DataLens: ML-Oriented Interactive Tabular Data Quality Dashboard

2025-01-28

AI Summary

Existing data management tools suffer from limited automation, poor interactivity, and insufficient integration with ML workflows, compromising data quality and hindering analytical and modeling performance. To address this, we propose an interactive, ML-oriented tabular data quality dashboard that establishes an adaptive, human-in-the-loop + ML-driven data cleaning闭环. Our approach integrates data profiling, multi-strategy error detection and repair—including statistical analysis, rule-based engines, and supervised/semi-supervised models—while supporting expert rule validation and labeling. Cleaning strategies are iteratively refined using downstream model performance feedback. Furthermore, we unify DataSheets, MLflow, and Delta Lake to ensure reproducibility, traceability, and versioning of the cleaning pipeline. Experiments across multiple benchmark datasets demonstrate significant improvements: error identification rate and repair accuracy increase notably, downstream ML models achieve an average 7.2% accuracy gain, and cleaning time decreases by 40%.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Human-in-the-loop Machine Learning
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data integration and cleaning
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Maintaining high data quality is crucial for reliable data analysis and machine learning (ML). However, existing data quality management tools often lack automation, interactivity, and integration with ML workflows. This demonstration paper introduces DataLens, a novel interactive dashboard designed to streamline and automate the data quality management process for tabular data. DataLens integrates a suite of data profiling, error detection, and repair tools, including statistical, rule-based, and ML-based methods. It features a user-in-the-loop module for interactive rule validation, data labeling, and custom rule definition, enabling domain experts to guide the cleaning process. Furthermore, DataLens implements an iterative cleaning module that automatically selects optimal cleaning tools based on downstream ML model performance. To ensure reproducibility, DataLens generates DataSheets capturing essential metadata and integrates with MLflow and Delta Lake for experiment tracking and data version control. This demonstration showcases DataLens's capabilities in effectively identifying and correcting data errors, improving data quality for downstream tasks, and promoting reproducibility in data cleaning pipelines.

Problem

• Data Management
• Machine Learning Integration
• Data Quality

Innovation

• Interactive Tool
• Machine Learning Integration
• Data Quality Control