CYBELE generates innovation and creates value in the domain of agri-food, and its verticals in the sub-domains of PA and PLF in specific, as demonstrated by the real-life industrial cases to be supported, empowering capacity building within the industrial and research community. Since agriculture is a high volume business with low operational efficiency, CYBELE aspires at demonstrating how the convergence of HPC, Big Data, Cloud Computing and the IoT can revolutionize farming, reduce scarcity and increase food supply, bringing social, economic, and environmental benefits. CYBELE intends to safeguard that stakeholders have integrated, unmediated access to a vast amount of large scale datasets of diverse types from a variety of sources, and they are capable of generating value and extracting insights, by providing secure and unmediated access to large-scale HPC infrastructures supporting data discovery, processing, combination and visualization services, solving challenges modelled as mathematical algorithms requiring high computing power. CYBELE develops large scale HPC-enabled test beds and delivers a distributed big data management architecture and a data management strategy providing 1) integrated, unmediated access to large scale datasets of diverse types from a multitude of distributed data sources, 2) a data and service driven virtual HPC- enabled environment supporting the execution of multi-parametric agri-food related impact model experiments, optimizing the features of processing large scale datasets and 3) a bouquet of domain specific and generic services
on top of the virtual research environment facilitating the elicitation of knowledge from big agri-food related data, addressing the issue of increasing responsiveness and empowering automation-assisted decision making, empowering the stakeholders to use resources in a more environmentally responsible manner, improve sourcing decisions, and implement circular-economy solutions in the food chain.

Development of data management and analysis algorithms for integration in the CYBELE platform by participating in the implementation of the individual deliverables of WP1, WP2, WP4 & WP8 as well as in the regular meetings in the context of coordinating the necessary activities.

WP 1 – Requirements Analysis, Use Cases & Reference Architecture

Contribution to a) the study of the functional and non-functional requirements of CYBELE architecture, b) the framework specific and the demonstrator specific requirements, and their translation in technical requirements, that eventually leads to the definition and development of the CYBELE Framework and c) the identification of the main stakeholders and the specification of the user cases – usage scenarios within CYBELE, aiming to its immediate development and future establishment as a product.

WP 2 – Infrastructure Implementation

Contribution and support to the development of the individual software packages and different environments for the development of software analytics, as well as to improve performance within underlying high-performance processes in the field of Big Data among the available heterogeneous environments.

WP 4 – Workflow Composition & Services Exposure

The computational implementation of selected process based, and/or data driven AI algorithms designed by or for the support of the scientific experiments to take place in the context of the project demonstrators, which are either not available by the open-source scientific packages and frameworks to be integrated in CYBELE, or need customizations and/or modifications in order to fully meet the end-user requirements

WP 8 – Dissemination, Communication & Clustering

Dissemination of the work progress (the techniques and methodologies followed, the results etc.) in research and scientific publications in international conferences and journals, as well as participation in relevant events, such as workshops and seminars, prospecting in general recognition within the academic community.