AI 中文总结
研究针对领域专家缺乏查询语言知识难以独立管理数据质量的问题，提出质量模式模型框架QPM，通过模型驱动定义与技术和领域无关的分析模板，经三种数据库技术验证及用户研究，证明其表现力强且能助领域专家独立进行质量分析。
AI 中文摘要
在数据驱动的世界中，有效处理数据很大程度上依赖其质量，质量分析是数据质量管理核心。数据质量因领域和上下文而异，质量要求定义主要由领域专家负责，但他们常缺乏实现质量分析的查询语言专业知识，导致需技术专家参与，排除了领域专家独立管理数据质量的可能。为此提出质量模式模型框架（QPM），一种模型驱动方法来定义与特定数据库技术和应用领域无关的数据质量分析模板。给出针对XML、RDF和Neo4j三种数据库技术的概念验证实现。通过定性用户研究评估其表现力、在文化遗产领域的适用性及领域专家的可用性。结果表明QPM匹配甚至超越常见数据库查询语言表现力，且能让领域专家独立定义基于模板的质量分析。
英文摘要
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.
Comments38 pages, 19 figures, submitted to SoSym Journal
