Universität Wien

052813 VU Scientific Data Management (2023S)

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

  • Wednesday 01.03. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 07.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 08.03. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 14.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 15.03. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 21.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 22.03. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 28.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 29.03. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 18.04. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 19.04. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 25.04. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 26.04. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 02.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Seminarraum 3, Währinger Straße 29 1.UG
  • Wednesday 03.05. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 09.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 10.05. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 16.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 17.05. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 23.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 24.05. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Wednesday 31.05. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 06.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 07.06. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 13.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 14.06. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 20.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 21.06. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
  • Tuesday 27.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Wednesday 28.06. 18:30 - 20:00 Hörsaal 3, Währinger Straße 29 3.OG
    Seminarraum 7, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

This course will be taught in English and take place on-site. The lectures will be streamed, recorded, and made available on Moodle.

The course introduces central methods for the organization and analysis of large and scientific data such as distributed data repositories, index data structures, hashing, classification, and clustering techniques. Specific methods for structured data such as sets, images, text documents, and graphs are discussed.

The lectures are complemented by exercises and programming assignments. Students will learn ways to realize similarity search and data mining on large data, e.g., using 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
- Support and advice of users

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

Assessment and permitted materials

Active participation is a requirement for passing the course. The overall grade is composed as follows:

30% Exercises (individual work)
30% Programming assignments (group work)
20% Written midterm exam (individual work)
20% Written final exam (individual work)

Minimum requirements and assessment criteria

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

Grades will be given according to the following scheme:
100.00 - 87.00: 1
75.00 - 86.99: 2
63.00 - 74.99: 3
50.00 - 62.99: 4
00.00 - 49.99: 5

Examination topics

All topics covered in class, the exercises, and the programming assignments.

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

Reading list

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.

Further literature and references to research papers will be provided via Moodle.

Association in the course directory

Module: SDM

Last modified: Mo 19.06.2023 13:27