Beyond data sharing:
Realizing Value
through Applications
in Data Spaces
12/03/2024 14:00–15:30 (CET)
Daniel Alonso, Nuria De Lama, Thomas Hahn, ArturoMedela, Emmanouil G. Spanakis, Ed Curry, Tanguy
Coenen, Hanno Focken, Rob Smeets, Aitor Corchero,
Denia Kanellopoulou, Mihnea Tufis

Objectives of the session• To discuss about different current and potential ways
to generate value from data spaces (with data sharingand collaboration among stakeholders as main drivers)• To identify technical enablers needed to materialize
this value generation• To hear from industry different sectors real use casesof value generation from data and data sharing, andhow data spaces are contributing to them.

• To focus on AI / industrial AI as a paradigmatic example
that can benefit from its connection with data spaces

Agenda of the session
SpeakerIntroduction and setting-up the scene
Daniel Alonso (BDVA), Nuria De Lama (IDC)

Intro and framework

Thomas Hahan (Siemens, BDVA)

Motivational picth

Part 1 - View from research and innovation (initial statement + panel discussion) -> moderator Daniel
Arturo Medela

SEDIMARK Project

Emmanouil G. Spanakis

Trustee Project

Ed Curry

Generative AI and data spaces

Tanguy Coenen

Algorithm Centric Automated Data Integration

Minhea Tufis

PISTIS project

Part 2 – View from industry (initial statement + panel discussion) -> moderator Nuria
Hanno Focken

Manufacturing sector

Rob Smeets

Healthcare domain

Aitor Corchero

Public administration

Denia Kanellopoulou

Industrial AI

BDVA data value generation framework
Business

Requirements, businessmodels, etc …

For specificsectors, basedon businessneeds andmodels, using
applications
that rely on theuse of data and/ or data
sharing
through dataspaces

Sectors

Usecases

Conformity assessment

Data
drivenvalue
generation

Healhcare, mobility, manufacturing, agrifood, energy, finance, media, telecom, SSCC,security, earth observation

Data space

AI tools

Data protection
Data quality

Data
analytics

Data
processingDatavisualization

Data
Exchange

Description,
publicationand discovery

Datageneration

Dataacquisition

Data
cleaning

Data
models andformats

Access andusage
controlSensing andperception

Action andinteraction

Knowledge
and learning

Reasoning& decision
makingLLM

NLP

Data Spaces Support CenterThomas Hahn
Siemens AG / BDVA PresidentSource Paul Clarke - Eigenes Werk, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=37435469

March 1989

Why Data Transparency is needed?
Example: Carbon Footprint• ~10% of CO2 emissions from industry generated by own factories, ~90%
by upstream/downstream supply chain 1)• To reduce CO2 emissions, transparency along the entire value chain isnecessary1) depending on production

Example: Battery production• Only if we use the potential of digitalization we can produce
batteries more sustainably• ~ 96% of the ingredients of a battery are recyclable• Consistent and transparent use of digital twins along thecomplete cycle the design and production will be moresustainableManufacturing-X: Make Data Work
Goal
Manufacturing-X is a German initiative to digitalize the entire
manufacturing and supply chains in industry. It aims at implementing thedata space for Industrie 4.0 across industries on a global scale. The
goal is to enable digital innovations for greater resilience, sustainability
and competitiveness.

Approach
Companies jointly work together on use cases and shared services
to enable data exchange across companies and products of different
vendors. Manufacturing-X comprises Catena-X for the automotive
industry and a proposed project proposal called Factory-X for Machine
Builders and factory operators. It builds on common standards such as
the Asset Administration Shell.

Customer value• Fast adaptation of existing standards and technologies• Creates customer value for concrete use cases• Fast adaptation of innovative ideas and business models• Opportunities for SMEs to gain access to technology and infrastructure
as basis to provide their digital offerings​

How industrial Data Ecosystems WORK?!

Env i ro n m e n transfer Servicesm m o B a s isnTCo

Arturo Medela
Eviden / SEDIMARK Project coordinator

What is SEDIMARK?
• SEDIMARK aims at designing and prototyping a secure

decentralised and intelligent data and services
marketplace that bridges remote data platforms andallows the efficient and privacy-preserving sharing ofvast amounts of heterogeneous, high quality, certified
data and services supporting the common EU dataspaces.

SEDIMARK and data sharing
Key concept• Data lifecycle control and data curation pipeline• AI-based enrichment on top of semanticallyannotated data

• Greener solution as context information does notleave its own context• Recommendation and data monetization schemes• On-demand (distributed) data processing/modeltraining

Going into the real world• Adopt standards and vocabularies prominent

in the IoT domain and emerging data spaces→ allow inter-disciplinary interoperability• Optimise AI models during training andinference → support providers and consumers• Semantic enrichment, Data quality

annotation, Model annotation, ML-oriented
data quality annotation, AI model quality
annotation, AI model validation, data
validation/certification → interoperability
enabler.

