用AI构建可自适应的数据质量监控框架，应对海量数据挑战
A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments
- 基于机器学习设计实时、可扩展的数据质量监控架构
- 融合异常检测与预测分析，实现动态质量评估
- 适合研究数据治理与智能运维的工程师与学者
本文提出一种AI驱动的数据质量监控理论框架，旨在应对高容量数据环境中数据质量维护的挑战。传统方法在数据规模、速度和多样性方面存在局限，本文通过引入先进机器学习技术，构建包含异常检测、分类与预测分析的系统架构。核心组件包括智能数据摄入层、自适应预处理机制、上下文感知特征提取及AI质量评估模块。框架以持续学习为核心，确保对不断变化的数据模式和质量需求的适应性。同时探讨了可扩展性、隐私保护及与现有数据生态系统的集成问题。虽未提供实证结果，但为未来研究与实践奠定了坚实的理论基础，推动了动态环境下数据质量管理的发展。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。