CoDAQ: Congressional-Based Data Imputation for Aggregate Queries

Visual analytics are widely used across various domains, including business intelligence, healthcare, and e-commerce, to provide data-driven insights that enhance decision-making processes. These systems analyze large datasets to generate visualizations that help users identify trends, patterns, and actionable insights. These insights rely heavily on accurate and complete data. However, effectively addressing the challenges posed by missing data is crucial for ensuring the accuracy and reliability of data-driven insights and visualizations. Incomplete datasets often lead to reduced accuracy in visualizations, prompting users to adopt costly imputation strategies to clean the data. This paper presents an innovative approach, 'CoDAQ'-Congressional-Based Data Imputation for Aggregate Queries that explores the use of a stratified-based selection technique for imputation to handle missing values within a specified imputation budget effectively. CoDAQ strategically selects cells for imputation by analyzing missing data patterns across different groups, ensuring a structured and representative approach. This technique offers enhanced accuracy and consistency over baseline methods. Our findings illustrate how the proposed technique enhances the utilization of available data, improves the accuracy of visualizations, and reduces bias in analysis, ultimately increasing the performance of visual analytics.