Towards Flexible Evolution of Digital Twins with Fluent APIs

With the increase of technologies such as the Internet of Things (IoT) and Cyber-Physical Systems, a huge amount of data is generated by current systems. To gain insights from this data, it must be combined with meta-information about its origins. Therefore, Digital Twins (DTs), as a common representation of a system and its data, are currently gaining traction in both industry and academia. However, these DTs have of course to be evolvable in order to reflect the high need of flexibility of the systems to support extensions, adaptations, customizations, etc. Evolving the DT representations currently not only involves a lot of manual effort, but might also lead to loss of data if not done correctly. To provide dedicated evolution support, we propose a dedicated framework for realizing evolution strategies between the schema, instance, and data level of a DT. In particular, we present a fluent API which allows the flexible but systematic manipulation of DTs during runtime and demonstrate its usage for a use case.