A Digital Twin System forms the basis for comprehensive digitization of production and logistics by gradually creating consistent data homogeneity and interoperability.

Any asset — it can be an actual physical asset like a drilling machine but also something virtual like a web service — has a digital representation with consistent semantics, called a Digital Twin. Digital Twins represent those assets and services and provide a shell for their Aspects which define the functionality the Digital Twin supports. Aspects provide the actual data of the Digital Twin that clients can use. Think of an Aspect as a live thing, i.e., a service that can be queried to retrieve current or aggregated data. The data can be anything from a single numeric value — e.g., a temperature sensor — to a complex block of data.

Each Aspect references a concrete Aspect Model. This model describes in a formal, i.e., machine readable, format, how an Aspect is structured. The model describes for example the used physical unit and possible value range of the temperature sensor. Note that even in this simple example, a conventional approach could not satisfyingly answer the question of where this kind of information would otherwise live — in the sensor’s data sheet that is only a human-readable document? In the Aspect’s API documentation that is also only human-readable? Should the information about the sensor’s physical range be hard-coded in the Aspect and included in each service reply, even though it never changes? By expressing it in the Aspect Model, the Aspect’s semantics can be made available to consumers of the data in a way that opens up new possibilities.

Therefore, an Aspect Model contains both information about runtime data structure (e.g., that there is a property in the data called "temperature", and that it has a numeric value) and information that is not part of the runtime data (e.g., the unit or range). It does not, however, contain actual runtime data (e.g., a numeric value representing the current temperature), as this will be delivered by an Aspect conforming to this Aspect Model. The combination of raw runtime data and its corresponding Aspect Model yields information.

To specify an Aspect Model, the Semantic Aspect Meta Model (SAMM) is used. This document specifies the Semantic Aspect Meta Model.

The Meta Model is specified using the Resource Description Format (RDF, [rdf11]) and the Terse RDF Triple Language syntax (TTL, [turtle]), together with validation rules in the Shapes Constraint Language (SHACL, [shacl]). Aspect Models are likewise specified in RDF/Turtle, following SAMM semantics.