Paper ID: 2410.08576 • Published Oct 11, 2024
A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments
Nikhil Bangad, Vivekananda Jayaram, Manjunatha Sughaturu Krishnappa, Amey Ram Banarse, Darshan Mohan Bidkar, Akshay Nagpal...
TL;DR
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This paper presents a theoretical framework for an AI-driven data quality
monitoring system designed to address the challenges of maintaining data
quality in high-volume environments. We examine the limitations of traditional
methods in managing the scale, velocity, and variety of big data and propose a
conceptual approach leveraging advanced machine learning techniques. Our
framework outlines a system architecture that incorporates anomaly detection,
classification, and predictive analytics for real-time, scalable data quality
management. Key components include an intelligent data ingestion layer,
adaptive preprocessing mechanisms, context-aware feature extraction, and
AI-based quality assessment modules. A continuous learning paradigm is central
to our framework, ensuring adaptability to evolving data patterns and quality
requirements. We also address implications for scalability, privacy, and
integration within existing data ecosystems. While practical results are not
provided, it lays a robust theoretical foundation for future research and
implementations, advancing data quality management and encouraging the
exploration of AI-driven solutions in dynamic environments.