The EC communication “Digitising European Industry: Reaping the full benefits of a Digital Single Market” aims to reinforce the EU's competitiveness in digital technologies and to ensure that every industry in Europe, in whichever sector, wherever situated, and no matter of what size can fully benefit from digital innovations. In the H2020 LEIT ICT WP2018-2020, the section “Platforms and Pilots” is launching the six calls (DT-ICT-07 to DT-ICT-12) for implementing next generation Digital Platforms in four basic industrial domains: Manufacturing, Agriculture (and Rural Areas), Energy (and Smart Homes / Grids) and Healthcare (Smart Hospitals). Moreover, outside of the specific suggested Digital Transformation (DT) topics, the development of EU Digital Industrial Platforms (a battle Europe cannot afford to lose) is a key priority also e.g. in Smart Systems (Smart Anything Everywhere SAE), Robotics, Photonics and Process Industry (SPIRE2030) domains. The OPEN DEI proposal aims at leveraging synergies, identifying gaps, sharing best practices, reinforcing regional/national relationships as well as putting in place the necessary joint measures to implement common dissemination, communication, training and exploitation action plans among the Innovation Actions implementing the EU DT strategy. OPEN DEI is structured around twelve coordinated and complementary objectives grouped in four main action lines: in Platform Building a reference architecture, a set of open source reference implementations and an Industrial Data Platform; in Large Scale Piloting a digital maturity model, a set of assessment methods and a migration journey benchmarking tool; in Ecosystem Building an Innovation and Collaboration Platform, a pan-EU network of Industrial DIHs, an Industrial Skills catalogue and observatory; in Standardisation a cross-domain survey, a promotion and implementation plan and a strategic alliance with existing EU and International SDOs.

UNLOCK CEI’s ambition is to UNLOCK the potential for accelerating the deployment of the Cloud-to-Edge-IoT (CEI) computing continuum in Europe by focusing on the demand-side drivers and challenges to identify technology-driven innovation and business opportunities driving demand value chains. The project represents the CEI demand constituency; provide insights and guidance to the Horizon Europe R&D projects in this domain; and contribute to a proactive dialogue with the supply stakeholders to encourage the development of an open European CEI ecosystem. It focuses on emerging value chains where investment is needed to foster the deployment of the Cloud-to-Edge-IoT continuum through the forthcoming CEI large scale pilots. This will ultimately foster European autonomy in the digital economy. The workplan is designed to provide early results to feed into the development of the HE Work Programme in 2023-24 based on the ongoing dialogue with the stakeholder ecosystem. The project pays specific attention to data governance issues, leveraging the relationship with industry-driven initiatives such as Gaia-X and CCAM (Connected, Co-operative & Automated Mobility) partnership. UNLOCK carries out a systematic assessment of the state of the open European CEI ecosystem and develop future market scenarios and related market pathways for the development of the open European CEI ecosystem. It will engage with industry stakeholders representing the most relevant industry value chains in Europe for CEI potential demand and develops a productive and effective interface between the demand constituency and the supply side, including the coordination of HE projects resulting from CL4-2021-DATA-01-05, CL4-2022-DATA-01-02, and CL4-2022-DATA-01-03 and Cluster 3 cybersecurity projects. It finally designs and implement a coordinated communication and dissemination strategy running throughout the lifetime of the project to create awareness about the whole of the European demand-side landscape.

