Valentina Golendukhina, Lisa Sonnleithner, Michael Felderer,
"Enhancing Data Quality in Large-Scale Software Systems for Industrial Automation (ESEC / FSE) 2023"
: 3rd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things at the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC / FSE), IEEE, New York, NY, United States, Seite(n) 5-8, 12-2023, ISBN: 979-8-4007-0378-2
Original Titel:
Enhancing Data Quality in Large-Scale Software Systems for Industrial Automation (ESEC / FSE) 2023
Sprache des Titels:
Englisch
Original Buchtitel:
3rd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things at the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC / FSE)
Original Kurzfassung:
Modern industrial systems have become highly automated and data-driven, generating large volumes of data through sophisticated machinery. However, the quality of the collected data is not always optimal, whereas monitoring data quality is challenging due to real-time data constraints. While significant research has been done on data validation of the exported and prepared data, there is no research on implementing data quality practices with programming languages and tools that directly interact with hardware in the domain of cyber-physical production systems (CPPSs), such as IEC 61499 and IEC 61131-3, i.e., software on level 1 of the automation pyramid. By examining a plant-building company, this short paper explores the challenges and opportunities for data quality management at L1 including knowledge transfer, data compression, and metadata formulation, and suggests possible data validation techniques.
Forschungs-