Missing Value Imputation in Tabular Data Lakes Unleashed: A Hybrid Approach

Abstract Missing values in tabular data lakes can severely impact data analysis and diminish the performance in downstream applications. We highlight that a robust imputation strategy should properly take three aspects of variety into consideration: source of imputed value, the types of tables involved, and the data types of the missing value. Existing imputation methods rely on estimation-based approaches (using a model trained on data from the same table to estimate missing values) or search-based approaches (retrieving values from other tables). Unfortunately, none of these approaches effectively incorporate all three aspects of variety. To address this gap, we propose , a novel framework that uses a C ombination of E stimation-based and S earch-based methods for missing value I mputation in D ata lakes. contains three core modules: (1) the , which efficiently discovers candidate values from tables by exploiting the contextual information; (2) the , which introduces an influence function and a sampling-based exploration strategy to yield accurate estimated values; (3) the , which determines the most suitable method based on table-level and column-level statistics. Extensive experiments conducted on three data lakes demonstrate that effectively and efficiently addresses the missing value problem.