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This entry is from Winter semester 2019/20 and might be obsolete. You can find a current equivalent here.
CS 671 — Data Integration
(dt. Datenintegration)
Level, degree of commitment | Specialization module, compulsory elective module |
Forms of teaching and learning, workload |
Lecture (2 SWS), recitation class (2 SWS), 180 hours (60 h attendance, 120 h private study) |
Credit points, formal requirements |
6 CP Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks. Examination type: Oral or written examination |
Language, Grading |
German,The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Data Science. |
Duration, frequency |
One semester, Zweijährlich im Sommersemester |
Person in charge of the module's outline | Prof. Dr. Bernhard Seeger |
Contents
- Semantic data models
- Extraction of data and metadata
- data preprocessing
- Techniques of schema transformation
- Fast loading of data
- Architecture for data warehouses
- Online analysis in data warehouses
- Continuous loading and data streams (message queuing)
- Coupling techniques for database systems
- Data exchange on the Web
Qualification Goals
- Knowledge of semantic data models,
- Learn techniques for linking databases,
- Acquisition of knowledge of techniques in schematic transformation,
- Principles of data warehousing,
- Analysis techniques for large databases,
- Principles of message queuing,
- Practice of scientific working methods (recognition, formulation, solving problems, training of abstraction skills),
- Training of oral communication skills in the exercises by practicing free speech in front of an audience and during discussion.
Prerequisites
None. The competences taught in the following modules are recommended: either Algorithms and Data Structures or Practical Informatics II: Data Structures and Algorithms for Pre-Service-Teachers, Database Systems.
Applicability
Module imported from M.Sc. Data Science.
It can be attended at FB12 in study program(s)
- B.Sc. Data Science
- B.Sc. Computer Science
- B.Sc. Business Informatics
- M.Sc. Data Science
- M.Sc. Computer Science
- M.Sc. Mathematics
- M.Sc. Business Informatics
- M.Sc. Business Mathematics
- LAaG Computer Science
When studying M.Sc. Computer Science, this module can be attended in the study area Specialization Modules in Computer Science.
Die Wahlmöglichkeit des Moduls ist dadurch beschränkt, dass es der Praktischen Computer Science zugeordnet ist.
Recommended Reading
- Han,Kamber: Data Mining: Concepts and Techniques, Morgan Kaufmann
- Lehner: Datenbanktechnologie für Data-Warehouse-Systeme, Dpunkt
- Conrad: Föderierte Datenbanksysteme - Konzepte der Datenintegration. Springer-Verlag
- Naumann: Quality-Driven Query Answering for Integrated Information Systems, Springer-Verlag
Please note:
This page describes a module according to the latest valid module guide in Winter semester 2019/20. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:
- Winter 2016/17
- Summer 2018
- Winter 2018/19
- Winter 2019/20
- Winter 2020/21
- Summer 2021
- Winter 2021/22
- Winter 2022/23
- Winter 2023/24
The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.
The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.