Traceable Intercorporation Data Exchange and Processing using a Graph Based Infrastructure

Abstract

Designing an aircraft requires multidisciplinary analysis and data processing abilities which often are spread over various partners. Effective collaboration across organisational boundaries is difficult, but essential. As the aerospace industry becomes increasingly digitalised, ever larger volumes of data and models must be exchanged. Heterogeneous tools, data formats, and infrastructures make it difficult to exchange data and to trace it. We propose using semantic graphs for data exchange to ensure interoperability, while semantic links between data models facilitate multidisciplinary and cross-organisational collaboration. Furthermore, our approach captures comprehensive metadata that describes the creation and modification of each dataset, thereby establishing a fully traceable data provenance chain. We demonstrate its functionality by a design process for an electromechanical actuator (EMA) given requirements from a different stakeholder (simulated). Having the requirements and the EMA models translated in Resource Description Framework (RDF) graphs, we are able to create links between them. This then enables the EMA model to be automatically re-evaluated when requirements change, ensuring that it complies with them. For the data exchange, we use the DLR SemanticHub, which utilizes a graph database. By providing traceability of the data results provided in different data formats and the data origins, we enable transparency and accountability across organisational boundaries, which is important for trusted collaboration and compliance in intercorporational data exchange.