In modern manufacturing, data plays a crucial role in optimizing processes, enhancing efficiency, and enabling interoperability across different systems. However, data models in manufacturing are often heterogeneous, proprietary, and lack standardization, making data sharing and integration challenging. The FAIR principles [1] – Findability, Accessibility, Interoperability, and Reusability – provide a structured framework to improve data management and usage across various domains. Applying these principles to manufacturing data models can enhance data exchange, facilitate automation, and support decision-making in Industry 4.0. This thesis aims to investigate methods for FAIRifying manufacturing data models (especially with focus on the Digital Shadow Reference Model from the Cluster of Excellence Internet of Production [2]), addressing key challenges such as semantic alignment, metadata enrichment, and interoperability with existing standards.
Thesis Type |
|
Student |
Lukas Joisten |
Status |
Running |
Proposal on |
13/05/2025 11:00 am |
Proposal room |
Seminar room I5 6202 |
Presentation room |
Seminar room I5 6202 |
Supervisor(s) |
Stefan Decker |
Advisor(s) |
Johannes Theissen-Lipp |
Contact |
theissen-lipp@dbis.rwth-aachen.de |
Background
Models play a vital role in managing the vast amounts of data and growing complexity in the domains of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and the Internet of Production (IoP). One example is the Digital Shadow Reference Model, which provides a foundational metadata schema to link data and metadata within these environments.
However, while such models are crucial, they often fall short in adhering to the FAIR Principles. The Digital Shadow Reference Model is no exception to that. Its lack of full FAIRness leads to reduced data reusability and interoperability, ultimately resulting in inefficiencies and decreased productivity in data-driven systems.
Ensuring that models like the Digital Shadow Reference Model follow the FAIR Principles is essential for enabling researchers to find data quickly and easily, access it in a standardised manner, integrate it seamlessly in their own workflows and, last but not least, reuse it thanks to rich meta information associated with it.
Objectives
In this thesis, we will develop a FAIRification process for the Digital Shadow Reference Model. By that, we will add FAIRness to the model. Finally, we will generalise our findings to apply FAIRification to data reference models in the manufacturing domain. Thus, we will pursue the following objectives.
- FAIRify the digital shadow reference model
- Evaluate its FAIRness before and after FAIRification with different suitable FAIRness metrics
- Implement a reference implementation of the FAIR digital shadow reference model
- Try it in production with other researchers if possible and validate the FAIRness
- Generalise findings
- How can a data model be FAIRified
Tasks
- Perform an extensive literature research
- Research current FAIRification approaches
- Research available FAIRness metrics
- Look into applications of the digital shadow reference model
- FAIRify the digital shadow reference model
- Identify, apply, and evaluate different FAIRness metrics to the current digital shadow reference model
- Refine metric(s) if evaluation of FAIRness is too vague
- Make the model FAIR and track the steps towards a FAIR digital shadow reference model
- In consultation with the domain experts, prioritize the aspects that need to be made FAIR in the model
- Select appropriate identifiers, data-serialisation formats, communication protocols, etc.
- Enrich metadata schema wherever it is necessary to meet the requirements of the FAIR principles
- Identify, apply, and evaluate different FAIRness metrics to the current digital shadow reference model
- Implement and demonstrate the FAIRified model in a reasonable programming language
- To ensure that the conceptional FAIRification works out in practice do the implementation in parallel while the conceptualisation phase
- Generalise the findings by abstracting the steps towards a FAIR digital shadow reference model to apply them to any data model
[1] https://www.go-fair.org/fair-principles/
[2] https://doi.org/10.1007/978-3-030-98062-7_3-3