A framework for the formalization of interactive visual analytics

交互式视觉分析形式化的框架

• 批准号: RGPIN-2016-05224
• 负责人: Goebel, Randy
• 金额: $ 1.6万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709208
• 关键词: framework; formalization; interactive; visual; analytics

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.

可视化的过程是将数据转换为图片。可视化的计算机科学研究是关于将数据转换为人类可以从中得出推论的图片的理论和实践的发展（我们使用“图片”这个词来包括各种视觉媒体，从页面上的点到3D视频）。 由于世界数据的种类和广度是巨大的，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数据到图片的转换保留那些最能帮助人们对这些数据进行“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很容易看出哪个国家培养了最多的曲棍球运动员。事实上，衡量一张好照片的一个标准是，它能让大多数人（如果不是所有人的话）得出相同的结论，而且它能避免引入视觉异常（例如，像内克尔立方体的模糊性）。 一个主要的挑战是如何构建保留基础数据重要属性的转换（例如，电子表格中的数字）时将它们转换成图片（例如，直方图）。由于可视化不能仅仅在图片中呈现每个基本数据点，因此需要一些数据聚合。例如，如果我们的曲棍球运动员原籍国将运动员的年龄转换为平均值，人们可能会“看到”哪些运动员平均年龄更小，但无法找出哪些是最年轻或最年长的。 最重要的可视化研究的挑战是如何减少数据量的可视化管理的形式，如何确定哪些聚合属性是重要的，以保持在该转换，然后如何评估替代转换，通过确认最典型的推理由人类。最近几乎无处不在的使用现代触摸屏技术加剧了挑战：除了保存数据属性，减少歧义，并确认有效的人类推理的首选图片，视觉操纵的问题回避了适当的剧目的图片操纵的挑战，以及如何以及他们可以帮助揭示图片内的数据属性。 总体而言，可视化研究的挑战是考虑财产保护，避免歧义，确认最佳推理支持，并确定适当的剧目的图片行动，以提高人类对数据的理解。所有的进步都有助于为各种形式的科学和商业数据提供更高的价值。