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.

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.

Prof. Myra Spiliopoulou and Uli Niemann are organizing a workshop on "Mining and Policy-Making for Epidemic Surveillance" (EpiMine), together with Prof. Panagiotis Papapetrou and Maria Bampa from the University of Stockholm, Sweden.

The goal of EpiMine is to promote research in the areas of knowledge discovery, data mining, and policy-making that contribute to the realization and further development of effective and timely prevention measures and strategies to contain epidemics such as COVID-19.

The workshop will be held on 07 December 2021 in conjunction with the IEEE International Conference on Data Mining (ICDM) in Auckland, New Zealand.

More information on the workshop's webpage.

Miro Schleicher has received the Marco Ramoni best paper award at the 20th Artificial Intelligence in Medicine (AIME) conference for his paper 'When can I expect the mHealth user to return? Prediction meets time series with gaps' (Miro Schleicher, Rüdiger Pryss, Winfried Schlee and Myra Spiliopoulou).

This work is within the frame of the UNITI project that encompasses machine learning methods for choosing the best treatment for each tinnitus patient. Treatments have an mHealth component, which assists the users towards self-empowerment and daily management of their disease. However, mHealth apps demand self-discipline; some users give up or interact very irregularly. The proposed method learns from the data of each user and from the absence of data, and it predicts if and when a user will start interacting again with the app.

The dates for the LAST ATTEMPT exams in DM4BA, DM I, DM II, Recommenders, ITO, and WMS are:

• July 21, 13:30-16:30
• Sept 7, 13:30-16:30

NOTE: Last attempt exams are offered exclusively for degrees that prescribe a last attempt, and state that this attempt must be an oral exam.

The next slots for last attempt exams will be offered after the teaching period of winter term 2021/2022.

Important announcement regarding registration process:

In order to stay more compliant to the social distancing guidelines, the registration for the exam will be conducted slightly differently.

1. Download, print, and fill out the examination registration form from the examination office. Fill all fields except the date and time.
2. Submit the document to Mr. Knopke electronically.
3. The next available time slot will be assigned to you, and Mr. Knopke forwards the updated form to the examination office. You will be informed about the date and time for your exam.

Please note that the registration must be at least 14 days before the exam.

Dear students,
we would like to inform you about the current state of affairs regarding the topic exam procedure.

Change in the examination form for the courses:
- Data Mining II - Advanced Topics in Data Mining
- Recommenders
- Wissensmanagement - Methoden und Werkzeuge
- Informationstechnologie in Organisationen
- Data Mining I - Introduction to Data Mining
- Data Mining - Einführung in Data Mining (DM4BA)

New form is:
"Elektronische schriftliche Prüfung (unbeaufsichtigt; In den Allgemeinen Bestimmungen als „schriftliche Ausarbeitungen“ bezeichnet.)" i.e. these exams are digital without supervision and open-book. These exams take place on the same day and time as originally scheduled.

Stay healthy!
KMD Team