A Framework for Big Data Sovereignty: The European Industrial Data Space (EIDS)

Mertens, Christoph; Alonso, Jesús Villadangos; Lazaro, Oscar; Palansuriya, Charaka; Böge, Gernot; Nizamis, Alexandros; Rousopoulou, Vaia; Ioannidis, Dimosthenis; Tzovaras, Dimitrios; Touma, Rizkallah; Tarzán, Miquel; Mallada, Diego; Figueiras, Paulo; Costa, Ruben; Graça, Diogo; Garcia, Gisela; Laibarra, Begoña; Celaya, Aitor; Soboński, Piotr; Naeem, Azzam; Mozó, Alberto; Vakaruk, Stanislav; Sierra-García, J. Enrique; Pastor, Antonio; Rodrı́guez, Juan Carlos; Hildebrand, Marlène; Luniewski, Tomasz; Zietak, Wojciech; Lange, Christoph; Σίψας, Κωνσταντίνος; Poulakidas, Athanasios

doi:10.1007/978-3-030-98636-0_10

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…The human-AI-centered AI orchestration framework that is introduced here will be extended to be compatible with data space concepts regarding sovereign data sharing. By coupling the knowlEdge cloud-to-edge continuum with concepts coming from IDSA 34 , FIWARE 35 , GAIA-X 36 , etc., it will be possible to create a human-centered AI orchestration framework that can be adopted by many European industries and factories; this will extend current European manufacturing data space approaches [72] to include AI orchestration and human-centered concepts rather than only data with relevant usage policies and governance rules). Contributing to or leveraging these standards will ensure seamless interoperability with other systems and data spaces within the manufacturing ecosystem.…”

Section: Discussionmentioning

confidence: 99%

AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

Alberti,

Alvarez-Napagao,

Anaya

et al. 2024

Systems

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The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems.

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Section: Discussionmentioning

confidence: 99%

AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

Alberti,

Alvarez-Napagao,

Anaya

et al. 2024

Systems

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“…IDS-RAM defines the required standards, control, and enforcement rules for data exchange among different participants in a data space, specifying their components and mechanisms [22]. In Figure 2, the most important modules and components of the IDS reference architecture model are depicted [23]. As described above, the IDS reference architecture aims at defining the necessary components needed for establishing data spaces.…”

Section: Ids Architecturementioning

confidence: 99%

“…Examples of resources include documents, time series, messages, images, and media streams. Figure 3 illustrates a concern hexagon (C-hexagon), meaning a separation of concerns (SoC) listed as dimensions where each dimension can be considered independently [23].…”

Section: Ids Information Modelmentioning

confidence: 99%

Exploring the Role of Federated Data Spaces in Implementing Twin Transition within Manufacturing Ecosystems

Jurmu

Niskanen

Kinnula

et al. 2023

Sensors

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Globally, manufacturing ecosystems are facing the challenge of twin transition, i.e., how to utilize digitalization for improving or transforming the sustainability of manufacturing operations. Here, operations refer widely to the upstream of manufacturing, while the entire product lifecycle also covers the downstream and end-of-life operations. Here, sustainability is understood to consider the impact of the product lifecycle at environmental, social, and governance (ESG) levels. In this article, we explore this progress through the digitalization concept of business-to-business data sharing, and through one example of a manufacturing ecosystem in Finland. We discuss the federated data space concept and the international data spaces (IDS) architecture as technological building blocks of twin transition, and report the first results from an industry−research shared-risk project. Semi-structured interviews and a diary-style reporting from an industry−research IDS proof-of-concept (PoC) experiment are presented and analyzed within a design science research method framework. The findings give the first indications that while data sharing is seen as important and increasing in relevance in industry, it is currently challenging for companies to see how an open standard architecture creates value beyond a single limited ecosystem view. We also highlight possible avenues for further research.

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Designing a Marketplace to Exchange AI Models for Industry 5.0

Nizamis,

Schlake,

Siachamis

et al. 2023

Artificial Intelligence in Manufacturing

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Nowadays, the market for AI services is continuously growing and it is expected to exceed 5 trillion euros in the next 5 years. However, the sharing of knowledge is primarily achieved by the sharing of published AI-related papers. The sharing of the trained AI/ML models is still in its infancy stage and in some domains it does not even exist. In this chapter, a marketplace for exchanging AI models related to smart manufacturing and Industry 5.0 domains is introduced. The proposed AI Marketplace consists of a semantic-based repository that manages the AI models, a blockchain-based framework that adds the business logic and web-based user interfaces that enable models’ exploration and sharing, and transactions among the stakeholders. The purpose of this chapter is to present the implementation details of this AI Model Marketplace by highlighting the key concepts and technologies used along with the main supported functionalities. By using such a marketplace, the manufacturing companies are able to capitalize in a large variety of AI models to solve various problems enabling intelligent, flexible, and cost-effective production.

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A Framework for Big Data Sovereignty: The European Industrial Data Space (EIDS)

Cited by 3 publications

References 5 publications

AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

Exploring the Role of Federated Data Spaces in Implementing Twin Transition within Manufacturing Ecosystems

Designing a Marketplace to Exchange AI Models for Industry 5.0

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