=Paper=
{{Paper
|id=Vol-3214/WS6Paper4
|storemode=property
|title=Towards a Digital Twin for Simulation of Organizational and Semantic Interoperability in IDS Ecosystems
|pdfUrl=https://ceur-ws.org/Vol-3214/WS6Paper4.pdf
|volume=Vol-3214
|authors=Patrício de Alencar Silva,Reza Fadaie,Marten van Sinderen
|dblpUrl=https://dblp.org/rec/conf/iesa/SilvaFS22
}}
==Towards a Digital Twin for Simulation of Organizational and Semantic Interoperability in IDS Ecosystems==
https://ceur-ws.org/Vol-3214/WS6Paper4.pdf
Towards a Digital Twin for Simulation of Organizational and
Semantic Interoperability in IDS Ecosystems
Patrício de Alencar Silva1,2, Reza Fadaie1 and Marten van Sinderen1
1
University of Twente, Drienerlolaan 5, Enschede, 7522 NB, The Netherlands.
2
Graduate Program in Computer Science UERN/UFERSA, Rio Grande do Norte State University (UERN),
Federal University of the Semi-Arid Region (UFERSA), Mossoró, RN – Brazil.
Abstract
An International Data Space (IDS) aims to support sharing sensitive data among trusted
actors, enabling data owners to control how other agents could use their data, a property
commonly denoted as data sovereignty. Data sharing with autonomy is increasingly essential
for modern businesses to form ecosystems providing complex services to demanding clients.
An IDS ecosystem requires the formation of data-sharing agreements involving different
business roles. A data usage contract constitutes a central artifact to formalize this type of
agreement. It can also guide actors in implementing or selecting the software components
required to enforce data sovereignty. However, there are at least two critical challenges to
overcome before forming data-sharing agreements in IDS. First, actors may interpret or
represent data usage contracts differently, resulting in a semantic interoperability problem.
Second, even assuming semantic mismatches as resolved, contract formation, in this case,
would require business process alignment, which leads to an organizational interoperability
problem. To address these issues, we envision a digital twin to simulate the formation of
data-sharing agreements in IDS, which could support companies exploring semantic and
organizational mismatches in this kind of environment. It could also help them assess the
risks of adopting and implementing IDS technology. The contribution of this paper is
threefold: (1) a research design based on the problem-solving perspective of Design Science;
(2) a preliminary architectural model of the digital twin; and (3) a capability assessment of
tools for modeling the digital twins envisioned by this research.
Keywords 1
Data sovereignty, digital twin, enterprise interoperability, international data spaces.
1. Introduction
The notion of data sovereignty involves the control, property, or ownership over data claimed by
different agents, ranging from individuals to countries [1]. An International Data Space (IDS), more
specifically, is an environment to enforce companies' sovereignty over the exchange of competitive
advantage data [2]. IDS may soon turn into a shortcut whereby companies will become trusted by
competence, not only long-term business cooperation, to access and use data from one another to,
among many possible goals, optimize internal operations and service delivery. In Europe, the
International Data Spaces Association (IDSA) is the frontline organization promoting the
organizational and technical guidelines to realize the IDS vision [3]. Those guidelines allow actors to
assume multiple and overlapping business roles, eventually leading to different corporate alliances,
supported by multi-sided data platforms and possibly constituting collective data-sharing agreements
[2, 4]. Such contracts could formalize data flow restrictions in an IDS ecosystem.
