CGV: Small: Illustration Inspired Visualization: A Gateway to Interacting with High-Dimensional Data

这个研究项目的目标是设计新的可视化工具来帮助科学家从他们的高维数据中获得洞察力。高维数据是具有许多属性的观测值，其数量级为100甚至更多。今天的数据通常本质上是高维的：DNA微阵列，金融数据，超光谱图像，仅举几例。可视化这些数据的挑战来自于屏幕的有限维度。传统的数据可视化范例在不丢失固有语义、模式或结构的情况下，无法将高维属性完全映射到二维显示。这可能导致模糊甚至误导的可视化。为了克服这一根本的鸿沟，该项目开发的显示系统使用了从插图设计中收集的方法来传达这些难以捉摸的属性，这些属性来源于高维数据空间的分析。这项研究的第二个重要动机是，这种插图启发的方法有望产生更容易解释和操作的可视化效果。这项工作的总体主题是使用信息抽象和说明性映射来提高显示的可理解性，减少不必要的复杂性，并更忠实地传达高维数据模式。说明性框架由一个双管齐下的数据分析套件驱动，该套件使用过滤来创建多个级别的数据表示，并使用模式分类来识别合适的外观插图。这两种分析都在本地高维数据空间中执行，以保留原始结构。各种说明性样式与视觉语义相关联，以提供直观的数据显示。我们框架的通用性允许它很容易地映射到三个最突出的高维可视化平台：空间嵌入、平行坐标和散点图。说明性的可视化设计和验证是与环境科学和人类微生物组项目的专家合作进行的。该系统旨在支持领域科学家进行知识发现，但也通过插图设计支持数据分析，从而吸引普通用户。显示看起来更自然，因为它使用熟悉的图形设计范例来构建说明性可视化。项目网页（http://www.cs.sunysb.edu/~mueller/IllustratorND）提供有关正在进行的进展的信息，邀请参与用户研究，并在软件的网络版本中提供一些数据分析功能。该项目为学生提供了教育和研究的机会。

The goal of this research project is to devise new visualization tools to help scientists gain insight from their high-dimensional data. High-dimensional data are observations with many attributes, on the order of 100s and more. Today's data are often inherently high-dimensional: DNA microarrays, financial tick-by-tick data, hyper-spectral imagery, just to name a few. The challenge in visualizing these data comes from the limited dimensionality of the screen. Traditional data visualization paradigms have inherent inabilities to fully map high-dimensional properties to a two-dimensional display without loss of inherent semantics, patterns or structure. This can lead to ambiguous and even misleading visualizations. To overcome this fundamental chasm, the display system developed in this project uses methods gleaned from illustrative design to communicate these elusive properties, derived from analysis in the high-dimensional data space. A second important motivation of this research is that this illustration-inspired approach are expected to produce visualizations that are easier to interpret and manipulate. The overall theme of this work is to use information abstraction and illustrative mappings to improve display comprehensibility, reduce unnecessary complexity, and communicate high-dimensional data patterns more faithfully. The illustrative framework is driven by a two-pronged data analysis suite that uses filtering to create a data representation at multiple levels of scale and pattern classification to identify suitable appearance illustrations. Both of these analyses are performed in the native high-dimensional data space to preserve the original structures. Various illustrative styles are linked to visual semantics to provide an intuitive data display. The generality of our framework allows it to readily map to the three most prominent high-dimensional visualization platforms: space embeddings, parallel coordinates, and scatter plots. Illustrative visualization design and validation is carried out in collaboration with experts in Environmental Science and the Human Microbiome Project.The system is designed to support domain scientists in knowledge discovery, but also appeal to casual users by supporting data analysis via illustrative design. The display looks more natural since it uses familiar graphics design paradigms to construct the illustrative visualizations. The project webpage (http://www.cs.sunysb.edu/~mueller/IllustratorND) provides information on ongoing progress, invites participation in user studies, and also provides some data analysis capabilities within a web-enabled version of the software. The project offers educational and research opportunities for students.