Service-oriented architectures (SOA) are becoming more widespread in the context of Industry 4.0, and their interface descriptions enable modular and scalable communication systems. Since syntactic checks such as data types are solved nowadays, the purpose of this work is to add semantic validation based on the idea of Semantic Web Services. This paper proposes Lightweight Semantic Web Services for Units (LISSU) and integrates promising concepts from the Semantic Web into SOA. We complement existing syntactic checks with semantic ones (e.g. for units), extend one-time initial checks with continuous monitoring, and include expressive constrain-based validations. LISSU can be integrated into any SOA and significantly increases the predictability of communications. Before components communicate, it checks their semantics via ontology URIs and automatically converts units if possible. Continuous monitoring at runtime extracts sent messages and guarantees flawless data quality via constraint-based validations. A real-world demonstrator setup in the manufacturing domain proves effectiveness and practicality. We present LISSU, which integrates concepts from the Semantic Web into SOA setups. It enables a wide range of semantic validations before and during communication, thereby increasing the quality and predictability of SOA communication.

Due to their growing amount and heterogeneity, we need a precise and standardized understanding about the foundation, structure, and forms of aggregation and especially the use of data and models within the production domain. Our aim is to investigate how to model data elements and static and dynamic relationships as well as their physical resources in the IoP, in a cross-disciplinary life cycle spanning cooperation as a basis for information management, meeting all technical, scientific-ethical, and legal framework conditions. The core solution for this challenge is the use of an adequate set of modeling techniques, transformations, and their integration with digital shadows. This chapter provides a deep insight into relevant concepts that constitute a digital shadow, link it to their semantics defined by appropriate metamodels, and discuss the data and models a digital shadow consists of in four use cases. We show a method to derive digital shadows and introduce their life cycle in relation to the product life cycle. These concepts are the foundation for data and model sharing within digital shadows applicable for worldwide production labs.

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