Proceedings of the Workshop of I-ESA’22, March 23–24, 2022, Valencia, Spain
EMAIL: p.dealencarsilva@utwente.nl (P. de Alencar); m.r.fadaie@utwente.nl (R. Fadaie); m.j.vansinderen@utwente.nl (M. van Sinderen)
ORCID: 0000-0001-6827-1024 (P. de Alencar); 0000-0002-0536-1521 (R. Fadaie); 0000-0001-7118-1353 (M. van Sinderen)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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[4] propose a set of constraints necessary to form data-sharing agreements in IDS, subdividing
them into contractual conditions and data usage contracts. Contractual conditions include
commercial clauses (e.g., costs of data usage), legal conditions (e.g., IPR constraints), and service
level parameters (e.g., data accuracy). A data usage contract should include: (1) an access control
policy defining role-based access permissions; (2) a data usage policy constraining data disclosure;
and (3) a security profile of authentication and authorization requirements to which a data user should
comply. These constraints constitute a starting point for discussing more specific realizations of data-
sharing agreements for IDS. It should be possible to enforce those constraints at different managerial
levels. While a business network model could prelude the contractual conditions, a data usage
contract would constitute a document provided by a data owner to support software-based
enforcement of data sovereignty claims over a particular data asset.
However, some practical barriers may arise in establishing a consensus over contractual conditions
and data usage contracts in IDS ecosystems. First, those contractual conditions will push
organizations to comply with IDS recommendations, such as the Reference Architecture Model
provided by the International Data Spaces Association (IDSA) [5]. Such compliance demands
business process alignment: an organizational interoperability problem. Second, organizations may
interpret data usage contracts differently, leading to semantic interoperability issues. Despite efforts to
propose data-sharing standards for specific business domains (e.g., the Open Trip Model (OTM) for
Logistics), data owners can attempt to protect the same data assets with different data policies,
depending on the targeted data users. These problems may even impact legal and technical aspects of
Enterprise Interoperability, as prescribed by the European Interoperability Framework [6]. They can
also hinder companies' adoption of the IDS vision and the implementation of its recommended
technological infrastructure.
Therefore, the main research question addressed in this paper is: how to simulate organizational
and semantic interoperability problems in the formation of data-sharing agreements in IDS
ecosystems? A digital twin could help address this problem in at least three ways: (1) represent the
formal structure of a system of organizational roles underlying the IDS ecosystems; (2) clarify the
internal structure of data policies and simulate semantic mismatches in their descriptions; and (3)
mimic the discovery and selection of IDS data connectors to enforce data sovereignty in IDS
ecosystems. This work takes a problem-solving approach based on Design Science [7]. The specific
guidelines to build the digital twin grounds on a methodology for modeling digital twins in the
context of Industry 4.0 [8].
The rest of this paper develops as follows. Section 2 provides a research design with knowledge,
technical and practical questions decomposing the main research question stated in the introduction.
Section 3 presents a preliminary architecture for a digital twin for simulating semantic and
organizational interoperability issues in IDS ecosystems. Section 4 brings an analysis of tools for the
modeling and implementation of the digital twin. Section 5 provides preliminary conclusions and
proposes directions for future research.
2. Research design
Problem decomposition is an essential part of Design Science [7]. For research in Information
Systems, Design Science recognizes at least three types of research questions: (1) knowledge
questions, i.e., descriptive or explanatory inquiries about the system's phenomena of interest; (2)
technical questions that depict the state-of-the-art technology for proof-of-concept prototyping; and
(3) practical questions expressing stakeholders' demands. The questions found relevant in this
research follow.
• Knowledge questions: What are the underlying organizational models of IDS ecosystems?
What phenomena are relevant to describing or explaining (e.g., enactment of data usage
contracts and forming data-sharing agreements)?
• Technical questions: What are the architectures, methodologies, requirements, and standards
for building a digital twin for IDS-based Logistics ecosystems?
• Practical questions:
o Trade-offs: What are the risks and benefits of disclosing sensitive data and investing in
IDS technology? How could a company mitigate these risks?
o Sensitivity analysis: How would changes in bilateral data usage contracts affect the
organizational interoperability of an IDS ecosystem? Conversely, how would
organizational changes in an IDS ecosystem affect the semantic interoperability of its
internal data usage contracts?
The methodology to address these problems will comprehend a triangulation of research methods.
A literature review will unveil organizational elements of an IDS ecosystem and the state-of-the-art
technology for modeling them in a digital twin. Simulation with the digital twin will help answer the
trade-off and sensitivity analysis questions. The construction of the digital twin will demand specific
research methods to guide the modeling of the digital twin and a networked ontology to represent the
internal state of the digital twin. Last, Technical-Action Research (TAR) will help evaluate the utility
of the digital twin in promoting acceptance of the IDS vision by small and medium enterprises.
