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DEEP Hybrid DataCloud accepted paper in 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2018), Sep 3-5, 2018, Belgrade, Serbia

We are proud to announce that a research paper developed under the Deep Hybrid DataCloud Project has been accepted for inclusion in 22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2018) to be held on 3-5 September 2018, in Belgrade (Serbia). This paper will be published by Elsevier Science in the open-access […]

Digital Infrastructures for Research 2018 (DI4R) and IBERGRID 2018

The third edition of the annual Digital Infrastructures for Research (DI4R) conference was hold this year at the University Institute of Lisbon (ISCT-IUL), from October 9th to 11th. Co-located with the event, IBERGRID 2018, the 9th Iberian Grid and Cloud conference with the objective to move towards the European Open Science Cloud. During the first […]

Report on the workshop: New challenges in Data Science: Big Data and Deep Learning on Data Clouds in Santander (Spain)

The Spanish National Research Council (CSIC) as coordinator of the DEEP-Hybrid-DataCloud project has organized together with the eXtreme-DataCloud project the “New challenges in Data Science” workshop, in the context of the Advanced Summer Courses offered by the prestigious Universidad Internacional Menendez Pelayo (UIMP). This workshop took place from June 18th to 22nd in Santander, at […]

D6.2 – Design for the DEEP as a Service solution

This document provides a description of the design, architecture and work plan the DEEPHybridDataCloud Work Package 6 (WP6) in order to provide the DEEP as a Service solution. As such it provides an overview of the state of the art of the relevant components and technologies, as well as a technology readiness level assessment with […]

D5.1 – Definition of the Architecture of the Hybrid Cloud

This document describes the design of the PaaS Layer and the detailed work plan for the WP5 of the DEEP-HybridDataCloud project, which aims at adopting and extending the PaaS orchestration solution for cloud infrastructures realised within the INDIGO-DataCloud project. In particular, the main focus is on the architecture and features of the hybrid cloud solution. […]

D4.1 – Assessment of available technologies for supporting accelerators and HPC, initial design and implementation plan

This document describes the state of the art of technologies for supporting bare-metal, accelerators and HPC in cloud and proposes an initial implementation plan. Available technologies will be analyzed from different points of views: stand-alone use, integration with cloud middleware, support for accelerators and HPC platforms. Based on results of these analyses, an initial implementation […]

D2.1 – Initial Plan for Use Cases

This report summarises the work of WP2 on the initial plan on the selection of Use Cases, providing a key input to the design of the DEEP-HybridDataCloud testbed and laying out the DEEP-HybridDataCloud solutions will be used. This document includes a description of the Research Communities involved and of the use-cases proposed, including Data Management […]

D1.11 – Data Management considerations and initial plan

This report summarises the work of WP1 on the Data Management considerations and initial plan for the DEEP-HybridDataCloud project. This document describes the types of data that will be generated or collected during the project, the standards that will be used and the ways in which the data may be exploited and shared including the […]

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New challenges in Data Science: Big Data and Deep Learning on Data Clouds in Santander (Spain) on 18-22 June 2018

The DEEP Hybrid DataCloud consortium is proud to announce the upcoming seminar which will be directed by Dr. Jesús Marco de Lucas and assisted by Dr. Álvaro López, both Project Coordinators of the DEEP Hybrid DataCloud project. This seminar will be hosted by UIMP within its Summer Advanced Courses Program and organized in collaboration with […]

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777435.

Unless otherwise indicated, all materials created by the DEEP-Hybrid-DataCloud consortium are licensed under a Creative Commons Attribution 4.0 International License.

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