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MIM8: Local Digital Twins

This is the page describing OASC's MIM8 on Local Digital Twins (LDTs)

Description

This MIM describes interoperability in terms of the application domain of Local Digital Twins. In support of describing minimal interoperability mechanisms in this domain the MIM8 working group put forward a minimal definition of Local (or Urban) Digital Twins. This does not preclude any other definitions to be valid, and we recognise that depending on applications features may vary.

A Local (or Urban) Digital Twin is a digital representation of physical assets, systems, or processes in a defined local context (e.g., city, district, building, industry, port, airport). It leverages either on historical data, near real-time data, or real-time data enables visualization, analysis, simulation and reasoning for supporting decision-making.

The working group understands the diversity of local digital twins depending on their functionalities. Below is a table of different functionalities of a Local (or Urban) Digital Twin can have and as such are commonly categorised as: Awareness LDT, Experimental LDT, Predictive LDT, and Intelligent LDT (with human supervision).

Category of LDT
Visualisation
Simulation
Analytics*
Autonomous

Awareness

X

Experimental

X

X

Predictive

X

X

X

Intelligent

X

X

X

X

*Analytics can include 'diagnostic', 'descriptive', 'predictive', and 'prescriptive' capabilities.

While we align with commonly agreed definitions (such as the one above), communities have expressed that simulations require more complex work process than analytics, and hence, experimental digital twins are viewed more complex as predictive LDTs.

We discern the following additional functionalities across the following layers (L):

1
Data acquisition (historic/near real-time/real-time data)

  • sensing

  • extraction

  • synthetic data generation

2
Connectivity

  • APIs

  • protocols

  • file sharing

3
Data pre-processing

  • semantics

  • interoperability

  • aggregation

4
Analysis & simulation

  • AI Models

  • Physics-based simulations

5
Communication of results (visualisation)

  • Dashboard

  • Multi-dimensional visualisation

  • natural language interface (like chatbot)

  • VR

6
Decision-making

  • prescription

  • support

  • automation

Literature

Deutsches Institut für Normung e. V. (2024). DIN SPEC 91607:2024-11 – Digitale Zwillinge für Städte und Kommunen [Digital twins for cities and municipalities]. DIN e. V https://dx.doi.org/10.31030/3575521

European Commission: Directorate-General for Communications Networks, Content and Technology, Robalo Correia, A., Sousa, M., Mulquin, M., Santos, F. et al., Mapping EU-based LDT providers and users, European Commission, 2023, https://data.europa.eu/doi/10.2759/547098

European Telecommunications Standards Institute. (2022). ETSI GR CIM 017 V1.1.1 (2022-12) – Context Information Management (CIM); Feasibility of NGSI-LD for Digital Twins [Group Report]. ETSI. https://cdn.standards.iteh.ai/samples/59463/1f99a12b505b412d85a19ebfa4d4d451/ETSI-GR-CIM-017-V1.1.1-2022-12-.pdf

Gil, J., Petrova-Antonova, D., & Kemp, G. J. (2024). Redefining urban digital twins for the federated data spaces ecosystem: A perspective. Environment and Planning B: Urban Analytics and City Science, 0(0). https://doi.org/10.1177/23998083241302578

Tartia, J., & Hämäläinen, M. (2024). Co-creation processes and urban digital twins in sustainable and smart urban district development – Case Kera district in Espoo, Finland. Open Research Europe, 4, 130. https://doi.org/10.12688/openreseurope.17791.1

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