Domain-Specific Data Quality Analysis Using Technology-Independent Query Templates

2026-07-27

AI Summary

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.

Abstract

In an increasingly data-driven world, effectively working with data depends heavily on its quality. Quality analysis is a central aspect of data quality management. As data quality is typically domain- and context-specific, the definition of quality requirements is primarily the responsibility of domain experts. However, domain experts often lack the query language expertise needed to implement quality analyses. Therefore, the process of defining quality analyses results in a resource-intensive workflow that requires the involvement of technical experts, effectively excluding domain experts from independently managing data quality. To address this challenge, we present the Quality Pattern Model framework (QPM), a model-driven approach to define templates for data quality analyses that are independent of specific database technologies and application domains. QPM can eliminate the need for deep technical expertise and prevent the need for defining quality analyses several times for different database technologies. We present a proof-of-concept implementation of this approach for three database technologies: XML, RDF, and Neo4j. We evaluate the expressiveness of our approach, its applicability in the cultural heritage domain, and its usability by domain experts. For this purpose, we conducted a qualitative user study and empirically collected quality problems in a catalog. Our findings suggest that QPM matches and even exceeds the expressiveness of common database query languages. Furthermore, the results indicate that our tool enables domain experts to define template-based quality analyses independently, without requiring support of IT experts.

Problem

• data quality
• domain experts
• query language
• technology independence
• quality analysis

Innovation

• Quality Pattern Model
• technology-independent query templates
• domain-specific data quality
• model-driven approach
• user-centric data quality analysis