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.

Collaboration.

This recently started axis is worked out in close collaboration with the group of Manish Parashar, Rutgers University, and with the LACODAM team at Inria, focused on large-scale collaborative data mining.