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, with particular emphasis on:
- Machine Learning methods for streams and time series with gaps – prediction and feature contribution
- Parsimonious usage of data and features – cost-aware active feature acquisition methods
- Design of human-understandable solutions
Our application areas are:
- Medical Mining I: Treatment prediction, phenotyping in medical research
- Medical Mining II: Human interaction with mHealth apps
- Experiments I: Analysis of participant behaviour in experimental settings
- Experiments II: Experiment design and analysis of human behaviour in human-machine interaction
More on our research can be found here.
Our research is reflected in our teaching curriculum, which is built around the topic of data mining: Students learn underpinnings of data mining in all bachelor courses we offer. In the mandatory courses ITO and WMS of the Bachelor Wirtschaftsinformatik degree, we focus on mining for business applications. In the Recommenders course, we elaborate on the mining methods for static and stream recommenders.
In the courses Data Mining I (two variants, one for bachelor degrees, one for master degrees), students learn fundamentals on algorithms, model evaluation and data preparation. In Data Mining II, students learn learning methods for timestamped data. In the seminars, team projects and individual projects, students learn to design and apply mining and machine learning methods in realistic applications, and they get involved in our research - in team projects and individual projects. Our courses can be found under Study.
News
Best Paper Award at ICBHI 2017
Best Paper Award (2. Position) at the Int. Conf. on Biomedical and Health Informatics (ICBHI 2017) for the Master DKE students Sourabh Dandage, Johannes Huber and Atin Janki: their paper
“Patient Empowerment through summarization of discussion threads on treatments in a patient self-help forum”
Authors: Sourabh Dandage, Johannes Huber, Atin Janki, Uli Niemann, Ruediger Pryss, Manfred Reichert, Steve Harrison, Markku Vessala, Winfried Schlee, Thomas Probst and Myra Spiliopoulou
is a followup of their teamproject on "How patients talk about their tinnitus". Link: here
IEEE Int. Conf. on Data Mining, New Orleans, Louisianna
IEEE Int. Conf. on Data Mining, New Orleans, Louisianna
- Tutorial by Myra Spiliopoulou with Panagiotis Papapetrou on "Mining Cohorts & Patient Data: Challenges and Solutions, for the Pre-Mining, the Mining and the Post-Mining Phases" Nov. 20, 2017
- Panel organized by Myra Spiliopoulou and Naren Ramakrishnan on "Ethics & Professionalism in the age of Social Data" with Huan Liu, Tanushree Mitra, Eirini Ntoutsi and Jilles Vreeken as panelists, Nov. 21, 2017
IEEE ICDM 2017 - Medical Mining Tutorial
IEEE ICDM 2017, New Orleans - Tutorial on Mining Cohorts and Patient Data, Monday November 20, 10:30 - 13:00 (NEW DATE)
FIN-Forschungspreis 2015
Dr. Georg Krempl and Daniel Kottke have been awarded with the "FIN-Forschungspreis 2015". This year, the best publication of the faculty has been chosen to be "Optimised probabilistic active learning (OPAL): For Fast, Non-Myopic, Cost-Sensitive Active Classification." by Georg Krempl, Daniel Kottke, and Vincent Lemaire. It was published in the special issue of the ECMLPKDD 2015 Journal Track in the Journal of Machine Learning.