define functional data-validity criteria

Data quality definitions have long suffered from multidimensionality and conceptual inconsistency, necessitating systematic synthesis to establish a unified theoretical framework. This study introduces Feature-Oriented Domain Analysis (FODA) to data quality research for the first time, integrating a Systematic Literature Review (SLR) with quality dimension modeling to construct the first structured taxonomy encompassing mainstream definitions. We identify and clarify 12 core quality dimensions and their semantic relationships, proposing a novel four-level, feature-oriented taxonomy that significantly enhances definitional comparability and theoretical coherence. Our analysis reveals three critical research gaps: (1) lack of understanding of dynamic dimension evolution, (2) insufficient cross-domain semantic alignment, and (3) weak empirical validation. The resulting taxonomy provides a scalable, theoretically grounded foundation for data quality assessment, standardization, and tool development.

This paper addresses the challenge of evaluating the generalizability of data-driven models to target populations. Methodologically, it integrates statistical learning theory with empirical validation paradigms to propose the first systematic, domain-agnostic framework for model validation. The framework establishes three core principles: (1) sufficiency of validation strategies, (2) mandatory disclosure of limitations, and (3) design of performance metrics enabling cross-model comparability. Its key contribution lies in unifying transparency, reproducibility, and methodological rigor within a single, generalizable validation standard—thereby overcoming longstanding fragmentation and inconsistent reporting in current validation practices. Applicable to diverse data-driven models—including machine learning and statistical prediction models—the framework substantially enhances validation reliability, result comparability across studies, and cross-study reproducibility. It provides a foundational methodological basis for clinical deployment and regulatory evaluation of predictive models.

This work addresses the challenge that domain experts face in translating natural language descriptions of data quality requirements into executable analyses, a process often hindered by reliance on data engineers, resulting in inefficiency and high technical barriers. To overcome this, the paper proposes a no-code, model-driven pipeline that leverages a QPM metamodel to define domain-specific quality analysis templates. Coupled with the Constrainify toolchain, it automatically transforms natural language requirements into executable and reusable analytical logic. By integrating model-driven engineering, metamodeling, and no-code web technologies, the approach significantly reduces dependency on technical expertise, enabling efficient, reproducible, and semantically aligned data quality assessments. This advancement enhances both the accessibility and automation of data quality analysis for non-technical domain practitioners.

This study addresses the challenge that domain experts, due to limited query language proficiency, often struggle to independently conduct context-specific data quality analyses and must rely on technical specialists, resulting in inefficient workflows. To overcome this limitation, the paper proposes the Quality Pattern Model (QPM) framework—a novel, template-based mechanism that is agnostic to both database technologies and application domains, enabling non-technical users to autonomously define data quality analysis logic. Leveraging a model-driven approach, the authors implement QPM prototypes over XML, RDF, and Neo4j. Experimental results demonstrate that QPM’s expressiveness matches or exceeds that of mainstream query languages while significantly enhancing domain experts’ analytical autonomy. The framework’s effectiveness has been validated in the cultural heritage domain.

This study addresses a critical limitation in traditional reproducible research, where sharing only code and results fails to expose the implicit assumptions, expectations, and premises underlying an analyst’s reasoning—thereby hindering thorough evaluation of analytical quality. To overcome this, the paper proposes a formal modeling framework that explicitly translates the analyst’s tacit reasoning process into structured logical representations, statically capturing the construction logic of the analysis. This approach enables systematic scrutiny of the analytical chain of reasoning, assumption sensitivity, and conclusion robustness—even in the absence of the original data. Empirical validation on representative data analysis tasks demonstrates the framework’s effectiveness, achieving both logical visualization and data-free static assessment of analytical integrity.

This study addresses the lack of systematic evaluation of data quality tools with respect to their measurement capabilities and integration with large language models (LLMs). It presents the first multidimensional assessment framework grounded in real-world enterprise use cases, systematically evaluating six prominent tools—including open-source solutions such as Great Expectations and Deequ, as well as commercial platforms like Informatica and Experian—across dimensions including rule definition, duplicate detection, metric aggregation, and uncertainty handling, along with their LLM integration mechanisms. The findings reveal that commercial tools offer more comprehensive functionality and初步 support for LLM-assisted rule generation, whereas open-source tools provide greater flexibility at the cost of higher implementation effort. Notably, none of the evaluated tools currently enable direct LLM-based data validation. This work provides empirical guidance for selecting data quality tools and advancing their integration with LLMs.

This study addresses the prevalent ambiguity, inconsistency, and incompleteness in articulating explainability requirements for AI systems due to a lack of standardized specifications. Through a structured literature review and interviews with developers, the authors identify a set of explainability quality attributes, which are then refined via a large-scale survey of practitioners into ten core attributes. For the first time, these attributes are translated into a prioritized, actionable guideline for writing explainability requirements. Building on this foundation, the authors design a lightweight, iterative requirements engineering workflow augmented by a large language model to assist in requirement generation. An accompanying web-based tool reduces average requirement drafting time by 23.5%, and user evaluations indicate that the generated requirements match or slightly exceed manually written ones in terms of implementability and textual quality.

This study addresses the compliance challenges faced by data practitioners in machine learning systems under regulations such as the GDPR and the AI Act, particularly concerning data quality. Through semi-structured interviews with practitioners in the European Union, combined with thematic analysis of regulatory texts and engineering workflows, the research systematically uncovers a structural disconnect between regulation-driven data quality requirements and ML engineering practices. It identifies five core challenges: misalignment between legal principles and engineering implementation, fragmented data pipelines, lack of purpose-built compliance tools, ambiguous accountability, and reactive responses to audits. Building on these findings, the work proposes directions for designing compliance-oriented tooling, establishing effective governance mechanisms, and fostering cultural transformation to bridge the gap between regulatory mandates and practical ML development.