The solutions provided by PROCESS enable to utilization of the world’s most powerful supercomputers for data-intensive tasks. This exceeds many other solutions in that domain and therefore advancing the state-of-the-art.
The invocation protocols used by services today are not suitable for transferring significant volumes of data as they mix the invocation and actual data transfer. New data delivery models need to be researched where the invocation protocol is separated from data movement with the aim to reduce the execution time of workflows, especially in the case of streaming applications. The problem will become more challenging assuming data is distributed across RIs, loosely coupled, and stored in a variety of storage resources ranging from a simple file system to heterogeneous cloud storage.
PROCESS outputs areshowcased through five use cases: exascale learning on medical image data, the LOFAR e-infrastructure, Airline revenues management and validating long-term agricultural modeling and simulation.
PROCESS positive impacts are based on three principles: Leapfrog beyond the current state-of-the-art, ensuring broad research and innovation impact and Supporting the long tail of science and broader innovation. In practical terms, PROCESS outputs allow more intuitive and easier to use exascale data services for broader communities, fostering wider uptake and seeking to expand European e-infrastructure user bases, to secure stronger impact and sustainability.
The use cases enhanced not only the developed services, but contributed to their community and the overall challenges:
The diagnostic support tool developed in the medical use case has led to advantages in manipulation and interpretability of medical imaging, with better diagnostic models thanks to the evaluation and interpretation of results from different depth learning techniques.
The PROCESS project has provided a solution for executing containerised workflows. Furthermore, the required large data transfers are fully automated, allowing the LOFAR astronomers to focus on the data processing and the results. PROCESS has added the LOFAR easy-to-use web portal for selecting observation data sets and processing pipelines. By integrating it, the user-friendliness and portability of such data processing pipelines has been significantly improved. The developments of the use case specific code were also useful for the community and will probably serve as a basis for follow-up projects.
The revenue simulation of the airline use case clearly shows that the developed algorithms promise a revenue increase. The results achieved have the potential to be translated into production for more precise offers regarding a greater value for customers and more income for the company.
The set of SME-oriented solutions allows for easier agricultural analysis based on Copernicus data sets while protecting the assets of SMEs.
