Universität Wien

052813 VU Scientific Data Management (2021S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

The course will be held online until further notice.

  • Tuesday 02.03. 15:00 - 16:30 Digital
  • Thursday 04.03. 08:00 - 09:30 Digital
  • Tuesday 09.03. 15:00 - 16:30 Digital
  • Thursday 11.03. 08:00 - 09:30 Digital
  • Tuesday 16.03. 15:00 - 16:30 Digital
  • Thursday 18.03. 08:00 - 09:30 Digital
  • Tuesday 23.03. 15:00 - 16:30 Digital
  • Thursday 25.03. 08:00 - 09:30 Digital
  • Tuesday 13.04. 15:00 - 16:30 Digital
  • Thursday 15.04. 08:00 - 09:30 Digital
  • Tuesday 20.04. 15:00 - 16:30 Digital
  • Thursday 22.04. 08:00 - 09:30 Digital
  • Tuesday 27.04. 15:00 - 16:30 Digital
  • Thursday 29.04. 08:00 - 09:30 Digital
  • Tuesday 04.05. 15:00 - 16:30 Digital
  • Thursday 06.05. 08:00 - 09:30 Digital
  • Tuesday 11.05. 15:00 - 16:30 Digital
  • Tuesday 18.05. 15:00 - 16:30 Digital
  • Thursday 20.05. 08:00 - 09:30 Digital
  • Thursday 27.05. 08:00 - 09:30 Digital
  • Tuesday 01.06. 15:00 - 16:30 Digital
  • Tuesday 08.06. 15:00 - 16:30 Digital
  • Thursday 10.06. 08:00 - 09:30 Digital
  • Tuesday 15.06. 15:00 - 16:30 Digital
  • Thursday 17.06. 08:00 - 09:30 Digital
  • Tuesday 22.06. 15:00 - 16:30 Digital
  • Thursday 24.06. 08:00 - 09:30 Digital
  • Tuesday 29.06. 15:00 - 16:30 Digital

Information

Aims, contents and method of the course

This course introduces central methods and approaches for the organization and analysis of large and scientific data: distributed data repositories, index, and access structures, hashing, classification, and clustering techniques. Specific methods for structured data such as sets, images, documents, and graphs are discussed. In programming exercises, students learn ways to realize similarity search and data mining on large data, e.g., parallelisation with MapReduce, Apache Spark, or filter-refinement techniques.

Subject-specific goals:
- Analysis of scientific data
- Interpretation and evaluation of results of the analysis process
- Choosing and applying techniques for structured data
- Implementation of scalable solutions for large amounts of data
- Users support and advice

Generic goals:
- Teamwork
- Improvement of programming skills
- Understanding of interplay in data mining and scientific computing

Assessment and permitted materials

Active participation
Work on exercise-sheets
Work on programming assignments in groups
Final exam

Minimum requirements and assessment criteria

It is recommended to complete the following courses beforehand:
- Algorithmen und Datenstrukturen
- Datenbanksysteme
- Software Engineering
- Netzwerktechnologien

30% exercise sheets
30% programming assignments in teams
40% written final exam

>87,00%: 1
75,00% - 86,99: 2
63,00% - 74,99%: 3
50,00% - 62,99%: 4
< 50%: 5

Examination topics

Scientific Data and Feature Spaces
Clustering
Big Data Frameworks
Searching Numerical Data
Searching Sets
Searching & Mining Graphs
Analyzing Large Networks

Reading list

Ester M., Sander J. Knowledge Discovery in Databases: Techniken und Anwendungen.
J. Leskovec, A. Rajaraman, J. Ullman. Mining of Massive Datasets.
J. Han, M. Kamber, J.Pei.Data Mining: Concepts and Techniques.
I. H. Witten , E. Frank, M. A. Hall. Data Mining: Practical Machine Learning Tools and Techniques.

Association in the course directory

Module: SDM

Last modified: Fr 12.05.2023 00:13