Lehr- und Forschungseinheit für Datenbanksysteme

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Big Data Management and Analytics (WS 2020/21)



  • Umfang: 3+2 hours weekly (equals 6 ECTS)
  • Lecture: Prof. Dr. Matthias Schubert
  • Assistants: Daniyal Kazempour, Sandra Obermeier
  • Required: Lecture "Database Systems I" or equivalent
  • Beneficial: Lecture "Knowledge Discovery in Databases I" or equivalent
  • Audience: The lecture is directed towards Bachelor students (5th term) and Master students in Mediainformatics, Bioinformatics, and Informatics

Time and Locations

All times are c.t. (cum tempore)

Component When Where Starts at
Lecture Tue, 13.00 - 16.00 h Room S 004 (Schellingstr. 3) 03.11.2020
Tutorial 1 Wed, 16.00 - 18.00 h Room D Z007 (HGB) 12.11.2020
Tutorial 2 Wed, 18.00 - 20.00 h Room D Z007 (HGB) 12.11.2020
Tutorial 3 Thu, 14.00 - 16.00 h Room D Z007 (HGB 12.11.2020
Tutorial 4 Thu, 16.00 - 18.00 h Room D Z007 (HGB 12.11.2020


In almost all areas of business, industry, science, and everybody's life, the amount of available data that contains value and knowledge is immense and fast growing. However, turning data into information, information into knowledge, and knowledge into value is challenging.To extract the knowledge, the data needs to be stored, managed, and analyzed. Thereby, we not only have to cope with increasing amount of data, but also with increasing velocity, i.e., data streamed in high rates, with heterogeneous data sources and also more and more have to take data quality and reliability of data and information into account. These properties referring to the four V's (Volume, Velocity, Variety, and Veracity) are the key properties of "Big Data". Big Data grows faster than our ability to process the data, so we need new architectures, algorithms and approaches for managing, processing, and analyzing Big Data that goes beyond traditional concepts for knowledge discovery and data mining.

This course introduces Big Data, challenges associated with Big Data, and basic concepts for Big Data Management and Big Data Analytics which are important components in the new and popular field Data Science.


Course Schedule