Current big-data systems are designed for either fast and reactive responses (data-in-motion), or computationally intensive analysis of a vast amount of data (data-at-rest). Instead of considering these options separately, the vision of CLASS is to provide an environment where data-in-motion and data-at-rest analytics can be combined into a single workflow, that can be efficiently distributed across the compute continuum, from the data sources and collocated edge devices, to the data servers at the cloud.
The CLASS SA environment, validated in a smart city use case, has achieved:
-Integration and optimization of advanced data analytics methods into a single workflow for collision detection and air pollution estimation, using both task-based and map-reduce analytics engines
-Up to 50% reduction in SW development costs, bringing down the development time for the smart city use case from 2 months to 3 weeks
-Up to 40% reduction of the analytics response time, through advanced scheduling for distributed execution, taking into account data dependencies, the quality of communication links and real-time requirements
-Support for concurrency at the cloud, through the execution of data analytics methods as serverless functions, as well as scaling capabilities through a predictive SLA management component
At a societal level, the solutions provided in CLASS can potentially:
-Reduce the number of accidents and provide a safer urban environment, by warning drivers for potential collisions, with up to 2 second margin
-Enable the estimation of vehicle-related air pollution levels in very small time scales (down to few minutes) based on real-time traffic observations, a feature not possible by current long-term statistical models, which enables the study of the impact of real-life traffic behaviour on the air quality, eventually leading to the identification of greener driving habits
-Enhance traffic management by incorporating smart vehicles able to respond in real-time to specific situations. For instance, through an enriched simulation framework developed in CLASS, a potential reduction of up to 36% of the response of an ambulance traveling within the MASA can be achieved by the use of smart vehicles
Overall, the CLASS ecosystem offers a smart, safe and sustainable transportation solution with faster, flexible and scalable software development and deployment capabilities, which can also be applied to a wide range of application domains with critical real-time requirements, such as smart factories, smart healthcare, etc.
