Abstract
Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data and information processed and disseminated puts Copernicus at the forefront of the big data paradigm, giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. In this short paper, we discuss the challenges of extracting information and knowledge from huge archives of Copernicus data. We propose to achieve this by scale-out distributed deep learning techniques that run on very big clusters offering virtual machines and GPUs. We also discuss the challenges of achieving scalability in the management of the extreme volumes of information and knowledge extracted from Copernicus data. The envisioned scientific and
technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019.
technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019.
Original language | English |
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Title of host publication | EDBT/ICDT 2019 Joint Conference |
Pages | 690-693 |
Number of pages | 4 |
DOIs | |
Publication status | Published - Mar 2019 |
Externally published | Yes |
Event | 22nd International Conference on Extending Database Technology, EDBT 2019 - IST Congress Center, Campus Alameda, Lisbon, Portugal Duration: 26 Mar 2019 → 29 Mar 2019 Conference number: 22 http://edbticdt2019.inesc-id.pt/ |
Publication series
Name | Open Proceedings |
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ISSN (Print) | 2367-2005 |
Conference
Conference | 22nd International Conference on Extending Database Technology, EDBT 2019 |
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Abbreviated title | ECBT 2019 |
Country/Territory | Portugal |
City | Lisbon |
Period | 26/03/19 → 29/03/19 |
Internet address |