Data Spaces Symposium
Innovation in Data Spaces:
lowering barriers andincreasing value

Daniel Alonso, Alberto Abella,
Antoine Garnier, JeanetteNilsson, Nuria De Lama, EdCurry, Georg Rehm, Shane ÓSeasnáin

Objectives of the session▪ To discuss on how to lower barriers to facilitate the design,development, use and onboarding in data spaces, from different
perspectives: skills and training needed for easy access andonboarding, how to facilitate technical adoption of data spaces▪ To discuss value creation on data spaces, how AI and other disruptive
applications can benefit from their connection to controlled andtrusted environments as data spaces, that can fuel through data
sharing the potential of those applications. Identify requirements andbenefits

Agenda
Daniel Alonso (BDVA)Intro and framework

How to lower barriers and boost innovation in DS (initial statement + panel discussion)
Alberto Abella

Hands-on technical approach to data spaces. FIWARE connector, solutions and data
modelsAntoine Garnier

Teach skills to build data spaces. How to equip workforce with the necessary
knowledge

Jeanette Nilsson

Hubs / BDVA i-Spaces as practical instruments to lower those barriers

Value creation in Data Spaces (initial statement + panel discussion)
Nuria De Lama

Enablers for providing (business) valueEdward Curry

Data Spaces for Generative AI (and AI for DS). Intelligent Data Spaces

Georg Rehm

Large Language Models and Data Spaces. Observations from the project OpenGPTX

Shane O’Shean

Digital twin and data spaces

How to lower barriers andboost innovation in dataspaces

Data Spaces Symposium
FIWARE connector, solutions
and data models

Alberto Abella.
FIWARE Foundation
Data Modelling Expert@aabellaData spaces by functions

Data spaces by Building Blocks

Data spaces by Building Blocks

Data spaces by Building Blocks

Data spaces by Building Blocks

FIWARE Data Space Connector components
FDSC Open source available at:https://github.com/FIWARE-Ops/data-spaceconnector

It provides:- Identity management W3C DID VC/VP- Compliant with SIOPv2 / OIDC4VP protocols​- Data interchange based on NGSI-LD- Attribute-based access control (ABAC)
following an XACML P*P architecture​- Trust Services aligned EBSI specifications​- Compliant with EU digital identity / Wallet- Compliant with DSBA recommendations- Based on existing components
Used in multiple data spaces (i4Trust, DSCaaS,DOME, Citcom.ai, etc)

Smart Data Models initiative

Smart Data Models is a collaborative program to provide data models for digital twins and data spaces- Free and open-licensed data models for digital market (0€ cost)- Multisector- Based on real use cases and adopted open standards. Collaborative.
- At market speed- Customizable to local needs- Compatible with linked data

Available at https://smartdatamodels.org
Repository at https://github.com/smart-data-models

Smart Data Models for Data spaces
pysmartdatamodels python package includes

1.000 DM and 156.000 terms
It manages data models’ assets with 23 functions
including:- Inserting compliant data in a context broker- Listing any asset (names, definitions, types)- Fuzzy search- Generation of data model compliant examples- Update from central repository- Extends information beyond DCAT catalogue
limitations (structure)- Provides support for creating new DM on the fly
Soon:- Manage your own data models (private)- Check compliance with Data Models- Contribute your data models-Open sourceFully documented
Possible stand alone-Available
https://pypi.org/project/pysmart
datamodels/104 stars
52 forks-

Summary
FIWARE components provides the foundations forrunning data space:- Based on open source technology- Based on already proved components- Compliant with standards- Beyond some limitations DCAT

Data Spaces Symposium
Professional Certification for
Data Spaces Experts

Antoine Garnier

IDSA

Skills Gap

Data spaces cannot be built without
knowledge and expertiseProfessional Certification Programme▪ IDSA is developing the Data Spaces Body of Knowledge(DSBOK) laying down essential information to develop
professional training▪ New Certifications▪ Data Spaces Fundamentals▪ Data Spaces Business Consultant▪ Data Spaces Technical Consultant▪ Open to the community

Join us now!
▪ Join as training provider for theprogramme▪ Keep me posted about the firsttraining courses to be available

Scan me!

Scan me!

Data Spaces Symposium
How data spaces are providing value totheir stakeholders, types of value. View
from hubs / BDVA i-Spaces

Jeanette Nilsson RISE

Nearly

3,993
SEK million, net sales

3,300
employees

Operating results: 22 SEK million

We are represented at

35
locations aroundSweden

Operating margin: 0,6%4%
20%
46%

40%
women

30%

Alla siffror avser 2022.