Emmanouil G. Spanakis
FORTH-ICS / TRUSTEE projectGoals/Ambition
TRUSTEE, in accordance with the principles of responsible/trustworthy AI, proposes a novel
secure-by-design, homomorphically enabled, federated platform in accordance with EU data
strategy and EU reference architectures capable of ensuring user-friendly, safe, trustworthy,compliant, fair, transparent, accountable, long-term data collection, storage, processing,querying, and delivery.

Pillars of innovation:

Collaboration | Trusted Data Solutions | Automate
Testing and Monitoring | Data driven (AI enabled)
services | Security and data sovereignty | Valueadding apps | Interoperability | Ecosystem
Harmonisation | Policies & legally compliantApplication space - uses cases
TRUSTEE’s
fully encrypted solution
will be validated
through six different
use cases supporting
GAIA-X, EOSC, EGI, etc.
demonstrating a multidisciplinary, PanEuropean federated
FAIR and private data
ecosystem.

Value1. Design a Secure and Trust framework
and Reference Architecture to ensure
end-to-end trust and privacy forstakeholders across European data

4. Analyze the ethical, legal, privacy and
IPR issues for collection, storage,processing, querying, analytics anddelivery of data enabling the European
single market for data

2. Design and develop a distributed
Homographic-capable self-sovereign
framework for accessing, sharing andmanipulating data, compliant to national
and EU legislation

5. Design and develop TRUSTEE
explainable and trustworthy AI for
efficient and robust use and re-use ofdata and metadata acrossinterdisciplinary domains

3. Novel Big Data management andanalytics infrastructure to facilitate use
and re-use of data in data spaces acrossinterdisciplinary science fields andbusiness sectors

6. Provide FAIR databases facilitating
sharing and manipulation of data incompliance with prevailing and emerging
legislation (e.g. GDPR)

Value creation for healthcare

12 Se

nt
lme nerolf E n rt pholerSma
Use ng a susi

rvic
e Registrati
on

Citizen/Patient

Service Privacy
Healthcare services

Data
Sovereignty
3Creation

Digital Identity

Ac cessCon
sent

Service
directory
management

Privacy Preservation

Edward Curry
Insight SFI Research Centre for Data Analytics and theData Science Institute (University of Galway)Edward CurryI have been researching theunderlying technology for data
spaces for the last decade…

Symbiotic Relationship between Data
Spaces and AI….
High Quality Data
Community of Data Users/Owners
Data Goverance/compliance

Provides training data
(Data Space 4 GenAI)
Data SpaceFoundation /Generative AI

Provides models to enable(GenAI 4 Data Space)
Extract Value from Data
Faster innovation cycles
Synthetic Data

Foundation Data Space Models……
A foundation data spacemodel is any model that istrained on broad data(generally from the data
space and using selfsupervision at scale) thatcan be adapted (e.g., finetuned) to a wide range ofdownstream tasks tosupport the life cycle of thedata space.
E. Curry, M. Timilsina, T. Zaarour, M. Al-QATF, R. Haque, “Foundational Data Space Models: Bridging the AI
and Data Ecosystems (Vision Paper), Proceedings of the 2023 IEEE International Conference on Big Data (Big Data),
Sorrento, Italy, 2023

Scene Graph Foundation Model for
Multimodal Semantic Interoperability….

Foundation Model for Scene
Graphs.J. Khan, J.G. Breslin, E. Curry, “Expressive Scene Graph Generation using Commonsense Knowledge Infusion for Visual

27 Understanding and Reasoning,” Extended Semantic Web Conference (ESWC) 2022, Crete, Greece, May-June 2022.

Graphs support the relationships between
data space resourcesDownstream Semantic Tasks:- Image Captioning & Multimodal- Question Answering.J. Khan, J.G. Breslin, E. Curry, “Expressive Scene Graph Generation using Commonsense Knowledge Infusion for Visual
Understanding and Reasoning,” Extended Semantic Web Conference (ESWC) 2022, Crete, Greece, May-June 2022.

Research Directions
Unified Data-AI Ecosystem andBusiness Model• Single data and AI life cycle• Incentivized to share data,models, and computing
infrastructure.
• Shared development toolkits
Data Space Interoperability• Model-based “good-enough”
semantic interoperability• Share semantic interpretation
models between dataspace(domain-specific)

Multimodal Data
• Establish connections andrelationships• Cross-modal information retrieval• From real-time sensor data to 3D
geospatial models

Robustness• Model monitoring and adaptation• Unified data-model lifecycles/Ops
Trusted, Ethical, and Compliant• Intrinsic biases• Integrity & transparency• AI and data governance.J. Khan, J.G. Breslin, E. Curry, “Expressive Scene Graph Generation using Commonsense Knowledge Infusion for Visual

