=Paper=
{{Paper
|id=Vol-3705/short12
|storemode=property
|title=Challenges and Opportunities for Enabling the Next Generation of Cross-Domain Dataspaces
|pdfUrl=https://ceur-ws.org/Vol-3705/short12.pdf
|volume=Vol-3705
|authors=Rohit A. Deshmukh,Diego Collarana,Joshua Gelhaar,Johannes Theissen-Lipp,Christoph Lange,Benedikt T. Arnold,Edward Curry,Stefan Decker,Maximilian Stäbler,Paul Moosmann,Patrick Dittmer,DanDan Wang,Frank Köster,Christoph Lange
|dblpUrl=https://dblp.org/rec/conf/sds3/DeshmukhCGT0ACD24
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==Challenges and Opportunities for Enabling the Next Generation of Cross-Domain Dataspaces==
https://ceur-ws.org/Vol-3705/short12.pdf
Challenges and Opportunities for Enabling the Next
Generation of Cross-Domain Dataspaces
Rohit A. Deshmukh1 , Diego Collarana1 , Joshua Gelhaar3 , Johannes Theissen-Lipp1,2 ,
Christoph Lange1,2 , Benedikt T. Arnold1,2 , Edward Curry4 and Stefan Decker1,2
1
Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany
2
RWTH Aachen University, Aachen, Germany
3
Fraunhofer Institute for Software and Systems Engineering ISST, Dortmund, Germany
4
Insight Centre for Data Analytics, University of Galway, Galway, Ireland
Abstract
Dataspaces are regarded as a standardized solution for sharing data in a trusted way. However, providing
and sharing high-quality data across dataspaces poses several scientific and technical challenges, opening
new research avenues. Developing governance models and technologies for supporting cross-domain
integration of data and services from the existing single-domain dataspaces represents a significant
challenge. In this vision paper, we discuss the challenges for enabling next-generation dataspaces and
propose an innovative approach that aims at developing a vision and identifying requirements and
building blocks for next-generation dataspaces, followed by defining a roadmap and practical migration
paths for the existing dataspaces towards the next-generation dataspaces.
Keywords
Cross-Domain Dataspaces, Interoperability, Semantic Interoperability, AI, Large Language Models
1. Introduction
Dataspaces are essential for enabling data sharing among participants in a sovereign, interop-
erable, and trustworthy manner. They have gained prominence in Europe with the European
Data Governance Act as part of the European Data Strategy1 . Dataspaces set the path to a
digital single market where data can flow seamlessly and securely across the borders and
sectors within the EU. In the last few years, due to the increasing need to enable secure and
interoperable data-sharing infrastructures among industries in the EU, dataspaces have been
getting increasing attention from the research community and the industry. However, the siloed
work of several independent initiatives has resulted in various definitions of dataspaces [1].
To prevent fragmented and heterogeneous implementations of dataspaces, the Data Spaces
The Second International Workshop on Semantics in Dataspaces, co-located with the Extended Semantic Web Conference,
May 26 – 27, 2024, Hersonissos, Greece
$ rohit.deshmukh@fit.fraunhofer.de (R. A. Deshmukh); diego.collarana.vargas@fit.fraunhofer.de (D. Collarana)
0000-0003-2885-7076 (R. A. Deshmukh); 0000-0002-2583-0778 (D. Collarana); 0000-0002-8358-5424 (J. Gelhaar);
0000-0002-2639-1949 (J. Theissen-Lipp); 0000-0001-9879-3827 (C. Lange); 0000-0001-8594-880X (B. T. Arnold);
0000-0001-8236-6433 (E. Curry); 0000-0001-6324-7164 (S. Decker)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073
1
https://digital-strategy.ec.europa.eu/en/policies/strategy-data
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Support Centre (DSSC)2 is working towards identifying common guidelines and building blocks
to accelerate the development of dataspaces in Europe. While the DSSC’s harmonization of
dataspace concepts enhances interoperability, establishing compatibility in a cross-domain
dataspace scenario remains an open question.
