  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.
