A framework for the formalization of interactive visual analytics

可视化的过程就是把数据转换成图片。可视化的计算机科学研究是关于将数据转换成图像的理论和实践的发展，人类可以从中得出推论（我们使用“图像”这个词包括所有形式的视觉媒体，从页面上的点到3D视频）。由于世界上数据的多样性和广度是巨大的，可视化过程必须过滤、压缩或以其他方式减少数据的范围和复杂性，以便将其转换为可感知的图像。因此，可视化的一个基本挑战是确保图像转换的数据保留那些最有助于人类对这些数据做出“适当”推断的属性。例如，一个按原籍国表示曲棍球运动员数量的条形图应该可以很容易地看出哪个国家产生的曲棍球运动员最多。事实上，衡量一张好图片的标准之一是，它能让大多数人（如果不是所有人）得出相同的结论，并且避免引入视觉异常（例如，像Necker立方体的模糊性）。***一个主要的挑战是如何在将基本数据（例如电子表格中的数字）转换为图片（例如直方图）时构建保存重要属性的转换。由于可视化不能仅仅显示图片中的每个基本数据点，因此需要一些数据聚合。例如，如果我们的曲棍球运动员的原籍国将球员的年龄转换为平均年龄，人们可能会“看到”哪些球员的平均年龄更小，但无法找出哪个是最年轻的或最年长的。***最重要的可视化研究挑战是如何将数据量减少到可视化可管理的形式，如何确定在该转换中保留哪些重要的聚合属性，以及如何通过确认人类最典型的推断来评估替代转换。最近几乎无处不在的现代触摸屏技术的使用加剧了这一挑战：除了保留数据属性，减少歧义，并为有效的人类推理确认首选图片外，视觉操作问题还要求适当的图片操作库，以及它们如何帮助揭示图片中的数据属性。***总体而言，可视化研究的挑战在于考虑属性保护、避免歧义、确认最佳推理支持以及识别适当的图片动作库，以提高人类对数据的理解。所有的进步都有助于对各种形式的科学和商业数据进行更高价值的开发

The process of visualization is about transforming data into pictures. The computer science study of visualization is about the development of the theory and practise of transforming data into pictures from which humans can draw inferences (we use the word "picture" to include all manner of visual media, from dots on a page to 3D video).***Because the variety and breadth of the world's data is enormous, a visualization process must necessarily filter, compress, or otherwise reduce the scope and complexity of data in order to transform it into sensible pictures. So one foundational challenge of visualization is to ensure that data to picture transformations preserve those properties that best help humans draw "appropriate" inferences about those data. For example, a bar chart that represents the number of hockey players by country of origin should make it easy to see which country produces the most hockey players. In fact, one measure of a good picture is that it leads most if not all humans to draw the same conclusions, and that it avoids introducing visual anomalies (e.g., like the ambiguity of a Necker cube).***A major challenge is about how to build transformations that preserve important properties of the base data (e.g., numbers in a spreadsheet) when turning them into a picture (e.g., a histogram). Since visualization can't merely present every base data point in a picture, some aggregation of data is required. For example, if our hockey player country of origin transformed the ages of players into averages, one could likely "see" which players were on average younger, but not find out which was youngest or oldest. ***The most important visualization research challenges are about how to reduce data volumes to visually manageable forms, how to determine what aggregate properties are important to preserve in that transformation, and then how to evaluate alternative transformations by confirming the most typical inferences by humans. The recent almost ubiquitous use of modern touch screen technologies exacerbates the challenge: in addition to preserving data properties, reducing ambiguity, and confirming preferred pictures for efficient human inference, the question of visual manipulation begs the challenge of appropriate repertoires of picture manipulation, and how well they can help reveal data properties within pictures.***Overall, the challenge of visualization research is to consider property preservation, avoidance of ambiguity, confirmation of best inference support, and identification of appropriate repertoires of picture actions to improve human understanding of data. All advances help provide higher value exploitation of all forms of scientific and business data.**