- DBS hat einen neuen Webauftritt: http://www.dbs.ifi.lmu.de
- Ab SoSe 2017 werden Seminarplätze zentral verteilt. Interessierte Studenten sollen sich ab Mitte Januar über die verschiedenen Angebote auf den jeweiligen Lehrstuhlseiten informieren.
- Matthias Schubert zum apl. Professor ernannt
- Vertretung der Professur Data Science durch Prof. Dr. Matthias Schubert
- Prof. Kriegel als 2016 Top 10 Most Influential Scholar genannt
- Website zum Elite Studiengang Data Science ist online
- DASFAA 2016 10-Year Best Paper Award
- more ...
Lehrstuhl für Datenbanksysteme und Data Mining
(Database Systems and Data Mining group)
Neuer Webauftritt unter http://www.dbs.ifi.lmu.de
Prof. Dr. Thomas Seidl, chair
Prof. Dr. Peer Kröger, apl. professor
Prof. Dr. Matthias Schubert, apl. professor
Prof. Dr. Volker Tresp, honorary professor
Prof. Dr. Hans-Peter Kriegel, chair emeritus
We advance data science, data mining, machine learning and database technology for artificial intelligence in order to support the analysis of huge and complex data sets from various domains including engineering, business, humanities, life sciences, etc. Both our fundamental and our applied research activities inspire and support each other. Our data science lab connects scientists, students, and industry.
Our teaching provides thorough insights into the conceptual and mathematical
background of data analytics as well as practical experience in real application
scenarios. We contribute
by lectures, tutorials, seminars and practicals to all
computer science programs at LMU and to the elite master program in Data Science.
Examples for our research areas include the following topics:
- Data mining, machine learning and knowledge discovery
- High-dimensional data, time series, subspace clustering, network analysis
- Stream data mining (MOA), sensor data mining, time series, mobile objects,
spatio-temporal data, process mining - Visual data analytics, interactive data mining, distributed processing
of data mining tasks
- Data management and data access technology
- Adaptive similarity models for multimedia, spatial and uncertain objects
- Efficient similiarity search algorithms, approximation techniques
- Indexing of time series and extended spatial-temporal objects