CPSLint: A Domain-Specific Language Enabling Cyber-Physical Systems Dataset Validation and Sanitisation
Abstract
Raw datasets are often too large and unstructured to work with directly, necessitating a data
preparation process. The domain of industrial Cyber-Physical Systems (CPSs) is no exception, as raw
data typically consists of large time-series datasets logging the system's status by regular time
intervals. We introduce CPSLint, a Domain-Specific Language (DSL) designed to enable the data
preparation process for industrial CPS. We leverage the fact that many raw datasets in the CPS
domain require similar actions to render them suitable for Machine-Learning (ML) solution workflows,
e.g., Fault Detection and Identification (FDI) workflows.
CPSLint's main features include enforcing constraints through validation and remediation for data
columns, e.g., imputing missing data from surrounding rows, as well as statistical insights. These
insights will provide user friendly analytic output such as plots. The more advanced features cover
inference of extra CPS-specific data structures, both column-wise and row-wise. For instance,
descriptive execution phases as an effective method of data compartmentalisation are extracted and
prepared for ML-assisted FDI workflows.
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