EUHubs4Data will set up a European federation of Big Data Digital Innovation Hubs (DIHs), with the ambition of becoming a reference instrument for data-driven cross-border experimentation and innovation, and support the growth of European SMEs and start-ups in a global Data Economy. Based on the concept “European catalogue, local offer”, EUHubs4Data will establish a Europe-wide, sustainable ecosystem drawing upon local expertise and achievements of European initiatives and national/regional Big Data DIHs, with the three-fold objective of: (1) creating a European catalogue of data sources and federated data-driven services and solutions; (2) making this offer accessible at the regional level so that European SMEs, start-ups and web entrepreneurs have access to the most valuable assets and expertise on the continent; (3) fostering cross-border and cross-sector data-driven experimentation facilitated through data sharing, and data & service interoperability. To achieve these objectives, the project relies on: • a strong initial ecosystem of 12 Big Data DIHs from the 4 EU poles, linked to European Data Incubators and SME networks; • a multi-dimensional approach (governance, operations, technical, ethical and legal aspects, interoperability, skills) for the expansion of the federation (growing up to 30 DIHs); • a strong offer of services and access to data sources, based on an initial set of solutions for data and service interoperability, and including a coherent and diverse training programme; • the capacity to attract, support and engage SMEs, start-ups and web entrepreneurs (40 cross-border experiments, and 60-80 companies directly involved in the data-driven innovation programme); • a community and ongoing collaborations with 60 relevant European Initiatives; EUHubs4Data initially covers 12 EU regions in 9 countries, and plans to expand to more than 20 regions and 14 countries during the project, establishing a long-lasting sustainable ecosystem

Procedural Knowledge (PK) is knowing-how to perform some tasks. For industry workers, PK is the knowledge required to carry out a specific job, like correctly executing the safety procedure during maintenance interventions, or configuring an industrial system by following the necessary steps in the right order, or adopting practices and behaviours to optimise energy consumption in a plant. The challenge in PK management is that this kind of knowledge may be hard to explain and describe, oftentimes it is poorly digitalised, and, even when documented, it may be still difficult to access and retrieve by industry operators. The PERKS project supports the holistic governance of industrial PK in its entire life cycle, from elicitation to management and from access to exploitation. PERKS bases its solutions on AI (both symbolic and subsymbolic) and data technologies, by advancing and integrating existing methodologies and tools in terms of readiness, flexibility and user acceptance. The results are applied and tested in three industrial scenarios (white goods production plant, computer numerical control machines, microgrid testbed) providing different use cases in terms of PK complexity and industrial requirements. Besides AI and data, the third pillar of PERKS is people: the goal is to satisfy the concrete needs of industry workers, providing them with digital support to better and easier perform their tasks following a human-in-the-loop paradigm, and putting them at the centre according to Industry 5.0 approaches. The outcomes of PERKS are: a reference architecture for PK management; a set of modular, interoperable and complementary digital tools to be composed and customised to industrial requirements; specific integrated solutions to solve the challenges of the project use cases; a set of methodologies and best practices for broader application in other industrial settings, paving the way for a wider transferability across contexts and sectors.

Organisations rely on evidence from the Benchmarking domain to provide answers to how their processes are performing. There is extensive information on how and why to perform technical benchmarks for the specific management and analytics processes, but there is a lack of objective, evidence-based methods to measure the correlation between Big Data Technology (BDT) benchmarks and an organisation’s business benchmarks and demonstrate return on investment. When more than one benchmarking tool exists for a given need, there is even less evidence as to how these tools compare to each other, and how the results can affect their business objectives. At the heart of the DataBench project is the goal to design a benchmarking process helping European organizations developing BDT to reach for excellence and constantly improve their performance, by measuring their technology development activity against parameters of high business relevance. DataBench will investigate existing Big Data benchmarking tools and projects, identify the main gaps, provide a robust set of metrics to compare technical results coming from those tools. It will provide a framework to associate those technical results with the economic processes that are imperative to a company. It will provide a robust set of benchmarks to assess which tools respond best and provide the most pertinent information for organisation’s economic planning and respond to their current and emerging industrial needs. It will provide a software tool which the industrial and research community users can leverage to do this evaluation. DataBench will interact with ICT14 and 15 projects to give access to this tool and framework to leverage the Big Data benchmarking investment so far carried out in the benchmarking community, contributing to the success of the BDV-PPP. The project envisions continuous interaction with the leading BDT suppliers and industrial user communities, and has a strong relationship with the BDV cPPP.