A comprehensive XML-based dataset for digital twin design recommendations. This dataset is used in a framework to provide structured guidance and smart decision-making for Digital Twins design for cyber-physical systems (CPS) and IoT system implementation.
This repository contains a structured dataset for digital twin design recommendations, organized across four main XML files that provide comprehensive guidance for designing, implementing, and deploying digital twin systems. The framework covers architectural patterns, modeling approaches, implementation standards, and goal-oriented capabilities.
dt-design-recommendation-dataset/
├── README.md
├── dt-design-recommendation-dataset_v0.1.tar # The tar file of the dataset
└── dataset
├── goal_and_purpose_model_v0.1.xml # Capabilities, services and goals
├── architecture_model_v0.1.xml # Architecture choices, models, and patterns
├── formalisms_and_deployment_model_v0.1.xml # Modeling approaches, deployment and enabling technologies
└── standards_model_v0.1.xml # Standards and specifications
Purpose: Defines digital twin capabilities organized by service categories
Service Categories:
- Data Acquisition & Ingestion
- Data Streaming
- Data Transformation & Wrangling
- Real-time Processing
- Batch Processing
- Data Storage & Archive Services
- AI/ML Model Repository
- Simulation Model Repository
- Enterprise System Integration
- Engineering System Integration
- OT/IoT System Integration
- Digital Twin Integration
- API Services
- Edge AI & Intelligence
- Command & Control
- Machine Learning (ML)
- Artificial Intelligence (AI)
- Simulation capabilities
- Prediction and Analytics
- Orchestration and Workflow Management
Capability Attributes:
- Objective and benefits
- Implementation feasibility
- Impact assessment
- Cost-benefit ratio
- Scalability considerations
- Risk level
- Technological readiness
- Implementation tools
Purpose: Defines various digital twin architecture patterns and frameworks
Key Components:
- Reference Architecture Models: Platform Stack Architectural Framework, RAMI 4.0, IIRA, UAF, OPC UA, AAS, TOGAF, NIST Framework, ISO 23247, Digital Twin as a Service
- Multi-Dimensional Architectures: 5-Dimension and 8-Dimension digital twin models
- Specialized Architectures: Layered, Modular Service-Oriented, Federated, Standard-Based, Platform, Cloud Stack, and IoT-Based architectures
Evaluation Criteria:
- Real-time synchronization capabilities
- Physical-virtual fidelity
- Data integration capabilities
- Simulation support
- Security and privacy frameworks
- Cross-domain interoperability
- Lifecycle management
Purpose: Covers modeling methodologies, simulation approaches, and deployment considerations
Key Sections:
- System Nature: Discrete, Continuous, Hybrid systems
- States and Transitions: Timed State Automata, Statecharts, Petri Nets, DEVS
- Feedback and Control Loops: Bond Graphs, System Dynamics, Causal Block Diagrams
- Multi-Physics Modeling: FEA, CFD, Multi-Body Dynamics
- Randomness and Stochastic Behavior: Markov Chains, Stochastic Petri Nets, Queuing Theory
- Formal Methods: Model Checking, Theorem Proving, Temporal Logic, Process Algebras
- Hardware Components: Computation, Sensors, Actuators
- Communication: Wireless and Wired technologies
- Time Synchronization: NTP, PTP, TSN protocols
- Operating Systems: ROS, FreeRTOS
- Network Architectures: Various topologies and architectures
- Twinning Types: Different approaches to digital twin implementation
- Frameworks: Comprehensive twinning frameworks
- Data Integration: Methods and approaches for data handling
Purpose: Comprehensive collection of relevant standards and specifications
Standard Categories:
- Simulation Standards: FMI, HLA (IEEE 1516), DSEEP (IEEE 1730), DIS (IEEE 1278), SSP
- Modeling Standards: SysML (ISO/IEC 19514), Modelica, UML, BPMN, ArchiMate
- Physical Entities Standards: IEEE 1451, ISO 23247 series, IEC 61131, OPC UA (IEC 62541)
- Additional Categories: Virtual Entities, Data, Connection, Services, Architecture, and Engineering Standards
Standard Attributes:
- Purpose and application domain
- Interoperability level
- Implementation complexity
- Scalability characteristics
- Industry adoption status
- Systematic Literature Reviews: Structured taxonomy for digital twin research
- Comparative Analysis: Framework for comparing different approaches
- Gap Analysis: Identify areas requiring further research
- Architecture Selection: Choose appropriate architectural patterns
- Technology Stack Decisions: Select suitable tools and technologies
- Standards Compliance: Ensure adherence to relevant standards
- Capability Planning: Understand required capabilities and their complexity
- Risk Assessment: Evaluate implementation risks and mitigation strategies
- Resource Planning: Estimate required resources and expertise
- Implementation Guidance: Practical recommendations for development
- Tool Selection: Choose appropriate development tools and frameworks
- Integration Planning: Plan system integrations effectively
- Hierarchical Structure: Organized taxonomy with clear relationships
- Attribute-Rich Elements: Comprehensive metadata for each component
- Extensible Design: Easy to extend with new categories and elements
- Version Controlled: All files include version information
- Identify Your Domain: Determine your application domain and requirements
- Plan Capabilities: Define required capabilities using
goal_and_purpose_model_v0.1.xml - Select Architecture: Choose the most appropriate architecture from
architecture_model_v0.1.xml - Define Modeling Approach: Select modeling methodologies, formalisms and deployment tools and frameworks from
formalisms_and_deployment_model_v0.1.xml - Ensure Standards Compliance: Check relevant standards in
standards_model_v0.1.xml
Manufacturing Digital Twin Project
│
├── Required Capabilities (Objectives and goals)
│ ├── Data Services → Real-time Processing, Data Streaming
│ ├── Integration → OT/IoT System Integration
│ └── Intelligence → Prediction, Machine Learning
│
├── Architecture Selection
│ └── Reference Architecture Model → ISO 23247 or Platform Stack Framework
│
├── Formalisms and Modeling Tools
│ ├── System Nature → Hybrid (Discrete + Continuous)
│ ├── Tools → Simulink, Modelica, or AnyLogic
│ └── Communication → OPC UA + Industrial Ethernet
│
└── Standards Compliance
├── Simulation → FMI for model exchange
├── Physical Entities → ISO 23247 series
└── Communication → IEC 62541 (OPC UA)
We welcome contributions to improve and extend this framework:
- New Standards: Add emerging standards and specifications
- Updated Capabilities: Include new digital twin capabilities and goals
- Architecture Patterns: Contribute new architectural approaches
- Tool Evaluations: Provide assessments of new tools and technologies
- Maintain XML structure and naming conventions
- Include comprehensive attribute information
- Provide clear documentation for new elements
- Update version numbers appropriately
- Digital Twin Consortium Platform Stack Architectural Framework
- Digital Twin Capabilities Periodic Table User Guide
- Foundations of Multi-Paradigm Modelling for Cyber-Physical Systems
- The Engineering of Digital Twins
- Theory of modeling and simulation.
- IEEE, ISO, and IEC as referenced in the XML files
This project is intended for research and educational purposes. Please respect the intellectual property rights of referenced standards organizations and framework developers.
@misc{marah2025dtdesign,
title={Digital Twin Design Recommendation Dataset},
author={Marah, Hussein},
version={v0.1},
doi={https://doi.org/10.5281/zenodo.16729488},
year={2025}
}
For questions, suggestions, or collaboration opportunities, please reach out through the repository's issue tracker.
Version: 0.1
Last Updated: August 2, 2025
Maintained by: Hussein Marah