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S. Kropatschek, T. Steuer, E. Kiesling, K. Meixner, T. Frühwirth, P. Sommer, D. Schachinger, S. Biffl:
"Towards the Representation of Cross-Domain Quality Knowledge for Efficient Data Analytics";
Talk: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Västerås, Sweden; 2021-09-07 - 2021-09-10; in: "Proceedings 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)", (2021), ISBN: 978-1-6654-2478-3.

In Cyber-physical Production System (CPPS) engineering, data analysts and domain experts collaborate to identify
likely causes for quality issues. Industry 4.0 production assets
can provide a wealth of data for analysis, making it difficult to
identify the most relevant data. Because data analysts typically do
not posses detailed knowledge of the production process, a key
challenge is to discover potential causes that impact product quality with various experts, as knowledge about production processes
is typically distributed across various domains. To address this,
we highlight the need for cross-domain modelling and outline an
approach for effective and efficient quality analysis. Specifically,
we introduce the Quality Dependency Graph (QDG) to represent
cross-domain knowledge dependencies for efficiently prioritizing
data sources. We evaluate the QDG in a feasibility study based
on a real-world use case in the automotive industry

In Cyber-physical Production System (CPPS) engineering, data analysts and domain experts collaborate to identify likely causes for quality issues. Industry 4.0 production assets can provide a wealth of data for analysis, making it difficult to identify th

http://dx.doi.org/10.1109/ETFA45728.2021.9613406