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

该研究项目的目标是设计新的可视化工具，以帮助科学家从高维数据中获得洞察力。高维数据是具有许多属性的观测，数量级为100或更多。今天的数据往往是固有的高维：DNA微阵列，金融逐点数据，高光谱图像，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具有固有的不能够将高维属性完全映射到二维显示而不损失固有的语义、模式或结构。这可能导致模糊甚至误导性的可视化。为了克服这一根本性的鸿沟，在这个项目中开发的显示系统使用的方法收集的说明性设计来传达这些难以捉摸的属性，从高维数据空间的分析。这项研究的第二个重要动机是，这种启发性的方法有望产生更容易解释和操作的可视化。 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。说明性的框架是由一个双管齐下的数据分析套件，使用过滤来创建一个数据表示在多个级别的规模和模式分类，以确定合适的外观插图。这两种分析都是在本地高维数据空间中进行的，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。我们的框架的一般性，使它能够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。该系统与环境科学和人类微生物组项目的专家合作进行了可视化设计和验证。该系统旨在支持领域科学家进行知识发现，但也通过说明性设计支持数据分析来吸引普通用户。显示器看起来更自然，因为它使用熟悉的图形设计范例来构建说明性的可视化。该项目网页（http：//www.cs.sunysb.edu/englishmueller/illustratorND）提供了关于正在取得的进展的信息，邀请参与用户研究，并在该软件的网络版中提供了一些数据分析能力。该项目为学生提供教育和研究机会。

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.