3. Preliminary architecture of a digital twin for simulation of organizational
and semantic interoperability in IDS
[9] stated that digital twins are cyber representations of objects in the real world that facilitate
their development, analysis, simulation, monitoring, persistence, and management. Kritzinger et al.
classified digital twins by the type of communication between their physical system and digital
counterparts as (1) digital models, where the transfer of information between the physical system and
the digital object is manual; (2) digital shadows, in which the physical object pushes information
automatically to the digital object, but the converse transfer occurs manually; or (3) digital twins,
where the communication is bilateral and fully automated. Modeling a digital twin depends on the
type of problem encountered in the physical system, e.g., innovation, optimization, or repair.
The IDS vision of trusted data exchange ecosystems is essentially innovative. The technical and
organizational guidelines provided by IDSA are promising [5], but real-world implementations are yet
to come. Companies still struggle to understand the benefits of the IDS vision, which will eventually
arrive at the cost of data usage prices and certifications for organizational assets and software
components. If companies do not understand such a trade-off, investing resources to pave the IDS
vision will become problematic. A digital twin could help address this problem. In this research, we
are mainly concerned with Enterprise Interoperability problems that may surge in different levels,
such as:
• Organizational interoperability: formation of data-sharing agreements – a data-sharing
agreement in IDS is a multi-sided contract of responsibilities and obligations in dealing with
sensitive data from the IDS actors [4]. Organizational interoperability issues may occur in: (1)
assigning IDS roles to actual actors; (2) ascribing business activities to roles, e.g., metadata
discovery and publication, software, and organizational certification issuance; (3) discovering
and selecting data connectors; (4) acquiring IDS-ready labels; (5) ranking organizational
models of IDS ecosystems based on economic effectiveness and efficiency criteria.
• Semantic interoperability: semantic reconciliation of data usage contracts – a data owner
may impose different restrictions on multiple data users. Semantic interoperability issues may
arise in: (1) reconciling multiple vocabularies to express data policies; (2) aligning different
interpretations of the same data access policy; (3) minimizing loss of information due to data
inconsistency; and (4) semantic discovery and selection of IDS data connectors.
There are alternative frameworks to guide the modeling of digital twins. This research adopts the
Digital Twin Framework for Manufacturing guidelines based on the ISO/DIS 23247 standard [10],
which provides a reference architecture to build digital twins with four logical layers supported by
reusable software components. Figure 1 depicts a preliminary architecture of a digital twin to simulate
the organizational and semantic interoperability issues to be addressed in this research. The
description of its layers and components follows in a bottom-up sequence:
• Observable element: Unlike the Manufacturing cases described in the ISO/DIS 23247
standard (where an observed part is a physical machine), in this research, this element
comprehends a model representing the current state of an IDS-based Logistics ecosystem.
• Data Entity: This layer has two sub-entities:
o Data Collection Sub-Entity: A crucial element is the sensitive data asset to exchange.
This sub-entity will provide a logical container for Logistics Operational Data
described in an industry standard, e.g., the Open Trip Model (OTM).
o Control Sub-Entity: This logical container will provide a process coordination model
describing the control flow and the temporal restrictions on how the actors
participating in an IDS-based Logistics ecosystem will exchange Logistics
Operational Data. These models will support the derivation of Key-Performance
Indicators (KPIs) for operational optimization in the ecosystem.
