Projektbeschreibung
Integration der Analyse von Big Data für intelligente Städte
Gegenwärtige Analysesysteme für Big Data beschränken sich darauf, entweder ein sofortiges reaktives Feedback (Daten auf dem Übermittlungsweg) oder eine intensive Analyse riesiger Datenmengen (gespeicherte Daten) zu liefern. Dies verhindert die Kombination dieser beiden Arten komplementärer Daten zur Verarbeitung in Echtzeit. Im Rahmen des EU-finanzierten Projekts CLASS wurde eine neuartige Softwarearchitektur entwickelt, die Daten auf dem Übermittlungsweg und gespeicherte Daten integriert, um die Echtzeitverarbeitung großer Mengen komplexer Daten und die Verteilung von Edge zur Cloud zu ermöglichen. Der Rahmen wurde in Mobilitätsanwendungen für intelligente Städte unter Verwendung von Prototypen vernetzter Fahrzeuge und einer Infrastruktur demonstriert. Diese ist in der Lage, Daten in Echtzeit aus geografisch verteilten Quellen, Verkehrsinfrastrukturen, Geräten des Internets der Dinge usw. zu verarbeiten.
Ziel
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
Wissenschaftliches Gebiet
Schlüsselbegriffe
Programm/Programme
Thema/Themen
Aufforderung zur Vorschlagseinreichung
Andere Projekte für diesen Aufruf anzeigenUnterauftrag
H2020-ICT-2017-1
Finanzierungsplan
RIA - Research and Innovation actionKoordinator
08034 Barcelona
Spanien