Recording of the lecture:

Access to lecture recordings via the Stream Player of the ATIS. 

Lecture material:

The lecture notes and other course materials can be found on the following folder at our BSCW Server.

Objective:

At the end of the lecture, the participants should be aware of – and able to explain – the necessity of data warehousing and of data mining concepts. They should be able to assess and compare different approaches of management and analysis of large datasets with respect to efficiency and applicability. The participants should have gained an insight into the current research issues in the area of data warehousing and data mining and should understand which problems are currently unsolved.

Content:

Data warehouses and data mining raise much interest from practitioners with huge amounts of data, e.g., in retail, finance and the insurance sector. Both warehousing and mining are motivated by the desire for keeping track of large and possibly distributed datasets and for extracting interesting relations from such data, ideally with minimal effort. A data warehouse is a repository which is fed with data from one or more operational database systems. The data is preprocessed allowing for a fast evaluation of complex analytical queries (OLAP, Online Analytical Processing). In contrary, data mining provides techniques for discovering patterns in large datasets.

Literature:

Jiawei Han, Micheline Kamber: Data Mining: Concepts and Techniques. 2nd edition,
Morgan Kaufmann Publishers, March 2006.

Elective literature:

Further literature will be mentioned at the end of each chapter in the lecture slides.