Label noise remains a fundamental challenge in supervised learning, often degrading model performance and data reliability. While many existing approaches assume that a single relabeling action by a human annotator suffices to correct errors, this assumption overlooks the inherent fallibility of human judgment. In this article, we propose a general-purpose framework that models relabeling as an iterative and imperfect process, drawing inspiration from industrial sample inspection techniques. Our method integrates uncertainty sampling with targeted inspection to identify and refine mislabeled data through repeated annotation and aggregation. Crucially, this framework extends label correction beyond categorical classification to encompass real-valued regression tasks. For categorical data, majority voting is used to resolve annotation conflicts, while for continuous labels, we introduce an averaging-based correction strategy that incrementally approximates true values over successive relabeling rounds. This design enables systematic refinement of both discrete and continuous labels under noisy supervision. We validate our approach through a two-stage evaluation. First, on clean benchmark datasets with synthetic noise, we demonstrate significant improvements in relabeling efficiency under various uncertainty metrics and model capacities. Second, on a real-world image dataset with inherently noisy labels, our method continues to outperform baseline strategies, especially when paired with strong learners like XGBoost. Our findings show that sample inspection is a scalable and cost-effective mechanism for robust label correction across both classification and regression domains, even under high error rates or limited computational resources.
Disinformation, although an ancient phenomenon, has gained unprecedented reach and speed with the rise of the internet and social media platforms. While traditional fact-checking approaches focus on the semantic content of information, this article proposes a quantitative analysis based on metadata and formal textual features to investigate disinformation from a quality dimension perspective, assuming that false or misleading information often fails to meet informational quality criteria. Using an experimental approach, we analyzed two datasets of news from reliable and unreliable sources and applied statistical methods, including the Mann–Whitney U test, Cliff’s Delta, and Rosenthal’s r, to measure differences and effect size in the quality dimensions of accuracy, currency, readability, consistency, and reliability. The results show that lexical cohesion and lexical diversity are the strongest discriminators of source reliability, followed by structural error rates, while currency and readability display only weak discriminative power. The proposed News Reliability Index (NRI) emerges as a moderate but complementary indicator. Overall, reliable sources consistently demonstrate higher information quality, but structural differences alone are insufficient to detect disinformation, especially considering the capacity of generative AI to produce syntactically coherent texts. We conclude that semantic content analysis remains essential for identifying disinformation, with structural features best applied as supporting signals in detection models. Finally, we highlight future challenges, such as the growing use of artificial intelligence in generating high-quality disinformation, which may reduce the effectiveness of structural metrics and complicate automation in verification processes.
Data cleaning is a critical component of modern data intelligence systems. However, most existing approaches have been proposed for tabular data, despite the fact that much real-world data is unstructured. We propose to leverage information extraction programs to disclose where to apply data cleaning processes for documents. After using information extraction to produce a table of values extracted from the document (an “extracted view” of the documents, in the sense of a database view ), the resulting table is subject to any desired tabular-based data cleaning protocol. However, in order to reflect such cleaning back in the source documents, we require that updates to the tabular views can be translated into document updates without introducing any unintended changes to the contents in those views. With this approach, we consider a document to be clean if all its extracted views are clean. In this article, we characterize and verify a set of sufficient conditions for rule-based extraction programs that ensure document updates do not introduce unintended changes to the extracted views, thus qualifying them for inclusion in a document cleaning pipeline. Through experiments conducted on medical records and on math-intensive documents, we demonstrate that our approach provides an effective, practical pipeline for correcting data quality problems in documents. The second of these experiments demonstrates that a math retrieval system can perform at least twice as well on the cleaned documents as it does when applied to the documents without cleaning, matching the improvements achieved by specially tailored cleaning methods applied to those documents during the search pipeline but preserving the cleaned documents for use in other applications as well.
Anomaly-management tools monitor data streams in complex systems, e.g., tracking weather parameters collected from sensors in a region or inspecting resource usage of machines in a data center. Their goal is to detect outliers and abnormal behavior of the system, including a burst of outliers, which is often the result of a major event, like a hurricane or a crash of machines in a data center. An interval alert is an alert raised for such a burst of outliers. In data pipelines, such alerts indicate time intervals during which major data quality issues may occur. This allows alerting downstream applications on potential events and data quality risks. In this article, we present Ourea , a novel unsupervised learning method that computes interval alerts by identifying anomalous time intervals of variable lengths in data streams. Ourea raises interval alerts on significant events, based on a concentration of outliers detected in the raw data stream and in high-level aggregate views. It uses Kernel Density Estimate (KDE) to identify the most significant time intervals and their boundaries, can issue preliminary alerts early before intervals end, and minimizes the number of raised alerts by alerting only on significant ones. Extensive experiments over real and synthetic data demonstrate the effectiveness and scalability of Ourea and show that for detecting variable-length anomalous intervals in data streams, our algorithm is more accurate and more efficient than state-of-the-art methods.
An important goal of Chief Data Officers (CDOs) and data quality efforts is to increase the value of an organization's data. But that increased value makes the data an even more desirable target for cyberattacks, which have become more frequent, sophisticated, and impactful. In addition to the efforts that individual companies have made, the governments worldwide are responding by introducing or proposing new cybersecurity regulations to help protect that data, making security an important aspect of data quality. This study offers a novel perspective on the evolving global cybersecurity regulatory environment. Drawing on a comprehensive comparative analysis of nearly 200 regulatory frameworks from a wide array of international and national jurisdictions, the research identifies a core group of regulatory features that are systematically organized into five principal thematic categories. In particular, this research employs an integrated classification schema and a multidimensional taxonomy to facilitate more precise navigation of the complex regulatory landscape. Using a structured qualitative synthesis approach combining elements of review and cross-jurisdictional mapping, the analysis highlights notable disparities in regional regulatory focus, with Data Privacy, Incident Reporting, and Security by Design standing out as the most recurrent regulatory priorities. A significant outcome of the study is the identification of varying synergy levels between regulatory features, with high integration observed in Data Privacy and Cross-Border Data Transfer, medium synergy in areas such as Incident Reporting and Risk Management, and low synergy between Security by Design and Emerging Technologies. Therefore, by examining how various regulatory features align—or fail to align—across jurisdictions, the study provides critical insights for legislators, regulators, and data quality leaders and researchers. The article concludes with targeted recommendations to support more consistent, adaptive, and future-resilient cybersecurity governance worldwide to further improve data quality.
