Black Holes, Keyholes And Brown Worms: Challenges In Sense Making
Black Holes, Keyholes And Brown Worms: Challenges In Sense Making
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黑洞、钥匙孔和棕色蠕虫：意义建构的挑战
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
M. Varga
中科院分区：
文献类型：
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作者：
B. Wong;M. Varga
我们解决了分析师所面临的问题，他们必须快速准确地筛选大量数据，以便理解数据中包含的信息或数据中表示的情况。我们从数据框架理论的角度讨论了我们所采取的视觉分析方法，以及它对因果推理的扩展，以及在这些过程中调用的认知策略如何得到支持。我们确定了视觉分析型系统的设计师需要解决的20个问题，以支持意义构建。特别地，我们讨论了与三个范例问题相关的设计问题：(i)黑洞-表示缺失数据的问题；（ii） Keyholes——只能访问和查看大数据集的一小部分或问题的一部分的问题；（iii）布朗蠕虫——处理和呈现误导性或欺骗性数据的问题。
We address the problems faced by analysts who have to sift through large amounts of data quickly and accurately in order to make sense of the information contained within the data or the circumstance represented in the data. We discuss the approach we have taken to visual analytics from the perspective of the Data-Frame Theory of Sense-making and its extension to Causal Reasoning, and how the cognitive strategies that are invoked in these processes need to be supported. We identify 20 problems that designers of visual analytics-type systems need to address in order to support sense-making. In particular, we discuss design issues associated with three exemplar problems: (i) Black holes - the problem of representing missing data; (ii) Keyholes - the problem of being able to access and view only a small part of a large dataset or only part of a problem; and (iii) Brown worms - the problem of dealing with and representing misleading or deceptive data.