arXiv 2109.070352021-09cs.HC

数据预感：将个人知识转化为可视化

Data Hunches: Incorporating Personal Knowledge into Visualizations

Haihan Lin, Derya Akbaba, Miriah Meyer, Alexander Lex

摘要：

数据的问题在于，它经常只提供感兴趣的现象的不完美的表示。熟悉数据集的专家在分析数据集时通常会进行隐含的心理修正，或者在存在警告的情况下会谨慎行事，不要对任何发现过于自信。然而，关于数据集警告的隐含知识通常不会以结构化的方式收集，这是有问题的，特别是当团队一起工作时，他们可能了解数据集的不同方面。在这项工作中，我们定义这样的分析师的知识数据集的数据预感。我们讨论了数据预感的影响，并提出了一套技术，通过数据可视化记录和交流数据预感。此外，我们还提供了设计支持记录和可视化数据预感的可视化的指导方针。我们设想，数据预感将使分析师能够将他们的知识具体化，促进协作和沟通，并支持从他人的数据预感中学习的能力。

英文摘要：

The trouble with data is that it frequently provides only an imperfect representation of a phenomenon of interest. Experts who are familiar with their datasets will often make implicit, mental corrections when analyzing a dataset, or will be cautious not to be over-confident in any findings if caveats are present. However, the implicit knowledge about the caveats of a dataset are typically not collected in a structured way, which is problematic especially when teams work together who might have knowledge about different aspects of a dataset. In this work, we define such analyst's knowledge about datasets as data hunches. We discuss the implications of data hunches and propose a set of techniques for recording and communicating data hunches through data visualization. Furthermore, we provide guidelines for designing visualizations that support recording and visualizing data hunches. We envision that data hunches will empower analysts to externalize their knowledge, facilitate collaboration and communication, and support the ability to learn from others' data hunches.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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英文摘要：

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