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Edge and CLoud Computation: A Highly Distributed Software Architecture for Big Data AnalyticS

Objective

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

Field of science

  • /natural sciences/computer and information sciences/data science/data analysis
  • /engineering and technology/mechanical engineering/vehicle engineering/automotive engineering/autonomous vehicle
  • /natural sciences/computer and information sciences/data science/big data
  • /natural sciences/computer and information sciences/software/software development/software architecture

Call for proposal

H2020-ICT-2017-1
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Funding Scheme

RIA - Research and Innovation action

Coordinator

BARCELONA SUPERCOMPUTING CENTER - CENTRO NACIONAL DE SUPERCOMPUTACION
Address
Calle Jordi Girona 31
08034 Barcelona
Spain
Activity type
Research Organisations
EU contribution
€ 761 625

Participants (5)

UNIVERSITA DEGLI STUDI DI MODENA E REGGIO EMILIA
Italy
EU contribution
€ 505 916,25
Address
Via Universita 4
41121 Modena
Activity type
Higher or Secondary Education Establishments
IBM ISRAEL - SCIENCE AND TECHNOLOGY LTD
Israel
EU contribution
€ 980 690
Address
94 Derech Em-hamoshavot
49527 Petach Tikva
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
ATOS SPAIN SA
Spain
EU contribution
€ 561 041,25
Address
Calle De Albarracin 25
28037 Madrid
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
COMUNE DI MODENA
Italy
EU contribution
€ 572 125
Address
Via Scudari 20
41121 Modena
Activity type
Public bodies (excluding Research Organisations and Secondary or Higher Education Establishments)
MASERATI SPA
Italy
EU contribution
€ 519 405
Address
Via Ciro Menotti 322
41121 Modena
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)