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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