AI-Driven Frameworks for Enhancing Data Quality in Big Data Ecosystems: Error_Detection, Correction, and Metadata Integration

• URL: http://arxiv.org/abs/2405.03870v1
• Date: Mon, 6 May 2024 21:36:45 GMT
• Title: AI-Driven Frameworks for Enhancing Data Quality in Big Data Ecosystems: Error_Detection, Correction, and Metadata Integration
• Authors: Widad Elouataoui,
• Abstract summary: This 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. Thirdly, we present a generic framework for detecting various quality anomalies using AI models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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