Schnizer, P.; Sulaiman Khail, W.; Rehm, G.: Use cases for consistent robust processing of data models. In: Kent Wootton ... [Ed.] : ICALEPCS 2025 : Proceedings of the 20th International Conference on Accelerator and Large Experimental Physics Control Systems : Argonne National Laboratory in Chicago, IL, United States, from 20–26 September, 2025Geneve: JACoW, 2025. - ISBN 978-3-95450-255-4, p. WEMG012/763-766
10.18429/JACoW-ICALEPCS2025-WEMG012
Open Access Version
Abstract:
Many control algorithms or optimisation procedures profit from a consistent set of data which is available with a high frequency: e.g. machine learning or automated commissioning. Modern distributed control systems allow combining and presenting data based on data models, which are then transported consistently over the network: e.g. EPICS7 introduced these data models as normative types or their combination. The authors present use cases that can profit from a consistent robust combination of data sub-models of many devices to a higher order model. Finally common patterns are presented which could be reasonable to implement independently.