Figure 1. The architecture of a digital twin for simulation of semantic and organizational
interoperability issues in Logistics IDS ecosystems
• Core Entity: Contains the reasoning mechanisms of the digital twin, aimed to simulate
semantic interoperability issues that could impact data-sharing contracts for IDS ecosystems. It
has the following sub-entities:
• Operation Sub-Entity (Networked IDS ontology): Three interrelated domain ontologies will
describe the operational state of an IDS ecosystem – an ontology to describe the organizational
roles of an IDS ecosystem, one to formalize data usage contracts, and yet another one to
classify IDS data connectors;
• Interchange Sub-Entity (Knowledge graph): Will aggregate ontology instances and external
data sources (e.g., ERP or IoT stream data and events), enabling knowledge inference about the
state of an IDS ecosystem;
• Service Sub-Entity (Simulation & Analytics): Will query the ontologies based on the data
sovereignty requirements provided by the users and provide feedback in terms of trade-offs and
sensitivity analysis of loss of information and sovereignty. It will also provide process
optimization guidelines to the components of the data entity.
• User Entity: Logistics companies will assess the risks of joining an IDS ecosystem. They
could provide requirements for data usage contracts as an input for the digital twin. These
requirements constitute their data sovereignty constraints.
The ISO/DIS 23247 standard also recommends cross-layer components to support data assurance,
security, and translation as part of the digital twin. In those regards, we make two assumptions. On a
first moment, the digital twin envisioned here will not receive input from real-world ERP systems but
only experimental data. Secondly, we will treat data translation and security only as part of the data
sovereignty requirements expressed in the networked IDS ontology. The following section assesses
state-of-the-art tools for implementing and deploying the IDS ecosystem digital twin.
4. Application requirements and tools for digital twin implementation
[11] provide an extensive list of requirements to help assess tools for the deployment and
implementation of digital twins. The following criteria are the most relevant for the digital twin
project of this research:
• Continuous integration and deployment: easy integration of changes from the observable
element into the digital twin, possibly avoiding inconsistencies;
• Domain expert involvement: easy to use by business analysts and IT architects operating the
digital twin without advanced knowledge about its technicalities;
• Modifiability: inclusion of new components, such as cross-layer security mechanisms,
graphical user interface, or enterprise data repositories;
• Platform interoperability: extension of the digital twin platform using value-adding services,
e.g., machine learning, simulation, or visualization;
• Provisioning: deployment of the digital twin in the cloud/edge computing for external scrutiny;
• Reusability: easy inclusion of external software components into the digital twin, e.g.,
ontology reasoners or machine learning algorithms;
• System interoperability: interaction between the digital twin and physical devices. In our
case, the observable elements will comprehend Logistics Operational Data coming from an
IDS-based Logistics ecosystem. Therefore, the tool should support integrating data from
external systems and applications, such as ERP systems and IoT-streamed data.
Table 1 summarizes an assessment of state-of-the-art tools for implementing the digital twin
envisioned by this research. We extended the evaluation framework proposed by [11] to analyze
Amazon Web Services, Arena, Microsoft Azure, Eclipse, LeanIX, Matlab, and Stardog regarding
complete, partial, or total absence of coverage of the requirements.
Table 1
Digital twin modeling tool assessment in conformity with essential requirements extracted from the
ISO/DIS 23247 and metrics proposed by [11]
Stardog
Matlab
Eclipse
LeanIX
Arena
Azure
AWS
Requirement Metric
Continuous Offers a complete CI/CD pipeline or
Integration integration.
Enables domain experts to model
Domain Expert physical devices, their types, and data
Involvement structured and provides a graphical
interface.
Allows modification of the DT during
Modifiability
runtime.
Provides standardized interfaces to
Platform retrieve data and structural information
Interoperability of DTs or make changes to the DTs for
external services.
The platform provides on-premise and
Provisioning
cloud-native solutions for the
deployment of the digital twin.
Offers custom and pre-built
Reusability
components that projects can reuse.
Provides support for defining
System connections between devices via their
interoperability DTs and automatically implementing
interactions based on this connection.
[11] referred to Amazon Web Service, Microsoft Azure, and Eclipse as the state-of-the-art tools
for digital twin implementation. Recent developments from Arena, LeanIX, Matlab, and Stardog
indicate an effort to adapt these tools to become options for digital twin modeling. However,
Microsoft Azure currently offers the best coverage for this research project's requirements,
specifically continuous integration, user involvement, and system interoperability.
