Description du projet
Intégrer l’analyse des mégadonnées dans les villes intelligentes
Les systèmes actuels d’analyse des mégadonnées se limitent à fournir soit un retour d’information réactif immédiat (données en transit), soit une analyse intensive de grandes quantités de données (données au repos). Ils ne permettent pas de combiner les deux types de données, pourtant complémentaires, pour les traiter en temps réel. Le projet CLASS, financé par l’UE, a élaboré une nouvelle architecture logicielle qui intègre les données en transit et au repos pour permettre le traitement en temps réel de grandes quantités d’informations complexes et leur distribution de la périphérie au nuage. Le cadre a été démontré dans des applications de mobilité pour les villes intelligentes à l’aide de prototypes de véhicules connectés et d’une infrastructure capable de traiter des données en temps réel à partir de sources géographiquement distribuées, d’infrastructures de circulation, de dispositifs de l’Internet des objets, etc.
Objectif
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
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RIA - Research and Innovation actionCoordinateur
08034 Barcelona
Espagne