Summary of A Theoretical Framework For Ai-driven Data Quality Monitoring in High-volume Data Environments, by Nikhil Bangad et al.
A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments
by Nikhil Bangad, Vivekananda Jayaram, Manjunatha Sughaturu Krishnappa, Amey Ram Banarse, Darshan Mohan Bidkar, Akshay Nagpal, Vidyasagar Parlapalli
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel AI-powered framework is proposed to tackle the challenges of maintaining data quality in high-volume big data environments. The framework leverages advanced machine learning techniques to address scalability, velocity, and variety limitations of traditional methods. Key components include intelligent data ingestion, adaptive preprocessing, context-aware feature extraction, and AI-based quality assessment modules. A continuous learning paradigm ensures adaptability to evolving data patterns and quality requirements. While practical results are not provided, the theoretical foundation laid out in this paper advances data quality management and encourages exploration of AI-driven solutions in dynamic environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to make sure big data is accurate and reliable. They wanted to solve the problem of dealing with huge amounts of data that come from many different sources. Their approach uses special machine learning techniques to identify and fix problems with the data. The system can learn as it goes, so it gets better at finding mistakes over time. This paper doesn’t show how well the system works in practice, but it provides a solid foundation for future research and development of AI-powered tools for managing big data. |
Keywords
» Artificial intelligence » Feature extraction » Machine learning
