[论文解读] AI-Driven Frameworks for Enhancing Data Quality in Big Data Ecosystems: Error_Detection, Correction, and Metadata Integration

Widad Elouataoui|arXiv (Cornell University)|May 6, 2024

Big Data and Business Intelligence被引用 6

一句话总结

本博士论文提出以人工智能为驱动的框架，用于在大数据生态系统中检测、纠正并整合元数据以提升数据质量，包括新的度量标准、异常检测和纠正建模。

ABSTRACT

The widespread adoption of big data has ushered in a new era of data-driven decision-making, transforming numerous industries and sectors. However, the efficacy of these decisions hinges on the quality of the underlying data. Poor data quality can result in inaccurate analyses and deceptive conclusions. Managing the vast volume, velocity, and variety of data sources presents significant challenges, heightening the importance of addressing big data quality issues. While there has been increased attention from both academia and industry, current approaches often lack comprehensiveness and universality. They tend to focus on limited metrics, neglecting other dimensions of data quality. Moreover, existing methods are often context-specific, limiting their applicability across different domains. There is a clear need for intelligent, automated approaches leveraging artificial intelligence (AI) for advanced data quality corrections. To bridge these gaps, this Ph.D. thesis proposes a novel set of interconnected frameworks aimed at enhancing big data quality comprehensively. Firstly, we introduce new quality metrics and a weighted scoring system for precise data quality assessment. Secondly, we present a generic framework for detecting various quality anomalies using AI models. Thirdly, we propose an innovative framework for correcting detected anomalies through predictive modeling. Additionally, we address metadata quality enhancement within big data ecosystems. These frameworks are rigorously tested on diverse datasets, demonstrating their efficacy in improving big data quality. Finally, the thesis concludes with insights and suggestions for future research directions.

研究动机与目标

• 在多指标和通用性方面弥补现有大数据质量方法的不足。
• 引入新的质量度量指标和加权评分系统以实现精确评估。
• 提出用于检测质量异常的通用 AI 驱动框架。
• 开发基于预测模型的框架，以纠正检测到的异常。
• 在大数据生态系统中提升元数据质量。

提出的方法

• 开发新的质量度量指标和加权评分模型用于数据质量评估。
• 设计用于多样化质量问题的通用 AI 驱动异常检测框架。
• 开发基于预测模型的检测到异常的纠正框架。
• 将元数据质量提升机制整合到大数据管道中。
• 在多样化数据集上验证框架以证明可行性。

实验结果

研究问题

• RQ1 如何通过一套通用的质量度量指标和加权评分系统来提升大数据质量评估？
• RQ2 基于 AI 的异常检测是否能够在不同数据源中可靠地识别质量问题？
• RQ3 预测模型在纠正检测到的质量异常方面有多有效？
• RQ4 如何在大数据生态系统中评估和整合元数据质量？
• RQ5 应用这些 AI 驱动框架的实际指南和局限性是什么？

主要发现

• 引入一组新的数据质量度量指标和加权评分系统。
• 提出一个可跨领域使用的通用 AI 驱动异常检测框架。
• 提出一个基于预测模型的检测到的异常纠正框架。
• 解决大数据生态系统中元数据质量提升的问题。
• 在多样化数据集上证明这些框架的可行性和潜在有效性。