Track and Trace: Integrating static and dynamic data in a hybrid graph-based traceability model

Kuhn M, Kaminski E, Franke J (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Procedia CIRP Curran

Publisher: Elsevier B.V.

Book Volume: 112

Pages Range: 250-255

Conference Proceedings Title: Procedia CIRP

Event location: Naples IT

DOI: 10.1016/j.procir.2022.09.080

Abstract

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.

Authors with CRIS profile

Marlene Kuhn Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Erik Kaminski Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik

How to cite

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.

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