Data Quality as a Service Framework for AI-Enabled Industrial Internet of Things

In the era of the Industrial Internet of Things (IIoT), the integration of Artificial Intelligence (AI) and Machine Learning (ML) into industrial processes has significantly enhanced operational efficiency and decision-making capabilities.However, the effectiveness of AI-enabled IIoT systems heavily depends on the quality of the underlying data.Ensuring data integrity, accuracy, and reliability in IIoT is challenging due to the heterogeneous nature of data sources and the dynamic operational conditions.Existing approaches often focus on individual data quality techniques, lacking a unified and comprehensive framework.To address this need, we propose Data Quality as a Service (DQaaS), a novel framework designed to provide scalable and reusable data quality solutions as services for AI-enabled IIoT applications.DQaaS encompasses a suite of modular services for data monitoring and repair, all orchestrated through a centralized platform.This framework leverages advanced ML algorithms and state-of-the-art processing techniques to ensure robust data quality management across the edge-cloud continuum.