CPSLint: A Domain-Specific Language Providing Data Validation and Sanitisation for Industrial Cyber-Physical Systems

TL;DR

CPSLint is a domain-specific language designed to automate data validation and sanitisation in industrial cyber-physical systems, improving data quality for machine learning applications.

Contribution

The paper introduces CPSLint, a novel language that enables non-programming experts to efficiently validate and correct time-series data in industrial settings.

Findings

CPSLint effectively detects and corrects common data corruption patterns.

Evaluation shows reduced manual effort and consistent data sanitisation.

The approach is applicable across various time-series datasets.

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.