Lecture “Data Warehousing and Data Mining Techniques”

Information
Classification: 
Master Informatik / Wirtschaftsinformatik
Credits: 
4 or 5 (depending on examination rules)
Exam: 
Oral
Regular Dates: 
Thursdays, 15:00-17:15, IZ 160, starting on the 28th of October
Contents
Contents: 

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.

Course will be tought completly in English.

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

 

Materials

Note

Achieving at least 50% of the total homework points is advisable.

Please send your solutions to silviuatifis [dot] cs [dot] tu-bs [dot] de until Friday, after the next lecture (the date is mentioned on each exercise sheet). You may answer in either German or English. You are encour-aged to work in teams of 2 students (not more than 2), and send your solution as a team. Please mention in your email the name of both students together with the corresponding inmatriculation numbers (“Matrikelnummer”).

 

Download

 

Date Topic Slides Exercises Video
28.10.10 Introduction Slides - Print Slides Exercise 1 Video 1
04.11.10

- Architecture

- Data Modeling (Conceptual Model)

Slides - Print Slides Exercise 2 Video 2
11.11.10  Data Modeling (Logical & Physical Models) Slides - Print Slides None Video 3
18.11.10  Indexes Slides - Print Slides Exercise 4 - as Doc - Solutions Video 4
25.11.10  Optimization Slides - Print Slides None Video 5
02.12.10 OLAP Operations & Queries Slides - Print Slides Exercise 6 Video 6
09.12.10 Build the DW, ETL Slides - Print Slides None Video 7
16.12.10 Data Mining Overview, Association Rule Mining Slides - Print Slides Exercise 8 - Solutions Video 8
06.01.11 Sequence Pattern Mining & Time Series Slides - Print Slides None Video 9
13.01.11 Classification Slides - Print Slides Exercise 10 - Solutions Video 10
20.01.11 Clustering Slides - Print Slides None Video 11
27.01.11 Decision Support Systems Slides - Print Slides None Video 12
03.02.11 DWs in Praxis None None Video 13

 

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File Solutions Ex8.pdf14/02/11 10:38 am584.03 KB
File Solutions Ex10.pdf14/02/11 10:38 am579.77 KB