Unfamiliar measurements usually hinder readers from grasping the scale of the numerical data, understanding the content, and feeling engaged with the context. To enhance data comprehension and communication, we leverage analogies to bridge the gap between abstract data and familiar measurements. In this work, we first conduct semi-structured interviews with design experts to identify design problems and summarize design considerations. Then, we collect an analogy dataset of 138 cases from various online sources. Based on the collected dataset, we characterize a design space for creating data analogies. Next, we build a prototype system, AnalogyMate, that automatically suggests data analogies, their corresponding design solutions, and generated visual representations powered by generative AI. The study results show the usefulness of AnalogyMate in aiding the creation process of data analogies and the effectiveness of data analogy in enhancing data comprehension and communication.

翻译：通常，不熟悉的度量标准会阻碍读者把握数值数据的规模、理解内容并融入相关语境。为增强数据理解与传达，我们利用类比来弥合抽象数据与熟悉度量之间的差距。本研究首先通过半结构化访谈与设计专家共同识别设计问题并总结设计考量；随后从多个网络来源收集包含138个案例的类比数据集。基于该数据集，我们刻画了创建数据类比的设计空间。接着，我们构建了原型系统AnalogyMate，该系统可自动推荐数据类比及其对应的设计方案，并借助生成式AI生成可视化表达。研究结果表明，AnalogyMate在辅助数据类比创建过程中的实用性，以及数据类比在增强数据理解与传达方面的有效性。