Data management

End-to-end techniques, transformational algorithms to address extreme concurrency, asynchrony or resilience,  advanced analytics algorithms or metadata specification are some of the key elements to name few, that need to be further investigated :

Set up actions to address end-to-end techniques for efficient disruptive I/O and data analysis, to describe the full life-cycle of data for a set of applications in order to produce highly parallel data workflows that are consistent all the way from the production to the analysis of the data while considering locality, structures, metadata, right accesses, quality of service, sharing etc.

Promote research in transformational algorithms to address fundamental challenges in extreme concurrency, asynchronous parallel data movement and access patterns, new alternative execution models, supporting asynchronous irregular applications and resilience, to enhance data analytics and computational methods in big data scientific applications.

Promote research in advanced data analytics algorithms and techniques, adopting new disruptive methodologies, to face the analysis of the big data deluge advancing in different scientific disciplines.

This research should also promote and support the adoption of efficient metadata specification, management and interoperability in different scientific disciplines, as a key element to govern the scientific discovery process.