# d-DQIVAR: Data-centric Visual Analytics and Reasoning for Data Quality Improvement

arXiv: https://arxiv.org/abs/2507.11960 · HTML: https://mlsanity.com/paper/2507.11960/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [A Survey of Visual Analytics Techniques for Machine Learning](https://mlsanity.com/paper/2008.09632/) (2020)
- [OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples](https://mlsanity.com/paper/2002.03103/) (2020)
- [VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection](https://mlsanity.com/paper/2009.12975/) (2020)
- [Visual Analytics For Machine Learning: A Data Perspective Survey](https://mlsanity.com/paper/2307.07712/) (2023)
- [Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets](https://mlsanity.com/paper/2108.05935/) (2021)
- [Diagnosing Concept Drift with Visual Analytics](https://mlsanity.com/paper/2007.14372/) (2020)
- [Diagnosing Ensemble Few-Shot Classifiers](https://mlsanity.com/paper/2206.04372/) (2022)
- [VIANA: Visual Interactive Annotation of Argumentation](https://mlsanity.com/paper/1907.12413/) (2019)
- [VASSL: A Visual Analytics Toolkit for Social Spambot Labeling](https://mlsanity.com/paper/1907.13319/) (2019)
- [FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images](https://mlsanity.com/paper/1908.00671/) (2019)
- [LabelVizier: Interactive Validation and Relabeling for Technical Text Annotations](https://mlsanity.com/paper/2303.17820/) (2023)
- [Interactive Reweighting for Mitigating Label Quality Issues](https://mlsanity.com/paper/2312.05067/) (2023)
- [Enhancing Single-Frame Supervision for Better Temporal Action Localization](https://mlsanity.com/paper/2312.05178/) (2023)
- [ZeroED: Hybrid Zero-shot Error Detection through Large Language Model Reasoning](https://mlsanity.com/paper/2504.05345/) (2025)

## Abstract (arXiv)

Approaches to enhancing data quality (DQ) are classified into two main categories: data- and process-driven. However, prior research has predominantly utilized batch data preprocessing within the data-driven framework, which often proves insufficient for optimizing machine learning (ML) model performance and frequently leads to distortions in data characteristics. Existing studies have primarily focused on data preprocessing rather than genuine data quality improvement (DQI). In this paper, we introduce d-DQIVAR, a novel visual analytics system designed to facilitate DQI strategies aimed at improving ML model performance. Our system integrates visual analytics techniques that leverage both data-driven and process-driven approaches. Data-driven techniques tackle DQ issues such as imputation, outlier detection, deletion, format standardization, removal of duplicate records, and feature selection. Process-driven strategies encompass evaluating DQ and DQI procedures by considering DQ dimensions and ML model performance and applying the Kolmogorov-Smirnov test. We illustrate how our system empowers users to harness expert and domain knowledge effectively within a practical workflow through case studies, evaluations, and user studies.