让领域专家不用懂技术也能自己检查数据质量

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.