Dataspaces are currently being established in specific domains3 , such as Manufacturing,
Mobility, Culture, and Healthcare. Interoperability among such domain-specific dataspaces
must be facilitated to enable an effective sharing and reuse of data across domains that will help
companies develop new innovative business models and revenue streams. Research is required
to establish the foundations and technology for this interoperation. This paper identifies gaps
in the existing dataspace efforts, presents the challenges for enabling cross-domain dataspaces
and proposes a novel approach that advocates for a coherent strategy combining semantic
interoperability, human-centricity, trust, data stewardship and service quality.
2. Current Generation of Dataspaces
Several research initiatives develop architectures and technologies for dataspaces. For instance,
the International Data Spaces (IDS) initiative4 has developed the IDS Reference Architecture
Model and the IDS Information Model [2] to enhance interoperability and sovereignty in data
sharing. While Gaia-X5 also shares these objectives, it additionally covers sovereignty over data
in cloud infrastructures. However, neither IDS nor Gaia-X implements Persistent Identifiers
(PIDs) (Pitfall 1), which have been a key enabler for interoperability in and across research data
infrastructures. Therefore, transferring and evaluating this idea in the context of B2B industrial
data infrastructures, or dataspaces is essential for addressing link rot and identifier clashes [3].
Most of the initiatives, including IDS and Gaia-X, advocate the use of semantics in datas-
paces [4]; however, its potential is not yet fully utilized in actual implementations (Pitfall 2).
Experts report6 that interoperability is often seen only on the metadata level and not on the
actual data payload level. There are no industry-specific controlled vocabulary and knowledge
graphs in place today. It is, therefore, clear that an effective use of semantics is necessary for
the success of dataspaces. However, the Semantic Web is perceived as complex by developers
and its practical adoption is usually challenging for them [5, 2] (Pitfall 3). Therefore, there is
an urgent need for new technological solutions to reduce this complexity and make the use of
semantics in dataspaces user-friendly.
The three pitfalls mentioned above are exacerbated by the emergence of cross-domain data
ecosystems, i.e., by the various interaction and exchange relationships among cross-domain
dataspace participants. Data ecosystems enable data reuse, the integration of data users and
data providers, and thus the linking of data to innovative services [6, 7]. Enabling a federation
of dataspace ecosystems [8] leads to additional requirements concerning interoperability of
metadata, data, identities, access policies, trust among participants, and pricing and governance
models, etc., that need further research.
2
https://dssc.eu/
3
https://digital-strategy.ec.europa.eu/en/policies/data-spaces
4
https://www.internationaldataspaces.org/
5
https://gaia-x.eu/
6
https://www.trusts-data.eu/data-spaces-semantic-interoperability/workshop-report-pictures-slides/
3. Challenges and Opportunities for Next-Generation Dataspaces
In the evolution of dataspaces, enabling cross-domain interoperation holds the potential to
enable new use cases (Figure 1). This involves several challenges and opportunities:
PID Infrastructure: PIDs play a crucial role in addressing link rot and identifier clashes [3]
and facilitating interoperability. However, existing dataspace infrastructure concepts lack
several requirements. The current PID infrastructure is openly accessible and centralized, which
is problematic for competitive industries. Centralized systems also pose single points of failure.
Examples of existing PID systems include ORCID.org, handle.net, and DOIs.
Data Quality, Incentivization, and Governance Frameworks: In the existing prevailingly
domain-centric dataspaces involving participants at different levels of technical maturity, obtain-
ing high-quality data is a big challenge. It takes a lot of effort, time, and several iterations to get
the data providers to provide high-quality data. Organizational rules govern data provision, and
typically, data providers do not receive any incentives, although their data allow for value-added
services. Therefore, designing an economic model that incentivizes high-quality data provision
and guarantees fair returns for providers is crucial.
Sector-specificity vs Interoperability: Catering for the needs of sectors and to boost
digitalization and innovation, it is necessary to enable and encourage bottom-up and sector-
specific initiatives. While doing this, we must avoid silos, and ensure interoperability and
seamless data flow across sectors and borders.
