Web Communication & Information Systems PT

CRM & Information Mining I

level of course unit

second cycle, Master

Learning outcomes of course unit

Instruction in the basic topics of data warehouse systems and information mining (data/web/social Content) will be given in this course. The material will be structured as follows:
Data warehouse systems: factual knowledge and content competence; architecture and structure of data warehouses; difference between transactional data and data analysis; basic knowledge of multidimensional and practical data modeling (star schema, snowflake schema, etc., plus selection of the correct model); basics of the ETL process, OLAP operations and graphic modeling with various data models, e.g. M ER storage of multidimensional data: ROLAP (relational) versus MOLAP (multidimensional variant); index structures for data warehouses: multidimensional index structures and their optimizing possibilities: star joins and partitioning, optimization of OLAP operations.
Information mining: basic principles of information mining (statistics, machine learning and database systems); data structures for data mining and KDD; clustering: hierarchical cluster, k-means, DBSCAN; basic techniques (association rules (a priori algorithm, etc.), classification (naive Bayes, etc.), regression, etc.); support vector machines; decision trees; machine learning in practice; time series; application of the techniques and methods in text, data, the web and social web environment; applications for data mining concepts (e.g. in price setting for budget flights, in calculating insurance premiums, in credit decisions and in marketing, etc.).

prerequisites and co-requisites

not applicable

course contents

The students will acquire detailed knowledge of the basic techniques, tasks and areas of application of a data warehouse system.
The students will also acquire detailed skills in methods for obtaining specialized know-how, and will be aware of their multidimensional modeling possibilities.
In this connection they will also be conversant with the various established means of storage (ROLAP, MOLAP), and will be able to apply these specifically and increase system efficiency by using optimizing techniques.
The second focal point of this course is acquiring in-depth knowledge of available algorithms and methods of information mining. The students will be able to use these specifically for data and web mining.

recommended or required reading

- Andreas Bauer, Holger Günzel: Data-Warehouse-Systeme : Architektur, Entwicklung, Anwendung, Heidelberg : dpunkt-Verl., 2001;
- Ian H. Witten, Eibe Frank: Data Mining: praktische Werkzeuge und Techniken für das maschinelle Lernen, Hanser, 2001.
- Wolfgang Lehner: Datenbanktechnologie für Data-Warehouse-Systeme : Konzepte und Methoden, Heidelberg, dpunkt, 2003.

assessment methods and criteria

final examination

language of instruction


number of ECTS credits allocated


planned learning activities and teaching methods

group work, individual work, presentation, duscussion

semester/trimester when the course unit is delivered


course-hours-per-week (chw)


name of lecturer(s)

Dr. Michael Kohlegger

year of study


recommended optional program components

not applicable

course unit code


type of course unit

compulsory (integrated lecture)

mode of delivery

in-class course

work placement(s)

not applicable