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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