Session 4:Big Data infrastructure and analytics in ocean science

Conveners

Stan Matwin, Dalhousie University, Halifax, Canada

Ronan Fablet, Telecom Bretagne, Brest, France

Scope

The extraction of knowledge on ocean’s dynamics and use are hot topics, for which new technological and methodological challenges recently emerged. Database have grown bigger and bigger (from the giga to the peta-scale) with rapid updating frequencies (from the year/month to the day/hour scale), and involve complex structures and higher dimensionality (from one file/parameter to millions of files and dozens of parameters on multiple locations). To the oceanographic community it has become challenging to manage, explore and extract knowledge out of such databases.

“Big Data”, “Data Mining”, “Database Knowledge Discovery” or “Artificial Intelligence” are the names given to the research discipline dedicated to face such challenges. The session aimed at fostering interactions between the “Data Mining” and “Oceanographic” research communities and for the latter to broaden its view on available analysis tools.

Potential, non-exclusive topics of interest include:

-          Big data platform for ocean-related data

-          Exploitation of large-scale ocean-related datasets (satellite-born remote sensing data, in situ data, numerical simulations,...)

-          Data-driven and learning-based strategies for ocean science

-          Data analytics applied to ocean-relate datasets