Explore the words cloud of the CloudDBAppliance project. It provides you a very rough idea of what is the project "CloudDBAppliance" about.
The following table provides information about the project.
Coordinator |
BULL SAS
Organization address contact info |
Coordinator Country | France [FR] |
Project website | http://clouddb.eu/ |
Total cost | 4˙832˙132 € |
EC max contribution | 4˙832˙132 € (100%) |
Programme |
1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)) |
Code Call | H2020-ICT-2016-1 |
Funding Scheme | RIA |
Starting year | 2016 |
Duration (year-month-day) | from 2016-12-01 to 2019-11-30 |
Take a look of project's partnership.
The project aims at producing a European Cloud Database Appliance for providing a Database as a Service able to match the predictable performance, robustness and trustworthiness of on premise architectures such as those based on mainframes. The project will evolve cloud architectures to enable the increase of the uptake of cloud technology by providing the robustness, trustworthiness, and performance required for applications currently considered too critical to be deployed on existing clouds. CloudDBAppliance will deliver a cloud database appliance featuring: 1. A scalable operational database able to process high update workloads such as the ones processed by banks or telcos, combined with a fast analytical engine able to answer analytical queries in an online manner. 2. A Hadoop data lake integrated with the operational database to cover the needs from companies on big data. 3. A cloud hardware appliance leveraging the next generation of hardware to be produced by Bull, the main European hardware provider. This hardware is a scale-up hardware similar to the one of mainframes but with a more modern architecture. Both the operational database and the in-memory analytics engine will be optimized to fully exploit this hardware and deliver predictable performance. Additionally, CloudDBAppliance will deal with the need to tolerate catastrophic cloud data centres failures (e.g. a fire or natural disaster) providing data redundancy across cloud data centres.
Use Cases Design | Documents, reports | 2020-01-24 15:19:44 |
Use Cases Requirements Analysis | Documents, reports | 2020-01-24 15:19:44 |
Project factsheet | Documents, reports | 2020-01-24 15:19:44 |
Project Portal | Other | 2020-01-24 15:19:44 |
Evaluation Plan | Documents, reports | 2020-01-24 15:19:44 |
Take a look to the deliverables list in detail: detailed list of CloudDBAppliance deliverables.
year | authors and title | journal | last update |
---|---|---|---|
2019 |
Ernesto Jiménez, José Luis López-Presa, Javier MartÃn-Rueda Hybrid binary consensus in anonymous asynchronous systems using coins and failure detectors published pages: 8262-8292, ISSN: 0920-8542, DOI: 10.1007/s11227-019-03001-6 |
The Journal of Supercomputing 75/12 | 2020-01-24 |
2018 |
Ji Liu, Esther Pacitti, Patrick Valduriez A survey of scheduling frameworks in big data systems published pages: 103, ISSN: 2043-9989, DOI: 10.1504/ijcc.2018.093765 |
International Journal of Cloud Computing 7/2 | 2020-01-24 |
2018 |
Afonso, João M.; Fernandes, Gabriel D.; Fernandes, João P.; Oliveira, Filipe; Ribeiro, Bruno M.; Pontes, Rogério; Oliveira, José N.; Proença, Alberto J. Typed Linear Algebra for Efficient Analytical Querying published pages: , ISSN: , DOI: 10.13140/rg.2.2.30588.18562 |
1 | 2020-01-24 |
2018 |
Djamel Edine Yagoubi, Reza Akbarinia, Boyan Kolev, Oleksandra Levchenko, Florent Masseglia, Patrick Valduriez, Dennis Shasha ParCorr: efficient parallel methods to identify similar time series pairs across sliding windows published pages: 1481-1507, ISSN: 1384-5810, DOI: 10.1007/s10618-018-0580-z |
Data Mining and Knowledge Discovery 32/5 | 2020-01-24 |
2020 |
Djamel-Edine Yagoubi, Reza Akbarinia, Florent Masseglia, Themis Palpanas Massively Distributed Time Series Indexing and Querying published pages: 108-120, ISSN: 1041-4347, DOI: 10.1109/TKDE.2018.2880215 |
IEEE Transactions on Knowledge and Data Engineering 32/1 | 2020-01-24 |
2017 |
Kolev , Boyan; Levchenko , Oleksandra; Masseglia , Florent; Akbarinia , Reza; Pacitti , Esther; Valduriez , Patrick Highly Scalable Real-Time Analytics with CloudDBAppliance published pages: , ISSN: , DOI: |
\"XLDB: Extremely Large Databases Conference, Oct 2017, Clermont-Ferrand, France. 10th Extremely Large Databases Conference, 2017, ⟨https://xldb2017.uca.fr⟩\" 1 | 2020-01-24 |
2019 |
Levchenko, Oleksandra; Kolev, Boyan; Yagoubi, Djamel-Edine; Shasha, Dennis; Palpanas, Themis; Valduriez, Patrick; Akbarinia, Reza; Masseglia, Florent Distributed Algorithms to Find Similar Time Series published pages: , ISSN: , DOI: |
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | 2020-01-24 |
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The information about "CLOUDDBAPPLIANCE" are provided by the European Opendata Portal: CORDIS opendata.