Brief description: 

Current trends towards the use of big data technologies in the context of smart cities suggest the need for developing novel software development ecosystems upon which advanced mobility functionalities can be developed. CLASS is creating a novel software architecture that allows users to develop and execute advanced big-data applications in an efficient way. The goal of this new software infrastructure is to allow collecting, storing, analysing and processing a vast amount of geographically-distributed data, in order to transform it into valuable knowledge for the public sector, private companies and citizens.

CLASS aims to develop a novel software architecture framework to help big data developers to efficiently distributing data analytics workloads along the compute continuum (from edge to cloud) in a complete and transparent way while providing sound real-time guarantees. This ability opens the door to the use of big data into critical real-time systems, providing them with superior data analytics capabilities to implement more intelligent and autonomous control applications.

Applying big-data technologies to smart cities field applications entails many challenges: from processing data across the compute continuum (from edge to cloud), to predicting real-time responses, and employing a programming model that can mix different application program interfaces (APIs) and models. The CLASS platform is facing these needs by integrating technologies from different computing domains into a single development framework. This allows to efficiently combine data-in-motion and data-at-rest analytics along the compute continuum, while providing real time guarantees.

The capabilities of the CLASS framework will be demonstrated on a real smart-city use case in the City of Modena, featuring a heavy sensor infrastructure to collect real-time data across a wide urban area, and three connected vehicles equipped with heterogeneous sensors/actuators and V2X connectivity to enhance the driving experience.

Main Features: 
  • Innovative parallel and distributed programming models and architectures from the high-performance domain
  • Timing analysis methods and energy-efficient parallel architectures from the real-time embedded domain.
  • Advanced data analytics platforms and programming models from the big-data domain
Areas of Application: 

Smart Cities, Connected cars, Urban mobility. In addition to those areas of application, CLASS architecture can be applied to all domains with critical real-time needs.

Market Trends and Opportunities: 

CLASS architecture is a solution for Smart Cities and Automotive Smart Areas (ASA), which provides the ASA city-awareness environment needed to support the huge performance requirements of the real-time big data analytics methods needed.

Customer Benefits: 

CLASS aims to develop a novel software architecture to help programmers and big data practitioners to combine data-in-motion and data-at-rest analysis, by efficiently distributing data and process mining along the compute continuum, while providing real-time guarantees

Technological novelty: 
  • A software architecture ecosystem for distributing big-data workloads along the compute continuum while providing real-time guarantees
  • Development of data analytics workloads combining data-in-motion and data-at-rest that benefits from distribution capabilities
  • A novel distributed sensing/computing infrastructure for the development of advanced urban mobility applications with data analytics and real-time requirements
Workflow: 
Published
Platform / Framework
CLASS Software Architecture Ecosystem
The compute continuum addressed by the CLASS architecture

Owner

CLASS Consortium
Barcelona
Spain
Type: 
Project
BDV Reference categories: 
Data Analytics
Data processing architectures
Markets: 
Urban Mobility
Readiness Level: 
Keywords: 
distributed computing
real time data analytics
smart cities
Cloud computing
data analytics platforms