Query-Guided Analysis and Mitigation of Data Verification Errors (Extended Version)

2026-03-09

AI Summary

This work addresses the propagation of label errors in data validation, which can severely compromise the reliability of downstream query results. To quantify the impact of such errors and identify high-risk tuples whose uncertainty may be exacerbated by validation, the authors propose Maximum Error Score (MES)—a data-distribution-agnostic metric. Building on MES, they design MESReduce, an interactive validation optimization algorithm that adaptively guides the verification process by efficiently computing MES and incorporating feedback from external validators. Experimental evaluation on both real-world and synthetic datasets demonstrates that MESReduce significantly reduces the maximum error score and effectively enhances validation accuracy.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• 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

Data verification, the process of labeling data items as correct or incorrect, is a preprocessing step that may critically affect the quality of results in data-driven pipelines. Despite recent advances, verification can still produce erroneous labels that propagate to downstream query results in complex ways. We present a framework that complements existing verification tools by assessing the impact of potential labeling errors on query outputs and guiding additional verification steps to improve result reliability. To this end, we introduce Maximal Error Score (MES), a worst-case uncertainty metric that quantifies the reliability of query output tuples independently of the underlying data distribution. As an auxiliary indicator, we identify risky tuples - input tuples for which reducing label uncertainty may counterintuitively increase the output uncertainty. We then develop efficient algorithms for computing MES and detecting risky tuples, as well as a generic algorithm, named MESReduce, that builds on both indicators and interacts with external verifiers to select effective additional verification steps. We implement our techniques in a prototype system and evaluate them on real and synthetic datasets, demonstrating that MESReduce can substantially and effectively reduce the MES and improve the accuracy of verification results.

Problem

• data verification
• labeling errors
• query reliability
• error propagation
• uncertainty quantification

Innovation

• Maximal Error Score
• Data Verification
• Query-Guided Mitigation
• Risky Tuples
• Uncertainty Quantification