Iterative Data Curation with Theoretical Guarantees

2025-10-13

AI Summary

Manual verification of large-scale dynamic datasets is infeasible, leading to challenges in ensuring data accuracy. Method: This paper proposes a theory-driven, iterative data cleaning framework integrating error detection, automated correction, and progressive optimization. It formally models the iterative process, conducts accuracy testing, and performs probabilistic convergence analysis—validated via simulations and real-world case studies. Contribution/Results: The framework establishes the first rigorous theoretical guarantee of *probabilistic convergence to zero errors* for iterative data cleaning and proves that error detection accelerates error decay. Empirical results demonstrate that it significantly outperforms baseline methods in accuracy improvement and progressively approaches a fully correct dataset state, thereby unifying theoretical soundness with practical efficacy.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Web Mining and Content Analysis: Web data integration and cleaning
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In recent years, more and more large data sets have become available. Data accuracy, the absence of verifiable errors in data, is crucial for these large materials to enable high-quality research, downstream applications, and model training. This results in the problem of how to curate or improve data accuracy in such large and growing data, especially when the data is too large for manual curation to be feasible. This paper presents a unified procedure for iterative and continuous improvement of data sets. We provide theoretical guarantees that data accuracy tests speed up error reduction and, most importantly, that the proposed approach will, asymptotically, eliminate all errors in data with probability one. We corroborate the theoretical results with simulations and a real-world use case.

Problem

• Developing automated methods to improve accuracy in large datasets
• Providing theoretical guarantees for error reduction through iterative curation
• Ensuring asymptotic elimination of all data errors with probability one

Innovation

• Iterative data curation procedure for continuous improvement
• Theoretical guarantees for asymptotic error elimination
• Validation through simulations and real-world use case