Complexity of Semantic Web and Practicality: Interconnecting dataspaces requires
research on varying levels of granularity and technical depth (ranging from fine-grained service
descriptions with Semantic Web standards to more abstract labelling frameworks [9]), for
finding the balance between a practical integration practice and complexity of options. Early
experiments with integrating data standards for domain-specific knowledge models [2] show
that such an integration goes beyond an engineering challenge but poses scientific questions [5]
regarding, e.g., how to delineate universal knowledge representation from application logic,
and the scalability of declarative vs. procedural approaches.
The scientific challenge is to identify a logic for representing data semantics and communica-
tion protocols that is sufficiently expressive to capture relevant aspects of domain knowledge
and sufficiently flexible to cope with the continuous evolution of data standards while remaining
practically applicable for service providers given their scalability, compatibility, and compliance
constraints. The technical challenge is to make essential services such as the persistent identifi-
cation of artefacts scale across dataspaces and to define a process for migrating existing “live”
dataspaces, into which participants have invested development efforts and where businesses are
in operation, and to provide tooling support for executing this process with minimal disruption.
4. Approach for Enabling Next-Generation Dataspaces
Our approach to address technical, governance, and economic challenges consists of three
steps: (1) Bottom-up investigation: A thorough exploration, analysis, and evaluation of existing
dataspaces; (2) Top-down ideation: Development of a comprehensive vision for next-generation
dataspaces in a green-field approach, i.e., not bound by limitations of legacy design and im-
Prerequisites: Federated PID infrastructure, interoperability Personal
at various levels, trust among participants, availability of Dataspace
high-quality data, generative AI-based tools
The following requires connections primarily to the
Culture dataspace (DS) X and additionally to some Health
other DSs as indicated: Tourism Dataspace Green Deal
Dataspace Dataspace
Connecting to a DS/federation:
- How do I connect to dataspace X as a company
that wants to share data?
- How can I offer movie showtimes for my theater
available as a CSV dataset through DS X? Can you Information Information
make that happen for me? I want to get paid per Model Model
API access.
- Create a workflow to fetch and aggregate Federated Federated
showtimes of movies and plays in city C. Display it Metadata Metadata
on our Portal. Tool with LLM- Catalog Catalog
based Chat
Using a DS/federation: Interface
- Can you show movie and play showtimes near
me?
- Book 2 tickets for movie M. Low-Code High-quality Low-Code
- I liked the shooting location in movie M and would Semantic semantically annotated Semantic
Testing &
like to visit it. Can you find the location and discover Sidecar data payload Sidecar
debugging
the best tourism company offers? (Tourism DS)
- I'd like to visit Greece for 5 days. Can you prepare User
an itinerary for me based on my preferences? Also, Data Provider Data Consumer
please avoid polluted places not suitable for Application Application
asthmatics. (Tourism, Personal, Health, Green Culture Dataspace X
Deal DSs) Mobility Dataspace
Figure 1: Our vision for enabling the next generation of cross-domain dataspaces.
plementation decisions, and (3) Migration roadmap: Development of a strategic roadmap and
practical migration paths from current dataspace systems towards next-generation dataspaces.
Addressing Complexity of Semantics in Dataspaces with User-friendly Tools and
Interfaces: To foster technical interoperability, we propose adapting the widely accepted FAIR
Data Principles (Findable, Accessible, Interoperable, Reusable) [10] originating from research
data management to dataspaces and Knowledge Graphs [11]. Specifically, we aim to develop a
distributed PID infrastructure for persistent identification and identifier mapping, including
service descriptions with Semantic Web standards to more abstract “labeling frameworks”.
Furthermore, to bridge the gap between Semantic Web and dataspaces, our approach includes
using tooling support. We plan to use intuitive, user-friendly tools to enable an effective use of
semantics in dataspaces at every stage in the lifecycle of semantic data–from provision, semantic
enrichment, and matchmaking to composition and consumption.
