Lecture “Data Warehousing and Data Mining Techniques”

Overview

Lecturer: Prof. Dr. Wolf-Tilo Balke

Assistant: Niklas Kiehne

Classification: Master (Computer Science, Business Information Technology, Data Science)

Credits: 5

Exam: Oral/Written

Regular Dates: Thursdays, 9:45-12:15 in Room IZ161

Note: There won't be any video recordings this semester. Thus, participating in presence is highly recommended!

Oral Exam dates: 

• July: 22nd, 24th, 30th, 31st,

• August: 6th, 7th, 10th, 12th, 17th

• September: 21st, 24th, 25th

Course Content

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.

Contents:

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