Knowledge Management & Discovery Lab

Logo 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:

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.



Registration and Voting


Hallo liebe Studierende,

hier findet ihr die Erklärung, wie ihr euch für unsere Kurse registriert: Kursanmeldung

und hier die Erklärung zu unserem Votierungssystem: Votierung

Dear students,

You can find out how to register for an exercise here: Registration

and here you will learn how our voting procedure works: Voting

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Tutor*innen für Data Mining (DM4BA) gesucht


Liebe Studierende, wir suchen für das Wintersemester 2023 noch nach 2 Tutor*innen für DM4BA. Wenn ihr also Ahnung von Data Mining und Machine Learning habt und euch das vorstellen könnt, dann meldet euch bei oder bei

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Participate in our online experiment!


In cooperation with University Medicine Greifswald (Germany) and University of L’Aquila (Italy), we conduct an online experiment on medical annotation tasks.

Join under,
The survey is anonymous and helps us in our research. Thanks and have fun!

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Colloquium - Potentials and Limitations of observational population-based studies

July 13, 2023 (15:00 cet in room G29-412 and zoom)
Potentials and Limitations of observational population-based studies
Dr. Till Ittermann, (Head of the Statistical Method Unit, Institute for Community Medicine, University Medicine Greifswald)

There is a broad range of medical research questions which can be addressed by population-based studies including the description of prevalence and incidence of diseases and risk factors, the definition of reference intervals for clinical biomarkers, the investigation of associations between potential (genetical) risk factors and diseases, the calculation and validation of prediction models for certain diseases, and data mining analyses. Limitations, which has to be taken into account, derive from selection bias, confounding bias and information bias. This talk will give a summary on the potentials and limitations of population-based studies using examples from the Study of Health in Pomerania.


Join Zoom Meeting:
Meeting ID: 661 9452 8351
Passcode: 832878

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DM4BA-SoSo23 25.05.2023 Keine Präsenzübung


Am 25.05.2023 findet aufgrund von Krankheit keine Präsenzübung für DM4BA statt. Die Erarbeitung des Übungsblattes 4 erfolgt durch die Studierenden selbst. Alle weiteren Hinweise sind in Moodle zu finden.

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Symposium Productive Teaming



Productive Teaming Symposium


Current technological development enables highly complex, intelligent, adaptable and autonomous cyber-engineered systems that can support, complement and surpass human cognitive abilities and skills in solving the new challenges of tomorrow. This constant further development of increasingly powerful and intelligent technologies is currently leading to equally new and complex challenges in dynamic production systems, which must be mastered quickly and consistently. A central challenge in the field of production systems in the future will be to solve highly complex problems with countless parameters that far exceed the capacities of gap and conventional human teams. Therefore, in order to solve these problems and to increase productivity, it is absolutely necessary to form teams of people and production systems that are specialized in such “teaming” between people and production systems. "Productive Teaming" is a joint research initiative of the TU Chemnitz, the TU Ilmenau and the OVGU Magdeburg, which has developed from the already existing research and innovation network "CHIM". The aim of this initiative is to use overarching themes to better understand the teaming between human and artificial agents and to find an answer to the following research question, among others: Can intelligent systems be cognitively augmented in such a way that they are able to use the skills and Dynamically anticipate the needs of the team partner within this process?

More informations about the research initiative you can find here: CHIM.

TU Chemnitz, 28.03.2023
(Author: Marlies Facius)

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Overview of Team Projects 2023


Please read the general overview in detail and apply with the expected information directly to Prof. Spiliopoulou.
Here you can find the:

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Last Modification: 16.06.2023 - Contact Person: