Lecture “Data Warehousing and Data Mining Techniques”

Master Informatik, Master Wirtschaftsinformatik
4 or 5 (depending on course of study and exam regulations)
Regular Dates: 
Tuesdays, 15:00-17:15, IZ 160
The first lecture day will be Tuesday the 25th of October

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 completely 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
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
Association rule mining, sequence patterns, time series
Classification: Decision trees, naive Bayes classifications, SVM
Cluster analysis: K-means, hierarchical clustering, aglomerative clustering, outlier analysis



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

Please drop your solutions into the silver homework box located at the second floor of Informatikzentrum at the Information Systems Institute (in front of the elevators) until Tuesday, before 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”).

ITIS students outside Braunschweig can send their solutions per mail at silviuatifis [dot] cs [dot] tu-bs [dot] de




Date Topic Slides Exercises Video
25.10.11 Introduction Slides - Print Slides Exercise 1 Video1

- Architecture

- Data Modeling (Conceptual Model)

Slides - Print Slides  None Video2
08.11.11  Data Modeling (Logical & Physical Models) Slides - Print Slides Exercise 2 Video 3
15.11.11  Indexes Slides - Print Slides Exercise 3 Video 4
22.11.11 Optimization Slides - Print Slides None Video 5
29.11.11 OLAP Operations & Queries Slides - Print Slides None Video 6
06.12.11 Build the DW, ETL Slides - Print Slides Exercise 4 Video 7
13.12.11 Real-Time DW Slides - Print Slides None Video 8
20.12.11 Data Mining Overview, Association Rule Mining Slides - Print Slides None Video 9
10.01.12 Sequence Pattern Mining & Time Series Slides - Print Slides Exercise 5 Video 10
17.01.12 Classification Slides - Print Slides None Video 11
24.01.12 Clustering Slides - Print Slides None Video 12
31.01.12 AdaBoost Slides - Print Slides None Video 13
07.02.12 DWs in Practice - OLD Video  -  - Video 14


Data Mining Literature:

The following paper presents an overview of the main techniques we have discussed in the data mining part of the lecture, and should be used as a starting point for each algorithm. Please read the citations of each technique for more speciffic information:

X. Wu, V. Kumar, J. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. McLachlan, A. Ng, B. Liu, P. Yu, Z. Zhou, M. Steinbach, D. Hand, D. Steinberg. Top 10 Algorithms in Data Mining.

Journal Knowledge and Information Systems archive, Volume 14 Issue 1. [http]




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