Kuhn M, Kaminski E, Franke J (2022)
Publication Type: Conference contribution
Publication year: 2022
Publisher: Elsevier B.V.
Book Volume: 112
Pages Range: 250-255
Conference Proceedings Title: Procedia CIRP
DOI: 10.1016/j.procir.2022.09.080
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.
APA:
Kuhn, M., Kaminski, E., & Franke, J. (2022). Track and Trace: Integrating static and dynamic data in a hybrid graph-based traceability model. In Roberto Teti, Doriana D'Addona (Eds.), Procedia CIRP (pp. 250-255). Naples, IT: Elsevier B.V..
MLA:
Kuhn, Marlene, Erik Kaminski, and Jörg Franke. "Track and Trace: Integrating static and dynamic data in a hybrid graph-based traceability model." Proceedings of the 15th CIRP Conference on Intelligent Computation in Manufacturing Engineering, ICME 2021, Naples Ed. Roberto Teti, Doriana D'Addona, Elsevier B.V., 2022. 250-255.
BibTeX: Download