# BClean: A Bayesian Data Cleaning System

arXiv: https://arxiv.org/abs/2311.06517 · HTML: https://mlsanity.com/paper/2311.06517/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 67 references that are in the map.
- First posted on arXiv 11 Nov 2023.

## Builds on (most cited first, 13 of 13)

- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [High-dimensional learning of linear causal networks via inverse covariance estimation](https://mlsanity.com/paper/1311.3492/) (2013)
- [HoloClean: Holistic Data Repairs with Probabilistic Inference](https://mlsanity.com/paper/1702.00820/) (2017)
- [HoloDetect: Few-Shot Learning for Error Detection](https://mlsanity.com/paper/1904.02285/) (2019)
- [Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models](https://mlsanity.com/paper/1810.11905/) (2018)
- [Picket: Guarding Against Corrupted Data in Tabular Data during Learning and Inference](https://mlsanity.com/paper/2006.04730/) (2020)
- [Truth Finding on the Deep Web: Is the Problem Solved?](https://mlsanity.com/paper/1503.00303/) (2015)
- [PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming](https://mlsanity.com/paper/2007.11838/) (2020)
- [DAEMA: Denoising Autoencoder with Mask Attention](https://mlsanity.com/paper/2106.16057/) (2021)
- [DimmWitted: A Study of Main-Memory Statistical Analytics](https://mlsanity.com/paper/1403.7550/) (2014)
- [A Formal Framework For Probabilistic Unclean Databases](https://mlsanity.com/paper/1801.06750/) (2018)
- [Batchwise Probabilistic Incremental Data Cleaning](https://mlsanity.com/paper/2011.04730/) (2020)

## Built on by (most cited first, 2 of 2)

- [Jellyfish: A Large Language Model for Data Preprocessing](https://mlsanity.com/paper/2312.01678/) (2023)
- [Quality Assessment of Tabular Data using Large Language Models and Code Generation](https://mlsanity.com/paper/2509.10572/) (2025)

## Abstract (arXiv)

There is a considerable body of work on data cleaning which employs various principles to rectify erroneous data and transform a dirty dataset into a cleaner one. One of prevalent approaches is probabilistic methods, including Bayesian methods. However, existing probabilistic methods often assume a simplistic distribution (e.g., Gaussian distribution), which is frequently underfitted in practice, or they necessitate experts to provide a complex prior distribution (e.g., via a programming language). This requirement is both labor-intensive and costly, rendering these methods less suitable for real-world applications. In this paper, we propose BClean, a Bayesian Cleaning system that features automatic Bayesian network construction and user interaction. We recast the data cleaning problem as a Bayesian inference that fully exploits the relationships between attributes in the observed dataset and any prior information provided by users. To this end, we present an automatic Bayesian network construction method that extends a structure learning-based functional dependency discovery method with similarity functions to capture the relationships between attributes. Furthermore, our system allows users to modify the generated Bayesian network in order to specify prior information or correct inaccuracies identified by the automatic generation process. We also design an effective scoring model (called the compensative scoring model) necessary for the Bayesian inference. To enhance the efficiency of data cleaning, we propose several approximation strategies for the Bayesian inference, including graph partitioning, domain pruning, and pre-detection. By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0.9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.