Explore the words cloud of the CLASS project. It provides you a very rough idea of what is the project "CLASS" about.
The following table provides information about the project.
BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION
|Coordinator Country||Spain [ES]|
|Total cost||3˙900˙802 €|
|EC max contribution||3˙900˙802 € (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||BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION||ES (BARCELONA)||coordinator||747˙875.00|
|2||IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD||IL (PETACH TIKVA)||participant||973˙190.00|
|3||MASERATI SPA||IT (MODENA)||participant||575˙655.00|
|4||COMUNE DI MODENA||IT (MODENA)||participant||558˙375.00|
|5||ATOS SPAIN SA||ES (MADRID)||participant||553˙541.00|
|6||UNIVERSITA DEGLI STUDI DI MODENA E REGGIO EMILIA||IT (MODENA)||participant||492˙166.00|
Big data applications processing extreme amounts of complex data are nowadays being integrated with even more challenging requirements such as the need of continuously processing vast amount of information in real-time. Current data analytics systems are usually designed following two conflicting priorities to provide (i) a quick and reactive response (referred to as data-in-motion analysis), possibly in real-time based on continuous data flows; or (ii) a thorough and more computationally intensive feedback (referred to as data-at-rest analysis), which typically implies aggregating more information into larger models. Given the apparently incompatible requirements, these approaches have been tackled separately although they provide complementary capabilities. CLASS aims to develop a novel software architecture to help big data developers to combine data-in-motion and data-at-rest analysis by efficiently distributing data and process mining along the compute continuum (from edge to cloud) in a complete and transparent way, while providing sound real-time guarantees. CLASS aims at adopting (1) innovative distributed architectures from the high-performance domain; (2) timing analysis methods and energy-efficient parallel architectures from the embedded domain; and (3) data analytics platforms and programming models from the big-data domain. The capabilities of the CLASS framework will be demonstrated on a real smart-city use case, featuring a heavy sensor infrastructure to collect real-time data across a wide urban area, and prototype cars equipped with heterogeneous sensors/actuators, V2I connectivity, and cluster support to present the innovative capabilities to drivers. Representative applications for traffic management and advanced driving assistance domains have been selected to efficiently process very large heterogeneous data streams in real-time, providing innovative services while preparing the technological background for the advent of autonomous vehicles
|Communication and Dissemination Plan||Documents, reports||2019-11-26 15:02:54|
|First release of the real-time analysis methods and tools on the edge||Demonstrators, pilots, prototypes||2019-11-26 15:02:53|
|Data Management Plan (DMP)||Open Research Data Pilot||2019-11-26 15:02:56|
|First release of the Cloud Data Analytics Service Scalability components||Demonstrators, pilots, prototypes||2019-11-26 15:02:54|
|Advanced multi-workload/multi-tenant performance evaluation tool for big data services||Demonstrators, pilots, prototypes||2019-11-26 15:02:53|
|Initial communication and dissemination report||Documents, reports||2019-11-26 15:02:55|
Take a look to the deliverables list in detail: detailed list of CLASS deliverables.
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The information about "CLASS" are provided by the European Opendata Portal: CORDIS opendata.
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