Universität Wien
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052813 VU Scientific Data Management (2024S)

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

  • Friday 01.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 05.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 08.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 15.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 19.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 22.03. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Seminarraum 12, Währinger Straße 29 2.OG
  • Tuesday 09.04. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 12.04. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 16.04. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 19.04. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 23.04. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Seminarraum 11, Währinger Straße 29 2.OG
  • Friday 26.04. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 30.04. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 03.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Seminarraum 7, Währinger Straße 29 1.OG
  • Tuesday 07.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 10.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 14.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 17.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 21.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 24.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Seminarraum 12, Währinger Straße 29 2.OG
  • Tuesday 28.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 31.05. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 04.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Seminarraum 12, Währinger Straße 29 2.OG
  • Friday 07.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 11.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 14.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 18.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Seminarraum 8, Währinger Straße 29 1.OG
  • Friday 21.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Tuesday 25.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
  • Friday 28.06. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal 3, Währinger Straße 29 3.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. Exercise sessions will be on-site only and attending them is mandatory. The midterm and final exams will be on-site.

The course introduces central methods for organizing and analyzing large and scientific data, such as distributed data repositories, index data structures, hashing, classification, and clustering techniques. In particular, methods for structured data such as sets, images, text documents, and graphs are discussed.

Exercises and programming assignments complement the lectures. Students will learn ways to realize similarity search and data mining on large data, e.g., using parallelization 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

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

Assessment and permitted materials

Active participation
Exercises (individual work)
Programming assignments (group work)
Written midterm exam (individual work)
Written final exam (individual work)

Minimum requirements and assessment criteria

The overall grade is composed as follows:
30% Exercises
30% Programming assignments
20% Written midterm exam
20% Written final exam

To successfully complete the course, you must achieve at least 40% of the points in the midterm exam and at least 40% of the points in the final exam.

Attendance of the lecture parts of the course is voluntary but highly recommended. Attendance of the exercise discussions, programming assignment discussions and the written exams is compulsory to obtain points.

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: We 15.05.2024 10:05