Digital Twin is a digital replica of a physical product, process, or system, with the objective of unifying the physical and digital worlds. Digital Twin leverages heterogeneous and structured big data, algorithms for inference as well as software and communication interfaces.

• Design: Massive operating and environmental sensors as well as actuators and models.
• Communication: Bidirectional real connectivity physical process / digital platform.
• Aggregation: Data storage in repositories and preprocessing.
• Analysis: Algorithmic findings, recommendations and decision making.
• Inference: Representation the findings about notifications, visualizations and dashboards.
• Sensing and Action: Signal feedback for actuating an adjustment in the physical process.

Building of Digital Twins is based on a structural information modelling. Heterogeneous data necessitate usage of semantics and ontology to fixing a standard vocabulary or language, hence, in particular enabling interoperability and reuse of information in various domains. By defining the structure of the ontology, we define the purpose and scope of information model. During domain knowledge capture, we conceptualized and formalized the ontologies and aligned them with existing ones. The information model comprises, in particular from a formal description of the physical assets, mappings to database schemas of existing systems, and a formalization of domain-related knowledge of processes.