Knowledge Management & Discovery Lab
KMD stands for "Knowledge Management and Discovery" .
The KMD Lab is part of the department Technical and Business Information Systems (ITI) .
The KMD Lab has been established in February 2003.
In the KMD lab, we develop and apply data mining methods for dynamic environments:
- to understand the progress of diseases and the long-term impact of interventions
- to make recommenders adaptive to changing user interests and market conditions
- to monitor opinions
- to learn actively from the data, minimizing human effort.
Our methods are mostly in the field of stream mining. We develop stream mining methods, methods that exploit timestamped data, and dedicated stream algorithms for recommendation engines, opinion analysis, patient records and longitudinal epidemiological data.
Our research is reflected in our teaching programs. With KMD, students learn fundamentals of data mining and recommendation engines. They learn to design and apply mining and machine learning methods in realistic applications, and get involved in our research - in team projects and individual projects.
All our projects are listed here.
News
Inspection of Examinations / Klausureinsicht
Exam inspections / Klausureinsicht:
All students who wish to inspect their exams from the previous semester can do so on the 26th of April, 2023. Those of you who want to inspect your exam are requested to write to the person responsibe for your course in order to arrange a personal appointment.
| Course | Contact Person |
| Data Mining I & II | Vishnu Unnikrishnan |
| DM4BA | Miro Schleicher |
| ITO | Maik Büttner |
| Recommenders | Noor Jamaludeen |
| WMS | Christian Beyer |
An appointment is required to arrange an exam inspection. The appointments will be offered on 26th April, 2023, and are non-transferable.
Tutorial Mining and multimodal learning from complex medical data
The proliferation of medical data and applications has increased the need for extracting useful knowledge that can be effectively used by the healthcare domain experts. The motivation of this tutorial is to address the complexity of medical data with specific focus on their temporal nature. While earlier tutorials in both AIME as well as other related venues such as KDD and ECML/PKDD have explored the application and utility of machine learning on medical data, there has yet been limited focus on the challenges emerging from the sequential and temporal nature of such data, as well as on the need for trust by the medical practitioners.
Dates for LAST ATTEMPT exams in ITO
The date for the LAST ATTEMPT exam in ITO is:
December 12, 14:00 and 15:00
NOTE: Last attempt exams are offered exclusively for degrees that prescribe a last attempt, and state that this attempt must be an oral exam.
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.
Download, print, and fill out the examination registration form from the examination office. Fill all fields except the date and time.
Submit the document to Mr. Knopke electronically Deadline 25.11.2022.
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.
'Best paper award' at AIME 2022
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.
20th International Conference on Artificial Intelligence in Medicine (AIME 2022)
20th International Conference on Artificial Intelligence in Medicine (AIME 2022), Halifax
The KMD team was present with two scientific contributions:
- 'When can I expect the mHealth user to return? Prediction meets time series with gaps' by Miro Schleicher, Rüdiger Pryss, Winfried Schlee and Myra Spiliopoulou: the new machine learning method analyses the behaviour of users towards an mHealth app, learns from the absence of data and predicts if and when a user will start using the app again.
- 'Discovering Instantaneous Granger Causalities in Non-stationary Categorical Time Series Data' by Noor Jamaludeen, Vishnu Unnikrishnan, André Brechmann, and Myra Spiliopoulou: the new machine learning method analyses the data of an auditory category learning experiment, and it identifies characteristic patterns that distinguish between learners and non-learners.
Myra Spiliopoulou, together with Panos Papapetrou (Univ Stockholm) also presented a tutorial on 'Machine learning for complex medical temporal sequences'. Her part was on 'Dealing with Missingness/Gaps'.
Lange Nacht der Wissenschaft 2022
Nehmen Sie an unseren Laborexperimenten teil! Participate in our Lab experiments!
Wir führen am Samstag, 11. Juni ab 18Uhr, Mitmachexperiment im Labor 021 durch.
Mehr Informationen:
https://wissenschaft.magdeburg.de/programm/ovgu/2596-forschung-trifft-crowdsourcing
Paper accepted in Frontiers in Virtual Reality
Anne Rother's paper, "Virtual Reality for Medical Annotation Tasks – A Systematic Review", has been accepted in Frontiers.
https://www.frontiersin.org/articles/10.3389/frvir.2022.717383/full