Data Hunches: Incorporating Personal Knowledge into Visualizations
Data Hunches: Incorporating Personal Knowledge into Visualizations
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DOI：
10.1109/tvcg.2022.3209451
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发表时间：
2021-09
影响因子：
5.2
通讯作者：
Haihan Lin;Derya Akbaba;Miriah D. Meyer;A. Lex
中科院分区：
文献类型：
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作者：
Haihan Lin;Derya Akbaba;Miriah D. Meyer;A. Lex
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，不要对他们的发现过于自信。然而，关于数据集注意事项的个人知识通常不会以结构化的方式合并，如果缺乏这种知识的其他人解释数据，这是有问题的。在这项工作中，我们将分析师对数据集的知识定义为数据预感。我们区分数据预感和不确定性，并讨论预感的类型。然后，我们探索记录数据预感的方法，并根据原型设计，开发设计支持数据预感的可视化的建议。最后，我们讨论了与数据预感相关的各种挑战，包括潜在的危害以及对信任和隐私的挑战。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。
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 overly confident about their findings if caveats are present. However, personal knowledge about the caveats of a dataset is typically not incorporated in a structured way, which is problematic if others who lack that knowledge interpret the data. In this work, we define such analysts' knowledge about datasets as data hunches. We differentiate data hunches from uncertainty and discuss types of hunches. We then explore ways of recording data hunches, and, based on a prototypical design, develop recommendations for designing visualizations that support data hunches. We conclude by discussing various challenges associated with data hunches, including the potential for harm and challenges for trust and privacy. 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.