Distribution of net sales
Business sector

1,831 MSEK

Public funds

1,179 MSEKState funds

812 MSEK

EU funds

171 MSEK

130+
Testbeds anddemonstration environments

78
Customer Satisfaction Index

Cybersecurity

Internet of Things and 5G• Cyber Range testbed• Vulnerability testing• IoT security• AI & Cyber• Cyber Node• Battery-free IoT• Secure IoT transfer• 6G security

Data platforms

AI and machine learning

Industrial data analysis

AI for network automation
Resource- efficient ML
Soundscape analysis
Cross-lingual and Multilingual AI• Knowledge graphs and reasoning• Predictive maintenance• Causal inference• Compilers• AI & Earth observation data
• Digital twins• Edge computing platforms• High Performance Computing••••

Datacenter• Datacenter technologies• Heat reuse& energy efficiency• Cloud & Edge testbed

AI at RISE – 150+ projects, 450+ network
Technologyareas
Center for
applied AI

Computervision

Advanceddataanalysis

Cybersecurity

Digitalinnovation

Transportand mobility

Energysystems

Materials
andproductionSynergistic
areas
Application
areas
Test & DemoDeeplearning

Naturallanguageprocessing

Planning
andscheduling

DigitalethicsAI
platforms
and edge

Automatedreasoning

Knowledge
representation

Education

Policyinnovation

Interaction
designSoftwareengineering

ClimatechangeTelecom

Healthcare

ICE
data centerIndustrial
automation

AI agenda

Built
environment

ToD:
Cyber Range

Food andagriculture

Chemistryand pharma

ToD:
AstaZero

ToD:
5G/EdgeSpecialprojects

BDVA labelled i-Spaces 2023

Infrastructure
Technology(12 KPIs)

Federation(11 KPIs)

Services(11 KPIs)

Data
Ethics
Projects
Sectors(13 KPIs)

Sustainability(8 KPIs)

Ecosystem
Impact(5 KPIs)

How to lower barriers andboost innovation in dataspaces

Value creation inData Spaces

Data Spaces Symposium
Enablers for Business Value

Nuria de Lama
Consulting Director
IDC Government Consulting

Investments to derive value from data:
Worldwide Data and Analytics 2024 Predictions (IDC)

IDC forecasts that data and analytics
software spending will grow at a CAGR of
16% through 2027 to reach close to $340 billion
for both analytics and operational workloads© IDC |

29

Technology Capabilities and Organizational Competencies for Data Spaces© IDC |30

A view on Impact Assessment
Impacts: long-term effects produced as aresult of an intervention
An outcome is a likely or achieved shortmedium term effect of an intervention's
outputs
Outputs are the products, goods andservices which result from implementedchange activities including new policies,
legislation, technological solutions,infrastructure.
Inputs are the required set of resources
needed for a transformation process
The context refers to specific featuresand dimensions to drive transformationand impact through data space use cases
and initiatives© IDC |31

Data Spaces for GenAI&GenAI for Data Spaces
Prof. Edward Curry
University of Galway
Insight SFI research Centre for Data Analytics

Edward CurryI have been researching theunderlying technology for data
spaces for the last decade…

Generative AI and Foundation models
will be the Killer App for Data Spaces….

34

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 dataspace 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

Data Spaces for GenAI: Centralise information fromthe data space, then adapt to a wide range ofdownstream tasks….
Data Space Tasks
CatalogCatalog
Local Storeand Index

Data SpaceVideo

Structured
Data

Sensors & Time
SeriesGraphsSimulation

Tables & Text

Symbolic Logic

ImagesApplications

Speech/Audio

Support Services(Downstream Tasks)

Adaptation

Training
Foundation Data
Space Model

Trained on extensive datasetsin the data space to establish
a knowledge foundation

Enable Data Space(Users & operations)

Leverage transfer
learning and scale invarious downstream
tasks

Data
Cleaning
EntityLinking
Semantic
Approximation

Information
Extraction
Embeddings
Image
Captioning

Vocab
HubRelation
Extraction

Search & Query
Question
Answering
ResultSummarisation
MultimodalQueries
Discovery
Sentiment
Analysis
Event &Stream
Processing
Instruction
FollowingData Space Task coverage by Foundation Models
ServiceDSSP Task
Metadata management

