)regularly to the hospital and undergo different tests; the streams of medical recordings and of signals (EEG, fMRI) can be used for learning. The hospital wants to learn a model on how the patients' health evolves in response to the disease and to medications. This problem seems completely different from the previous one, albeit streams of data are there in both cases.
In this tutorial, Myra Spiliopoulou and Georg Krempl bring together research advances on model learning and adaption for dynamic applications that collect and analyze different sources of dynamic data. In the introductory part of the tutorial, they present the classic stream mining paradigm and summarize the challenges being investigated in the state-of-the-art research.
Organizational information regarding the exam reviews of our lectures can be found under "Study" on the respective lecture webpage.
Myra Spiliopoulou was PC Chair of the IEEE 2016 Int. Symposium on Computer-Based Medical Systems (CBMS 2016), together with Jaakko Hollmen (Univ. Aalto, Finland). CBMS 2016 took place in Belfast and Dublin, June 20-23, 2016. The proceedings are now online as part of the IEEE XPlore digital library under:
The KMD Lab presented two papers at Computer-Based Medical Systems 2016, Dublin, Ireland and Belfast, Northern Ireland .
- "Learning Pressure Patterns for Patients with Diabetic Foot Syndrome" by Uli Niemann, Myra Spiliopoulou, Fred Samland, Thorsten Szczepanski, Jens Gruetzner, Antao Ming, Juliane Kellersmann, Jan Malanowski, Silke Klose and Peter R. Mertens
- "Identifying relevant features for a multi-factorial disorder with constraint-based subspace clustering" by Tommy Hielscher, Myra Spiliopoulou, Henry Voelzke and Jens-Peter Kuehn