050038 VU Scientific Data Management (2015S)
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 Mo 02.02.2015 09:00 to Mo 23.02.2015 23:59
- Deregistration possible until Su 15.03.2015 23:59
Details
max. 50 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
- Friday 06.03. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 13.03. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 20.03. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 27.03. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 15.04. 18:30 - 21:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 17.04. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 24.04. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 08.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Monday 11.05. 19:00 - 21:00 PC-Unterrichtsraum 6, Währinger Straße 29 2.OG
- Friday 15.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 22.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 29.05. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 05.06. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 12.06. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 19.06. 09:45 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Friday 26.06. 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 26, 2015. 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. Each Project is supported by a modern programming environment.
Reading list
In jedem Foliensatz befindet sich die entsprechende empfohlene Literaturliste.
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 and related technologies (Map/Reduce, Hadoop, etc.) , 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, data preparation for the analysis, knowledge discovery techniques (sequential, parallel and distributed), data-intensive scientific workflows, provenance- and dataspace-based scientific research