Database Querying under Missing Values Governed by Missingness Mechanisms

Leopoldo Bertossi; Farouk Toumani; Maxime Buron

cs.DBApr 7, 2026

Database Querying under Missing Values Governed by Missingness Mechanisms

Authors: Leopoldo Bertossi, Farouk Toumani, Maxime Buron

Organizations: Carleton University, Ottawa, Canada & IMFD, Chile. · LIMOS, CNRS, UCA, France.

Abstract

We address the problems of giving a semantics to- and doing query answering (QA) on a relational database (RDB) that has missing values (MVs). The causes for the latter are governed by a Missingness Mechanism that is modelled as a Bayesian Network, which represents a Missingness Graph (MG) and involves the DB attributes. Our approach considerable departs from the treatment of RDBs with NULL (values). The MG together with the observed DB allow to build a block-independent probabilistic DB, on which basis we propose two QA techniques that jointly capture probabilistic uncertainty and statistical plausibility of the implicit imputation of MVs. We obtain complexity results that characterize the computational feasibility of those approaches.

Figures & tables

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 3: (a) Missingness graph. (b) Resulting BID.

Figure 8: The MG

\mathcal{M}

Relational Databases

Relational databases store structured data in tables linked by keys, enabling reliable querying, updates, and analysis with SQL.

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Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values. In many real-world datasets, however, missingness may arise from two distinct sources: some entries are meaningfully missing (intrinsically absent and semantically valid), while others are missing due to the observation process and should be imputed. We formalize this distinction as a selective imputation problem, where the goal is to jointly infer which missing entries should be preserved and which should be recovered. To address this challenge, we propose Diff-Joint, a diffusion-based framework that jointly models tabular data together with a latent missingness mask. The method alternates between conditional sampling and uncertainty-aware aggregation to iteratively refine both imputed values and missingness labels. Empirical results on synthetic and real-world datasets demonstrate that Diff-Joint effectively identifies meaningfully missing entries while achieving competitive imputation accuracy and improved downstream task performance.

Abstract

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