Enhancing Data Quality in Large-Scale Software Systems for Industrial Automation Article Swipe
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· 2023
· Open Access
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· DOI: https://doi.org/10.1145/3617573.3618028
· OA: W4389215057
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.
