Section: Research Program
Research axis 3: Supporting AI across the digital continuum
Integrating and processing high-frequency data streams from multiple sensors scattered over a large territory in a timely manner requires high-performance computing techniques and equipments. For instance, a machine learning earthquake detection solution has to be designed jointly with experts in distributed computing and cyber-infrastructure to enable real-time alerts. Because of the large number of sensors and their high sampling rate, a traditional centralized approach which transfers all data to a single point may be impractical. Our goal is to investigate innovative solutions for the design of efficient data processing infrastructures for a distributed machine learning-based approach.
In particular, building on our previous results in the area of efficient stream processing systems, we aim to explore approaches for unified data storage, processing and machine-learning based analytics across the whole digital continuum (i.e., for highly distributed applications deployed on hybrid edge/cloud/HPC infrastructures). Our ZettaFlow project is targeting a startup creation precisely this area.