Stöhr Mark R, Günther Andreas, Majeed Raphael W
UGMLC, German Center for Lung Research (DZL), Justus-Liebig-University, Giessen, Germany.
Stud Health Technol Inform. 2019 Sep 3;267:66-73. doi: 10.3233/SHTI190807.
Data integration is the problem of combining data residing at different sources and providing the user with a unified view of these data. In medical informatics, such a unified view enables retrospective analyses based on more facts and prospective recruitment of more patients than any single data collection by itself. The technical part of data integration is based on rules interpreted by software. These rules define how to perform the translation of source database schemata into the target database schema. Translation rules are formulated by data managers who usually do not have the knowledge about meaning and acquisition methods of the data they handle. The professionals (data providers) collecting the source data who have the respective knowledge again usually have no sufficient technical background. Since data providers are neither able to formulate the transformation rules themselves nor able to validate them, the whole process is fault-prone. Additionally, in continuous development and maintenance of (meta-) data repositories, data structures underlie changes, which may lead to outdated transformation rules. We did not find any technical solution, which enables data providers to formulate transformation rules themselves or which provides an understandable reflection of given rules. Our approach is to enable data providers understand the rules regarding their own data by presenting rules and available context visually. Context information is fetched from a metadata repository. In this paper, we propose a software tool that builds on existing data integration infrastructures. The tool provides a visually supported validation routine for data integration rules. In a first step towards its evaluation, we implement the tool into the DZL data integration process and verify the correct presentation of transformation rules.
数据集成是一个将驻留在不同源的数据进行合并，并为用户提供这些数据统一视图的问题。在医学信息学中，这样的统一视图能够进行基于更多事实的回顾性分析，并能比任何单一数据收集本身招募到更多的患者进行前瞻性研究。数据集成的技术部分基于软件解释的规则。这些规则定义了如何将源数据库模式转换为目标数据库模式。转换规则由数据管理人员制定，而他们通常并不了解所处理数据的含义和获取方法。收集源数据的专业人员（数据提供者）虽然具备相关知识，但通常又没有足够的技术背景。由于数据提供者既无法自行制定转换规则，也无法对其进行验证，整个过程容易出错。此外，在（元）数据存储库的持续开发和维护中，数据结构会发生变化，这可能导致转换规则过时。我们没有找到任何技术解决方案，能够让数据提供者自行制定转换规则，或者提供对给定规则的可理解的反映。我们的方法是通过直观地展示规则和可用上下文，让数据提供者理解关于他们自己数据的规则。上下文信息从元数据存储库中获取。在本文中，我们提出了一种基于现有数据集成基础设施构建的软件工具。该工具为数据集成规则提供了可视化支持的验证程序。在对其进行评估的第一步中，我们将该工具应用于DZL数据集成过程，并验证转换规则的正确呈现。
