Track and Trace: Integrating static and dynamic data in a hybrid graph-based traceability model - Kuhn, Marlene; Kaminski, Erik; Franke, Jörg (Procedia CIRP, 2022)
In smart manufacturing, an increasing amount of heterogeneous data inputs need to be integrated into a holistic traceability model. One key challenge is the coherent mapping of dynamic and event-based sensor data for real-time tracking purposes with static data, which are mostly needed for retrospective tracing purposes. In this research, we develop a graph-based traceability model, which integrates static and dynamic data sub-models in one connected traceability graph. For an automotive use case, the novel traceability model is implemented and tested using a hybrid architecture based on the graph database Neo4j and the event engine Apache Kafka.
CRIS-Team Scopus Importer:2022-12-09
Keywords: data model; graph; ICT architecture; smart manufacturing; traceability