Adaptation to Downstream Tasks
Automated metadata generation, metadata tagging and classification, multimodal metadataenrichment, Named Entity Recognition (NER), content summarization, semantic enrichment,
and data relationship mapping.
Catalog
Search and Browsing
Keyword search enhancement, natural language query processing, semantic search,
similarity search, multimodal search, content summaries, and question answering.
Keyword Search
Semantic understanding, query expansion, natural language query processing, Named EntityRecognition, Contextual awareness and personalization
Metadata queries
Metadata attribute recommendation, complex query parsing, semantic matching, metadatasummaries, relation extraction, Named Entity Recognition
SearchandStructured
and complex Semantic understanding, Query completion, translation, reformulation, or rewriting, complex
queryqueries
query parsing, query planning and optimization, multimodal queries, query resultsummarization, question answeringApproximate query processing Semantic understanding, approximate nearest neighbour search, textual similarity andsemantic matching
Schema mapping
Word Embeddings, textual similarity and semantic matching, relation extraction, Named
Entity Recognition
Local
Store
Data integration
Data quality assessment, data cleaning, data transformationand index
Caching and Monitoring
Caching recommendations, cache invalidation, caching content generation (e.g., summaries,responses, suggestions), event detection
Relationship Identification
Knowledge Graph Embeddings, relation extraction, link prediction, clustering, and entity
resolution and matching
Discovery
Approximate
Semantic Word embeddings, query expansion, approximate nearest neighbour search, and textual
Matching
similarity
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

GenAI for Data Spaces: Supports tasks
across the life cycle of the data space…

39

Data Spaces Symposium

Georg Rehm

DFKI

LLMs and Data Spaces:Observations from OpenGPT-X
Georg Rehm and Martin Courtois (DFKI)

Data Spaces Symposium Darmstadt,Innovation in Data Spaces Session
13 March 2024

multilingual – versatile – trustworthy – open source

Develop open LLMs for enterprises●Gaia-X project funded by the Federal Ministry for
Economic Affairs and Climate Action (BMWK):
19 million euros total budget (2022 to 2024)●

Nine project partners.

●Coordinator: Fraunhofer IAIS & IIS.

●Addresses concrete business needs.

●German and European (EN, FR, ES, IT) languages➢Improve freedom of choice for enterprises
Strengthen Germany's digital sovereignty➢

Technical Architecture and Components
Self Description Tools
Manual or SDcreation wizard

LLM/ServiceProvider
Writeclaims

Federated Catalogue
FC-service(Local / IONOS Cloud)

VC/VP

Verified and issued

store
VC Wallet
FC database(postgres)Notarization Service
FC-service Signer

User

Consume Service
Query FC Services(SD, participants ,etc.)

Federated
Catalogue

Register at Gaia-X as
consumer and gets VC

search▪ VC – Verifiable Credentials▪ VP – Verifiable Presentation▪ LLM – Large Language Model▪ FC – Federated Catalog▪ SD – Self Description

Technical Architecture and Components
Step 1: Register LLM Service Provider as a Participant

Create and ingest Self-Description to Gaia-X Federated Catalogue• Develop Self-Description using SD-generator• Ensure Self-Description conforms with the respective Gaia-X schema• Validate using SHACL shapes• Generate public-private key pair

Technical Architecture and Components
Step 2: Publish LLM Service Offering• Create Self-Description to describe the offering• Then verify, sign and publish SD to the Federated Catalogue to be accessedby the consumer

The example in the demo shows the existing Language Technology platform
European Language Grid developed in an EU project (2019-2022), which wefurther extend in OpenGPT-X so that it adheres to Gaia-X.
In the example, we provision a data set in Gaia-X through ELG.

Federated Catalogue

Data Spaces Symposium

Shane Ó Seasnáin

TUEEuropean Contribution to Technology
Local Digital Twins

Data Spaces

Generative AI

e.g. Aleph Alpha, Mistral AI

A Crisis of Awareness
Digital Twin
Data Space
Generative AIGDPR

Major Societal Problems
Global Warming
Housing Shortages
Nitrate Air Pollution
Energy Transition
Labor Shortages
Strategic Security…

DigitalTwin

Making Twins Simpler Through
Data Spaces and Gen AI

Data Space as a Service:- Feature Extraction- Version Management- Public Data Spaces
Generative AI Engine:- Generated (Surrogate) Twins- User Flexibility- Thinking with the UserHPC

Value creation inData Spaces

Thank you!