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Flexible OPC UA Data Load Optimizations on the Edge of Production

: Lipp, J.; Rath, M.; Rudack, M.; Vroomen, U.; Bührig-Polaczek, A.


Filipe, J.:
Enterprise Information Systems. 22nd International Conference, ICEIS 2020 : Virtual Event, May 5-7, 2020, Revised Selected Papers
Cham: Springer Nature, 2021 (Lecture Notes in Business Information Processing 417)
ISBN: 978-3-030-75417-4 (Print)
ISBN: 978-3-030-75418-1 (Online)
ISBN: 978-3-030-75419-8
International Conference on Enterprise Information Systems (ICEIS) <22, 2020, Online>
Fraunhofer FIT ()

Recent trends like the (Industrial) Internet of Things and Industry 4.0 lead to highly integrated machines and thus to greater challenges in dealing with data, mostly with respect to its volume and velocity. It is impossible to collect all data available, both at maximum breadth (number of values) and maximum depth (frequency and precision). The goal is to achieve an optimal trade-off between bandwidth utilization versus information transmitted. This requires optimized data collection strategies, which can extensively profit from involving the domain expert’s knowledge about the process.
In this paper, we build on our previously presented optimized data load methods, that leverage process-driven data collection. These enable data providers (i) to split their production process into phases, (ii) for each phase to precisely define what data to collect and how and (iiii) to model transitions between phases via a data-driven method. This paper extends the previous approach in both breadth and depth and focuses especially on making its benefits, like the demonstrated 39% savings in bandwidth, to domain experts. We propose a novel, user-friendly assistant that enables domain experts to define, deploy and maintain a flexible data integration pipeline from the edge of production to the cloud.