Contents:
In this course, we examine the aspects of building, maintaining, and operating data warehouses and give an insight into the main knowledge discovery techniques. The course deals with basic issues like the storage of data, execution of analytical queries, various data mining procedures and a short introduction to deep learning.
This course will be held in English.
The general structure of the course is as follows:
- Typical DW use case scenarios
- Basic architecture of DW
- Data modelling on conceptual, logical and physical levels
- 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, agglomerative clustering, outlier analysis
- Bootstrapping, Bagging, adaptive Boosting
- Deep Learning Intro
