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050038 VU Scientific Data Management (2014S)
Continuous assessment of course work
Labels
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).
- Registration is open from Sa 01.02.2014 09:00 to Su 23.02.2014 23:59
- Deregistration possible until Mo 31.03.2014 23:59
Details
max. 50 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
- Friday 07.03. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 14.03. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 21.03. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 28.03. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 04.04. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 11.04. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 02.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 09.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 16.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 23.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 30.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 06.06. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 13.06. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 20.06. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 27.06. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Information
Aims, contents and method of the course
Assessment and permitted materials
All projects have to be completely and on time solved. The authors have to be able to present and explain their approaches to the solution. The final test will be on June 27, 2014. Each Student has to achieve at least 50% of possible points.
Minimum requirements and assessment criteria
Communication of knowledge about key data structures of Scientific Computing and organization of scientific information in scientific data repositories (data warehouse, database, file, distributed or parallel data management system, etc.). Through their practical work on concrete projects, the students acquire skill for application of these systems and techniques in Scientific Computing and for information and knowledge extraction by means of appropriate query mechanisms and algorithms.
Examination topics
Each lecture topic block is associated with one practical project.
Reading list
The suggested literature is available in each slide file.
Association in the course directory
Last modified: Mo 07.09.2020 15:29
data science paradigms, objectives and methods of Scientific Data Management, BIG Data paradigm, features and taxonomy of modern data-intensive scientific applications, scientific libraries and databases, Web data extraction techniques, data stream Management and Analysis, distributed database Systems architecture and models (distribution, heterogeneity, autonomy), fragmentation, query optimization, parallel database Systems, federated database Systems, structured and semantic data Integration, data curation, parallel and distributed data warehouses, databases in Grids and Clouds, Scientific Streaming Cloud, knowledge discovery techniques, provenance- and dataspace-based scientific research