In this course, we examine the aspects regarding building maintaining and operating data warehouses as well as give an insight to the main knowledge discovery techniques. The course deals with basic issues like storage of the data, execution of the analytical queries and data mining procedures.

The general structure of the course is:

• Typical dw use case scenarios
• Basic architecture of dw
• Data modelling on a conceptual, logical and physical level
• Multidimensional E/R modelling
• Cubes, dimensions, measures
• Query processing, OLAP queries (OLAP vs OLTP), roll-up, drill down, slice, dice, pivot
• MOLAP, ROLAP, HOLAP
• SQL99 OLAP operators, MDX
• Snowflake, star and starflake schemas for relational storage
• Multimedia physical storage (linearization)
• DW Indexing as search optimization mean: R-Trees, UB-Trees, Bitmap indexes
• Other optimization procedures: data partitioning, star join optimization, materialized views
• ETL
• Association rule mining, sequence patterns, time series
• Classification: Decision trees, naive Bayes classifications, SVM
• Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis