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.
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.
[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: (
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: (
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.
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: (
• 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 (
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: (
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. • 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.
[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.
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: (
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). [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 IESA Conferences, vol 9, Springer, Cham, 2019, pp. 359-368. doi:10.1007/978-3-030-136932_30. [7] R. J. Wieringa, Design science methodology for information systems and software engineering,
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