Department of Statistics, Virginia Tech, Blacksburg, Virginia, USA.
PLoS One. 2013;8(3):e50474. doi: 10.1371/journal.pone.0050474. Epub 2013 Mar 20.
Typical data visualizations result from linear pipelines that start by characterizing data using a model or algorithm to reduce the dimension and summarize structure, and end by displaying the data in a reduced dimensional form. Sensemaking may take place at the end of the pipeline when users have an opportunity to observe, digest, and internalize any information displayed. However, some visualizations mask meaningful data structures when model or algorithm constraints (e.g., parameter specifications) contradict information in the data. Yet, due to the linearity of the pipeline, users do not have a natural means to adjust the displays. In this paper, we present a framework for creating dynamic data displays that rely on both mechanistic data summaries and expert judgement. The key is that we develop both the theory and methods of a new human-data interaction to which we refer as " Visual to Parametric Interaction" (V2PI). With V2PI, the pipeline becomes bi-directional in that users are embedded in the pipeline; users learn from visualizations and the visualizations adjust to expert judgement. We demonstrate the utility of V2PI and a bi-directional pipeline with two examples.
典型的数据可视化结果来自线性流水线，该流水线从使用模型或算法对数据进行特征化开始，以降低维度并总结结构，最后以降维形式显示数据。在管道的末端，当用户有机会观察、消化和内化显示的任何信息时，可能会进行意义构建。但是，当模型或算法约束（例如参数规范）与数据中的信息相矛盾时，某些可视化会掩盖有意义的数据结构。然而，由于流水线的线性性质，用户没有自然的方法来调整显示。在本文中，我们提出了一种创建依赖于机械数据汇总和专家判断的动态数据显示的框架。关键是我们开发了一种新的人机交互的理论和方法，我们称之为“可视化到参数交互”（V2PI）。通过 V2PI，流水线变得双向，因为用户嵌入在流水线中；用户从可视化中学习，可视化根据专家判断进行调整。我们通过两个示例演示了 V2PI 和双向流水线的实用性。
