Descripción del proyecto
Integración del análisis de datos masivos en las ciudades inteligentes
Los sistemas actuales de análisis de grandes volúmenes de datos se limitan a proporcionar información reactiva de forma inmediata (lo que se conoce como datos en movimiento) o a ofrecer un análisis intensivo de ingentes cantidades de datos (datos en reposo). Esto impide combinar los dos tipos de datos con el fin de procesarlos al instante. El equipo del proyecto CLASS, financiado con fondos europeos, ha desarrollado una novedosa arquitectura de «software» capaz de integrar tanto los datos en movimiento como los datos en reposo, lo que permite el procesamiento al instante de enormes cantidades de datos complejos y su distribución desde la periferia hasta la nube. La utilidad de este enfoque con vistas a desarrollar aplicaciones de movilidad para ciudades inteligentes se ha demostrado empleando prototipos de vehículos conectados y una infraestructura capaz de procesar datos al instante procedentes de diversas fuentes distribuidas geográficamente, infraestructuras de tráfico, dispositivos del internet de las cosas, etc.
Objetivo
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
Ámbito científico
Palabras clave
Programa(s)
Tema(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-ICT-2017-1
Régimen de financiación
RIA - Research and Innovation actionCoordinador
08034 Barcelona
España