Explore the words cloud of the E2DATA project. It provides you a very rough idea of what is the project "E2DATA" about.
The following table provides information about the project.
EXUS SOFTWARE LTD
|Coordinator Country||United Kingdom [UK]|
|Total cost||4˙676˙250 €|
|EC max contribution||4˙676˙250 € (100%)|
1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
|Duration (year-month-day)||from 2018-01-01 to 2020-12-31|
Take a look of project's partnership.
|1||EXUS SOFTWARE LTD||UK (LONDON)||coordinator||503˙125.00|
|2||DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBH||DE (KAISERSLAUTERN)||participant||720˙750.00|
|3||THE UNIVERSITY OF MANCHESTER||UK (MANCHESTER)||participant||658˙750.00|
|4||BAMBOO SYSTEMS GROUP LIMITED||UK (CAMBRIDGE)||participant||610˙750.00|
|5||INSTITUTE OF COMMUNICATION AND COMPUTER SYSTEMS||EL (ATHINA)||participant||590˙750.00|
|6||NEUROCOM LUXEMBOURG SA||LU (LUXEMBOURG)||participant||505˙625.00|
|7||IPROOV LIMITED||UK (LONDON)||participant||483˙625.00|
|8||SPARK WORKS ITC||UK (SWADLINCOTE DERBYSHIRE)||participant||315˙375.00|
|9||INSTITOUTO TECHNOLOGIAS YPOLOGISTONKAI EKDOSEON DIOFANTOS||EL (PATRAS)||participant||287˙500.00|
Imagine a Big Data application with the following characteristics: (i) it has to process large amounts of complex streaming data, (ii) the application logic that processes the incoming data must execute and complete within a strict time limit, and (iii) there is a limited budget for infrastructure resources.
In today’s world, the data would be streamed from the local network or edge devices to a cloud provider which is rented by a customer to perform the data execution. The Big Data software stack, in an application and hardware agnostic manner, will split the execution stream into multiple tasks and send them for processing on the nodes the customer has paid for. If the outcome does not match the strict three second business requirement, then the customer has two options: 1) scale-up (by upgrading processors at node level), 2) scale-out (by adding nodes to their clusters), or 3) manually implement code optimizations specific to the underlying hardware.
E2Data proposes an end-to-end solution for Big Data deployments that will fully exploit and advance the state-of-the-art in infrastructure services by delivering a performance increase of up to 10x while utilizing up to 50% less cloud resources. E2Data will provide a new Big Data paradigm, by combining state-of-the-art software components, in order to achieve maximum resource utilization for heterogeneous cloud deployments without affecting current programming norms (i.e. no code changes in the original source). The E2Data innovations will be driven by the requirements of four resource demanding applications from the finance, health, green buildings, and security domains. Finally, the evaluation will be conducted on both high-performing x86 and low-power ARM cluster architectures representing realistic execution scenarios of real-world deployments.
|Prototype V1 and preliminary evaluation||Documents, reports||2020-03-04 09:44:22|
|Project Presentation and web portal||Other||2020-03-04 09:44:22|
|The Intelligent Scheduler Definition||Documents, reports||2020-03-04 09:44:22|
|Quality plan||Documents, reports||2020-03-04 09:44:22|
|Intermediate API definition||Documents, reports||2020-03-04 09:44:22|
|E2Data architecture||Documents, reports||2020-03-04 09:44:22|
|Dissemination and exploitation activities report 1||Documents, reports||2020-03-04 09:44:22|
|User requirements and data management plan||Documents, reports||2020-03-04 09:44:22|
Take a look to the deliverables list in detail: detailed list of E2DATA deliverables.
|year||authors and title||journal||last update|
Traub, Jonas; Grulich, Philipp; CueÌllar, Alejandro RodriÌguez; BreÃŸ, Sebastian; Katsifodimos, Asterios; Rabl, Tilmann; Markl, Volker
Efficient window aggregation with general stream slicing
published pages: , ISSN: , DOI: 10.5441/002/edbt.2019.10
|22nd International Conference on Extending Database Technology 2019 March||2020-03-04|
BreÃŸ, S., Funke, H., Zeuch, S., Rabl, T. & Markl, V.
An Overview of Hawk: A Hardware-Tailored Code Generator for the Heterogeneous Many Core Age
published pages: , ISSN: , DOI: 10.18420/btw2019-ws-07
|BTW 2019 â€“ Workshopband.||2020-03-04|
Clemens Lutz, Sebastian BreÃŸ, Tilmann Rabl, Steffen Zeuch, Volker Markl
Efficient and Scalable kâ€‘Means on GPUs
published pages: 157-169, ISSN: 1618-2162, DOI: 10.1007/s13222-018-0293-x
Sebastian BreÃŸ, Bastian KÃ¶cher, Henning Funke, Steffen Zeuch, Tilmann Rabl, Volker Markl
Generating custom code for efficient query execution on heterogeneous processors
published pages: 797-822, ISSN: 1066-8888, DOI: 10.1007/s00778-018-0512-y
|The VLDB Journal 27/6||2020-03-04|
A Methodology for Saving Energy in Educational Buildings Using an IoT Infrastructure
published pages: , ISSN: , DOI:
|10th International Conference on Information, Intelligence, Systems and Applications (IISA 2019)||2020-03-04|
Steffen Zeuch, Bonaventura Del Monte, Jeyhun Karimov, Clemens Lutz, Manuel Renz, Jonas Traub, Sebastian BreÃŸ, Tilmann Rabl, Volker Markl
Analyzing efficient stream processing on modern hardware
published pages: 516-530, ISSN: 2150-8097, DOI: 10.14778/3303753.3303758
|Proceedings of the VLDB Endowment 12/5||2020-03-04|
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The information about "E2DATA" are provided by the European Opendata Portal: CORDIS opendata.