News

Student Reseach Award for Uli Niemann

14.01.2015 -

The faculty of computer science Magdeburg has awarded KMD staff member Uli Niemann the faculty's 2014 Student Research Award for the article

U. Niemann, H. Völzke, J.-P. Kühn, and M. Spiliopoulou. Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis. Expert Systems with Applications, (41)11:5405-5415, Elsevier BV, September 2014.

The article was written within the frame of scientific individual project during Uli's Master study of Business Information Systems and employs data from the Study of Health in Pomerania (SHIP). This work is a cooperation of the KMD research lab with the University Medicine Greifswald.

The prestigious journal Expert Systems with Applications (ESWA) from Elsevier emphasises on artificial intelligence and machine learning methods with a special focus on challenging practical applications. The article was submitted on 25.10.2013 and accepted on 20.02.2014. ESWA has an Impact Factor of 1.965 (2013) and a Five-Year Impact Factor of 2.254.

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Tutorial @ PAKDD 2013

14.12.2016 -

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.

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