Missing data is a widespread problem in many domains, creating challenges in
data analysis and decision making. Traditional techniques for dealing with
missing data, such as excluding incomplete records or imputing simple estimates
(e.g., mean), are computationally efficient but may introduce bias and disrupt
variable relationships, leading to inaccurate analyses. Model-based imputation
techniques offer a more robust solution that preserves the variability and
relationships in the data, but they demand significantly more computation time,
limiting their applicability to small datasets.
This work enables efficient, high-quality, and scalable data imputation
within a database system using the widely used MICE method. We adapt this
method to exploit computation sharing and a ring abstraction for faster model
training. To impute both continuous and categorical values, we develop
techniques for in-database learning of stochastic linear regression and
Gaussian discriminant analysis models. Our MICE implementations in PostgreSQL
and DuckDB outperform alternative MICE implementations and model-based
imputation techniques by up to two orders of magnitude in terms of computation
time, while maintaining high imputation quality.

通过在数据库系统中使用 MICE 方法，本研究实现了高效、高质量和可扩展的数据填充，充分利用计算共享和环形抽象技术以加快模型训练并在 PostgreSQL 和 DuckDB 中取得了比其他 MICE 实现和基于模型填充技术快两个数量级的计算时间，同时保持了高水平的填充质量。