29 Understanding and Reasoning,” Extended Semantic Web Conference (ESWC) 2022, Crete, Greece, May-June 2022.

Tanguy Coenen
IMEC / ACADIEXAMPLE APPLICATION DOMAINS

Floodify image3132

DATA SPACES SHOULD ALLOW FASTER INTEGRATION OF MORE
DIVERSE DATASET INTO ALGORITHMS… BUT WE’RE NOT THERE YET

33

Data integration
orchestrator
Data space
Catalogue

Data Discovery

Policy negotiation

Data conversion

Policy negotiation

Data at rest

Streaming data

Data Consumer

Model(Personal) Data Producer

Data signature

Algorithm

ALGORITHM-CENTRIC AUTOMATED DATA INTEGRATION

Minhea Tufis
Eurecat / PISTIS project

The PISTIS ProjectHanno Focken
Catena-X

From Ego- to Ecosystem
Catena-X for moretransparency andsustainability
DSS 2024
Hanno Focken
Managing Director – Operations & GovernanceCatena-X Association
14/03/2024

Catena-X was founded… to tackle our industries most pressing issues, together.
TRUSTED SUSTAINABILITY!
Scale Circularity with Product & Battery Passports!
Report Product Carbon Footprint (incl. primary data)

Environmental & Social Governance

RESILIENT SUPPLY-CHAINS
Master Data Services & Certificates
Ensure Material Flow with Demand / Capacity Mgt.
- Short- Long-Term!

COMPLIANT & EFFICIENT PLM
Traceability of Parts & Contain non-compliant parts
Live Quality Loops & Analysis

4109.04.2024© 2024 Catena-X or a Catena-X affiliate company. All rights reserved.

Behavior Twins!

Catena-X Value Proposition

Provide a trusted, compliant, global and interoperable environment
for the industry, to create and utilize data driven value chains for
dedicated business processes.

Tier-1mm

Tier-2mmm

Tier-3mmm

Tier-nmmmmmCatena-X offering
Solve the toughest industry problems via radical
collaboration in BIZ / DEV / OPS.

Usage4209.04.2024© 2024 Catena-X or a Catena-X affiliate company. All rights reserved.

Dismantlermm

Recyclermmm▪ Trusted Identity▪ Business KITs

▪ Trusted Data Exchange▪ Interoperable Services▪ Trusted App / Services▪ onBoarding andscalability from
SME to Large Corp.

▪ Trusted GovernanceExample UseCase „Circularity“

Circular Economy
Fast Facts

Catena-X Standards & Artifacts ()Connect partners, streamline flow of information and create solution portfolio to enable scalable value derivation

100 billiontons of material resources
consumed annually

Network builder completes thevalue chain by connecting partners

Trusted material accounts manage
material flow and reduce costs
Circularity dashboards (SRQ / CO2)
recommend optimal R-Strategies< 9%of resources arerecycled or reused~ 30%
secondary material
ratio per vehicle$4 Trillion
demand for battery
materials up to 2050

Digital product passports capture
and carry rel. information end to end>30.000
to be connected industry
partners (to close loops)

Industrie connectors allow exchangeof data and material with other sectors

other industries4309.04.2024© 2023 Catena-X or a Catena-X affiliate company. All rights reserved.

Material market places create a
2nd material supply chain & allow
prediction of supply scenarios

Questions?
Get in touch with us!
Catena-X Automotive Network e.V.
Reinhardtstraße 58
10117 Berlin
info@catena-x.net

Rob SmeetsPhilips

Aitor Corchero
NTT Data

Hello! My name is Aitor
Corchero, and I would like todiscuss about DS in public
administrations
I am Semantic Domain Expert atNTT Data focused on bringing
Semantic interoperability to public
sectorChallenge & Needs Data Spaces in
Public Services
1. Active involvement of public administrations andcompanies providing services to such administrations
2. Improve data interoperability and quality to enablefurther exploitation of data through analytical services;
3. Elaboration and generation of digital services toensure the implementation of governance proceduresin an efficient manner;
4. Establishingcertification
mechanisms
forapplications and services to establish trusted
application catalogues;
5. Automaticallyensuring
law
enforcement
verification and control processes (e. g. GDPR).

Key Messages

Denia Kanellopoulou
ahedd – Demokritos / DeployAI project• one-stop shop for exchanging and building trustworthy AI resources and applications• support European enterprises and the public sector• ensure European sovereignty in AI, especially in Generative AI

European AI-Technology: Cloud native Open Source AI-Technology Accessi.e. DevOps tools / Application Builder / Docker / European Large Language Models

European DATA: Seamless access to European Data Spaces
i.e. Health, Public Administration, Skills, Manufacturing, etc.

European High Performance Computing Ressources
Seamless access to European HPC‘s for testing and AI-training

EU AI-TrustworthinessEU regulatory compliance and trustworthiness support

European AI-Community:
Qualified AI Consulting Network / Access to talents / Training Resources / Professional Services

Thank you!