Project information
CERIT Scientific Cloud
(CERIT-SC)
- Project Identification
- CZ.02.1.01/0.0/0.0/16_013/0001802 (kod CEP: EF16_013/0001802)
- Project Period
- 5/2017 - 6/2021
- Investor / Pogramme / Project type
-
Ministry of Education, Youth and Sports of the CR
- Operational Programme Research, Development and Education
- Priority axis 1: Strengthening capacities for high-quality research
- MU Faculty or unit
-
Institute of Computer Science
- Mgr. Aleš Křenek, Ph.D.
- doc. Ing. RNDr. Barbora Bühnová, Ph.D.
- doc. RNDr. Jiří Filipovič, Ph.D.
- doc. Mouzhi Ge, Ph.D.
- Mgr. Samuel Gorta
- Mgr. Jan Horáček
- Ing. Jana Hozzová, Ph.D.
- Mgr. Kristián Katanik
- Mgr. Vojtěch Krajňanský
- Mgr. Aleš Křenek, Ph.D.
- RNDr. Martin Macák, Ph.D.
- Mgr. David Myška
- RNDr. Petra Němcová
- Mgr. et Mgr. Jaroslav Oľha
- Mgr. Marek Pastierik
- RNDr. Tomáš Raček, Ph.D.
- RNDr. Tomáš Rebok, Ph.D.
- Bruno Rossi, PhD
- Bc. Maksym Skoryk
- RNDr. Terézia Slanináková
- Mgr. Radim Šašinka
- Mgr. Matúš Štovčik
- Muhammad Usman, Ph.D.
- Mgr. Vladimír Višňovský
- Project Website
- https://www.cerit-sc.cz/en
Centre CERIT-SC (CERIT Scientific Cloud) is a national centre operating a computing and data storage experimental infrastructure for research and development in the area of flexible e-infrastructures and large in-silico experiments, performed in close collaboration with other scientific disciplines. Its top-level mission can be phrased as “Speeding up the time from ideas to publications (and products) in all research disciplines”. The centre is built on three pillars: hardware resources of sufficient scale to ensure competitiveness, excellent research in specific areas of computer science (i.e. know-how to use the hardware resources efficiently), and long-term collaboration with the user communities (being true research partners of the users, not just providers of “precanned” technical solutions). This project aims at strengthening the first and the second pillar; the third one is currently being funded by other means.
Specifically, significant part of project budget is investment to hardware, pushing the centre’s equipment to the leading edge of available technology. This is complemented with the in-house research programme, with the goal to increase the efficient usage of the current and procured infrastructure.
The research programme will consist of two integrated subprogrammes aimed at big data analysis and high performance computing. Their approach to efficient use of infrastructure is complementary. While the Big Data subprogramme approaches it more top-down, concentrating on data processing methods and software architectures at a higher level, the High Performance Computing subprogramme concentrates on optimization at a lower level, from bottom-up. The Big Data research subprogramme will focus on the research in efficient big data analysis, with special focus on the careful selection of the best-fit data analysis technique to each specific problem setup. The High Performance Computing research subprogramme will focus on software parallelization, acceleration and optimization, including automated frameworks.
Publications
Total number of publications: 39
2020
-
A Cross-domain Comparative Study of Big Data Architectures
International Journal of Cooperative Information Systems, year: 2020, volume: 29, edition: 4, DOI
-
A Large-Scale Replication of Smart Grids Power Consumption Anomaly Detection
Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS), year: 2020
-
Big Data Processing Tools Navigation Diagram
Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, year: 2020
-
CaverDock: A Novel Method for the Fast Analysis of Ligand Transport
IEEE/ACM Transactions on Computational Biology and Bioinformatics, year: 2020, volume: 17, edition: 5, DOI
-
Developing the Quality Model for Collaborative Open Data
Proceedings of the 24th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems - KES 2020, year: 2020
-
Exploiting historical data: Pruning autotuning spaces and estimating the number of tuning steps
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, year: 2020, volume: 32, edition: 21, DOI
-
FlexAlign: An Accurate and Fast Algorithm for Movie Alignment in Cryo-Electron Microscopy
Electronics, year: 2020, volume: 9, edition: 6, DOI
-
How well a multi-model database performs against its single-model variants: Benchmarking OrientDB with Neo4j and MongoDB
Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, year: 2020
-
Research Challenges of Open Data as a Service for Smart Cities
Proceedings of the 10th International Conference on Cloud Computing and Services Science, year: 2020
-
Smart Grids Data Analysis: A Systematic Mapping Study
IEEE Transactions on Industrial Informatics, year: 2020, volume: 16, edition: 6, DOI