5. Conclusions and future work
This paper provided a research plan to address semantic and organizational interoperability
problems that hinder the formation of data-sharing agreements in IDS ecosystems. Solving it demands
exploring and understanding Enterprise Interoperability problems, such as inter-organizational
process alignment and semantic reconciliation of data usage contracts. We proposed a preliminary
architecture of a digital twin to help business and IT architects assess the risks of joining IDS
ecosystems for their companies. We have also analyzed state-of-the-art tools for implementing the
digital twin, which partially responds to the technical questions derived from the main research
question. This research project has three immediate steps: (1) the design of the networked business
ontologies to describe an IDS ecosystem, data usage contracts, and (virtual) data connectors; (2)
consolidation of value models to describe organizational options to configure the IDS ecosystems (in
alignment with process coordination models for specifying the flow of sensitive data); and (3)
technical action research for promoting user involvement on validating the relevance of the
interoperability problems to simulate.
6. Acknowledgments
This research is financially supported by the Dutch Ministry of Economic Affairs and co-financed
via TKI DINALOG and NWO, under the grant Nº 439.19.633 (CLICKS project).
7. References
[1] P. Hummel, M. Braun, M. Tretter, P. Dabrock, Data sovereignty: A review, Big Data & Society
8 (2021) 1-17. doi: 10.1177/2053951720982012.
[2] B. Otto, M. Jarke, Designing a multi-sided data platform: findings from the International Data
Spaces case, Electronic Markets 29 (2019) 561-580. doi: 10.1007/s12525-019-00362-x.
[3] A. Braud, G. Fromentoux, B. Radier, O. Le Grand, The road to European digital sovereignty
with Gaia-X and IDSA, IEEE Network 35 (2021) 4-5. doi: 10.1109/MNET.2021.9387709.
[4] H. Bastiaansen, M. Kollenstart, S. Dalmolen, T. van Engers, User-centric network-model for data
control with interoperable legal data sharing artefacts: Improved data sovereignty, trust and
security for enhanced adoption in interorganizational and supply chain is applications, in:
Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems
(IS) for the Future, PACIS 2020, Dubai, United Arab Emirates, pp. 1-15.
[5] B. Otto, S. Steinbuß, A. Teuscher, S. Lohmann, International Data Spaces: Reference
Architecture Model Version 3, 2019. URL: https://www. internationaldataspaces. org/wp-
content/uploads/2019/03/IDS-Reference-Architecture-Model-3.0.pdf.
[6] V. Kalogirou, Y. Charalabidis, The European union landscape on interoperability
standardisation: status of European and national interoperability frameworks, in: K. Popplewell,
K. D. Thoben, T. Knothe, R. Poler (Eds.), Enterprise Interoperability VIII, Proceedings of the I-
ESA Conferences, vol 9, Springer, Cham, 2019, pp. 359-368. doi:10.1007/978-3-030-13693-
2_30.
[7] R. J. Wieringa, Design science methodology for information systems and software engineering,
Springer, Cham, 2014.
[8] G. N. Schroeder, C. Steinmetz, R. N. Rodrigues, R. V. B. Henriques, A. Rettberg, C. E. Pereira,
A Methodology for Digital Twin Modeling and Deployment for Industry 4.0, Proceedings of the
IEEE 109 (2020) 556-567. doi:10.1109/JPROC.2020.3032444.
[9] C. Atkinson, T. Kuhne, Taming the Complexity of Digital Twins, IEEE Software 39 (2021) 27-
32. doi:10.1109/MS.2021.3129174.
[10] G. Shao, Use Case Scenarios for Digital Twin Implementation Based on ISO 23247, 2021. URL:
https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932269.
[11] D. Lehner, J. Pfeiffer, E.-F. Tinsel, M. M. Strljic, S. Sint, M. Vierhauser, A. Wortmann, M.
Wimmer, Digital Twin Platforms: Requirements, Capabilities, and Future Prospects, IEEE
Software 39 (2021) 53-61. doi:10.1109/MS.2021.3133795.