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原文連結
論文資訊
- 類型：已發表論文
- 日期：2015-12-30
摘要
In this paper, we present an abstract model of visualization and inference processes, and describe an 資訊-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal 熵 along a workflow. Based on this observation, we establish an 資訊-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To
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