# Structuring Reference Architectures for the Industrial Internet of Things
## 4 Selecting the Reference Architectures
### Reference Architecture Alignment Process  
Relying on this knowledge graph, we align the most popular IIoT reference architectures regarding their overlaps and unique features (see Figure 1). We supply a qualitative comparison complemented with configurable visualizations. System architects can use the visualizations to find according proposals related to their problems. Creators of architectures can find corresponding specifications from different initiatives. Decision makers can use the knowledge graph to get overview on covered domains. We compare the different reference _frameworks_ by visualizing the respective concerns of their _classifications_ (see Figures 8-10) and the relations (alignments) between them. Therefore, a comprehensive set of relevant and suited concerns needs to be formulated. We reach this list by extracting explicitly stated topics and challenges in the collected reference publications. The core set of concerns is additionally based on the Unified Requirements list collected by the IoT-A project [30], extended by our analysis of the various architecture descriptions and other publications of the IIoT domain in general. While some concerns are essential for most architecture descriptions, like describing interoperability between devices, services and applications, others are only relevant in a specific context, for example, the ability to have clear data provenance across a system architecture or only specified by a single source, for example, the ability to control the transmitting power in wireless networks. A mentioned concern is added to the list if either more than one architecture description specifies its relevance or if it is proposed as a crucial concern by at least one prominent publication. A complete spreadsheet with all concerns, explanations and references is publicly available ([[https://github.com/sebbader/architecturecomparison/blob/mast](https://github.com/sebbader/architecturecomparison/blob/mast)]([https://github.com/sebbader/architecturecomparison/blob/mast](https://github.com/sebbader/architecturecomparison/blob/mast)) er/IoT-A%20 Requirements.xlsx). Each selected IIoT reference framework was analyzed according to its coverage of each concern. Their mentioned categories are stored as entities of the classification class and annotated with additional meta information. For instance, short textual descriptions, links to their official resources and, if existing, links pointing to DBpedia are added. DBpedia is the open knowledge graph representing the structured information in Wikipedia and plays a central role for the Linked Open Data Cloud ([[https://lod-cloud.net/](https://lod-cloud.net/)]([https://lod-cloud.net/](https://lod-cloud.net/))). Therefore, additional look-ups and the discovery for further information for all entities is directly provided (see Figure 2). We link a classification or framework to a concern if there is at least some coverage of the related aspects of the respective concern (lowest common denominator approach). The alignment enables the traversal of links in the graph. Starting from one view, related specifications and definitions can be discovered by following the links to other frameworks with related foci. Classifications with nearly no alignment relations may present an unique perspective on the IIoT whereas highly connected nodes most probably address fundamental and widely regarded topics. Both cases contain valuable insights and are presented in Sections 5 and 7. The explained relations between the frameworks presented in Section 5 are represented as formal links between the respective classification nodes in the knowledge graph.Classifications with high degrees of incoming or outgoing links indicate a broader coverage, for instance the IIRA Information domain (center) and the RAMI Functional Layer (bottom). More focused and target-oriented classifications, like the IVI Activity View (bottom left) are reflected by less connections.