The latest advances in AI make it an excellent technology for improving user interface to
dataspaces. We plan to explore using Large Language Models (LLMs) to automate tedious and
repetitive tasks such as data mapping, translation, and semantic enrichment and integration,
ultimately reducing costs and manual effort. Furthermore, the use of low-code development
environments has been explored before to enable data integration and service composition, e.g.,
in the manufacturing domain [12]. We plan to enhance such proven tools with Semantic Web
technologies and LLMs, particularly generative AI, to simplify complex and tedious operations
in dataspaces. We also plan to investigate enhancing LLMs with Knowledge Graphs to reduce
hallucinations, increase deterministic behavior and predictability and thus, data quality.
Addressing Data Quality with Incentivization: Based on the experiences gained from the
existing dataspaces, we aim to establish a robust, flexible governance structure that facilitates
seamless data interoperability, while protecting sovereignty, and ensuring fair data sharing.
Our governance model will also include the aspect of economics for incentivizing the provision
of high-quality data. Firstly, we plan to develop a reward or incentive system that aligns with
the value generated by data. This will encourage more parties to provide high-quality data.
Secondly, we propose a mechanism to evaluate the true value of data by tracking its usage and
assessing its value based on impact and usefulness. This mechanism could be complemented
by trust value-assessment features to strengthen the built-in reward system further, ensuring
fair compensation to data providers proportional to the value and trustworthiness of their data.
Finally, we plan to explore cost recovery models for dataspace sustainability and new business
models enabled by dataspaces, including data sharing, marketplaces, and value-added services,
to create new revenue streams and monetize data innovatively.
Thus, our novel approach fosters a coherent strategy combining interoperability, user-
friendliness, trust, incentivization, data stewardship and service quality. Future work includes
implementing the presented approach, and developing and evaluating technological and process
building blocks to ensure a smooth migration towards next-generation dataspaces.
Acknowledgments
Funded by EU Commission within ’Data Spaces Support Centre’ (grant 101083412), Science
Foundation Ireland (SFI) (grant SFI/12/RC/2289_P2) & German Federal Ministry of Education and
Research (BMBF) within ’WestAI’ (grant 01IS22094C) & ’FAIR Data Spaces’ (grant FAIRDS05).
References
[1] E. Curry, S. Scerri, T. Tuikka, Data Spaces: Design, Deployment, and Future Directions,
2022.
[2] S. Bader, J. Pullmann, C. Mader, S. Tramp, C. Quix, A. W. Müller, H. Akyürek, M. Böckmann,
B. T. Imbusch, J. Lipp, S. Geisler, C. Lange, The International Data Spaces Information
Model – An Ontology for Sovereign Exchange of Digital Content, in: ISWC, 2020.
[3] S. Auer, Semantic Integration and Interoperability, Springer Nature, 2022, pp. 195–210.
[4] J. Theissen-Lipp, M. Kocher, C. Lange, S. Decker, A. Paulus, A. Pomp, E. Curry, Semantics
in Dataspaces: Origin and Future Directions, WWW ’23 Companion, 2023.
[5] R. Verborgh, M. Vander Sande, The semantic web identity crisis: in search of the trivialities
that never were, Semantic Web 11 (2020) 19–27.
[6] J. Gelhaar, T. Groß, B. Otto, A taxonomy for data ecosystems (2021).
[7] M. Jarke, B. Otto, S. Ram, Data sovereignty and data space ecosystems, 2019.
[8] B. Otto, The evolution of data spaces, in: Designing data spaces: The ecosystem approach
to competitive advantage, Springer International Publishing Cham, 2022, pp. 3–15.
[9] Gaia-x policy rules and labelling document - 22.11 release., 2021. URL: https://docs.gaia-x.
eu/policy-rules-committee/policy-rules-labelling/22.11/.
[10] M. Wilkinson, The FAIR guiding principle for scientific data management and stewardship
(2016). URL: https://arrow.tudublin.ie/dataguide/2.
[11] P. A. Bonatti, S. Decker, A. Polleres, V. Presutti, Knowledge graphs: New directions for
knowledge representation on the semantic web (Dagstuhl seminar 18371) (2019).
[12] R. A. Deshmukh, D. Jayakody, A. Schneider, V. Damjanovic-Behrendt, Data spine: A
federated interoperability enabler for heterogeneous IoT platform ecosystems (2021).