Implementing CPSLint: A Data Validation and Sanitisation Tool for Industrial Cyber-Physical Systems

TL;DR

CPSLint is a domain-specific language and tool designed to simplify and standardize data validation and sanitization for large, unstructured time-series datasets in industrial cyber-physical systems.

Contribution

The paper introduces CPSLint, a DSL that enables both data scientists and domain experts to efficiently perform data preparation tasks for industrial CPS data.

Findings

CPSLint reduces code complexity for data sanitization tasks.

The tool improves readability and reusability of data preparation scripts.

CPSLint is publicly available and applicable to various time-series data in CPS.

Abstract

Raw datasets are often too large and unstructured to work with directly, and require a data preparation phase. The domain of industrial Cyber-Physical Systems (CPSs) is no exception, as raw data typically consists of large time-series data collections that log the system's status at regular time intervals. The processing of such raw data is often carried out using ad hoc, case-specific, one-off Python scripts, often neglecting aspects of readability, reusability, and maintainability. In practice, this can cause professionals such as data scientists to write similar data preparation scripts for each case, requiring them to do much repetitive work. We introduce CPSLint, a Domain-Specific Language (DSL) designed to support the data preparation process for industrial CPS. CPSLint raises the level of abstraction to the point where both data scientists and domain experts can perform the data preparation task. We leverage the fact that many raw data collections in the industrial CPS domain require similar actions to render them suitable for data-centric workflows. In our DSL one can express the data preparation process in just a few lines of code. CPSLint is a publicly available tool applicable for any case involving time-series data collections in need of sanitisation.