Scalable data science requires access to metadata, which is increasingly managed by databases called data catalogs. With today's data catalogs, users choose between designs that make it easy to store or retrieve metadata, but not both. We find this problem arises because catalogs lack an easy to understand mental model. In this paper, we present 5W1H+R, a new catalog mental model that is comprehensive in the metadata it represents, and comprehensible in that it permits users to locate metadata easily. We demonstrate these properties via a user study. We then study different schema designs for the new mental model implementation and evaluate them on different backends to understand their relative merits. We conclude mental models are important to make data catalogs more useful and to boost metadata management efforts.

翻译：可扩缩的数据科学需要获取元数据,而元数据越来越多地由称为数据目录的数据库管理。用今天的数据目录,用户在便于存储或检索元数据的设计中选择一种设计,而不是两者兼而有之。我们发现这一问题是因为目录缺乏易于理解的心理模型而产生。在本文中,我们提出了5W1H+R,这是一个新的目录心理模型,它在其所代表的元数据中是全面的,可以理解它使用户能够很容易地查找元数据。我们通过用户研究来展示这些属性。我们随后为新的心理模型的实施而研究不同的系统设计,并在不同的后端上评估这些设计,以了解它们的相对优点。我们得出结论,精神模型对于使数据目录更加有用和增强元数据管理努力非常重要。