# Database Querying under Missing Values Governed by Missingness Mechanisms

arXiv: https://arxiv.org/abs/2604.06520 · HTML: https://mlsanity.com/paper/2604.06520/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Builds on (most cited first, 6 of 6)

- [Graphical Models for Processing Missing Data](https://mlsanity.com/paper/1801.03583/) (2018)
- [OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport](https://mlsanity.com/paper/2403.02372/) (2024)
- [A Formal Framework For Probabilistic Unclean Databases](https://mlsanity.com/paper/1801.06750/) (2018)
- [Consistency and Trust in Peer Data Exchange Systems](https://mlsanity.com/paper/1606.01930/) (2016)
- [Querying Incomplete Numerical Data: Between Certain and Possible Answers](https://mlsanity.com/paper/2210.15395/) (2022)
- [Querying Incomplete Data : Complexity and Tractability via Datalog and First-Order Rewritings](https://mlsanity.com/paper/2310.12694/) (2023)

## Abstract (arXiv)

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.