CPSLint
Abstract
Industrial cyber-physical systems generate vast amounts of semi-structured time-series
data that require careful preprocessing before they can be effectively used for machine
learning applications such as fault detection and identification. Raw sensor datasets
are often corrupted or incomplete, making it challenging to develop reliable solutions
without proper data preparation and validation. In this paper, we introduce CPSLint, a
domain-specific language for data validation and sanitisation. We present the design,
implementation and evaluation of CPSLint, demonstrating its ability to automatically
detect and correct common data corruption patterns while enabling non-programming domain
experts to effectively prepare their data for analysis.
We report evaluation results on a representative dataset, tracking memory consumption
and CPU-time for sanitisation activities. Our approach offers several advantages over
traditional methods, including reduced manual effort, guaranteed consistency and broader
applicability across time-series datasets and projects.
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