A framework for the formalization of interactive visual analytics

可视化的过程是将数据转换为图片。计算机科学对可视化的研究是关于将数据转换为图片以供人类进行推理的理论和实践的发展(我们使用“图片”一词包括从页面上的点到3D视频的所有形式的可视媒体)。*由于世界上数据的种类和广度巨大，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，以便将其转换为合理的图片。因此，可视化的一个基本挑战是确保数据到图片的转换保留那些最能帮助人类对这些数据做出“适当”推论的属性。例如，一个按来源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培养的曲棍球运动员最多。事实上，一张好图片的一个衡量标准是，它会导致大多数人(如果不是所有人)得出相同的结论，并且它避免了引入视觉异常(例如，像Necker立方体的模棱两可)。*主要挑战是如何构建转换，在将基础数据(例如，电子表格中的数字)转换为图片(例如，直方图)时，保留它们的重要属性。由于可视化不能仅仅呈现图片中的每个基本数据点，因此需要对数据进行一些聚合。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”哪些球员平均年轻，但不能找出哪些是最年轻的，哪些是最老的。*最重要的可视化研究挑战是关于如何将数据量减少到可视可管理的形式，如何确定在该转换中保留哪些聚合属性是重要的，然后如何通过确认人类最典型的推断来评估替代转换。最近现代触摸屏技术的几乎无处不在的使用加剧了这一挑战：除了保存数据属性、减少歧义和确认用于有效人类推理的首选图片之外，视觉操作的问题引发了对图片操作的适当指令集的挑战，以及它们能够在多大程度上帮助揭示图片中的数据属性。*总的来说，可视化研究的挑战是考虑属性保留、避免歧义、确认最佳推理支持以及识别适当的图片操作指令集以提高人类对数据的理解。所有进步都有助于为所有形式的科学和商业数据提供更高价值的利用。**

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.**