I-Corps: Filtergraph - A fast and intuitive data visualization interface for massive datasets

I-Corps：Filtergraph - 适用于海量数据集的快速直观的数据可视化界面

• 批准号: 1443314
• 负责人: Keivan Stassun
• 金额: $ 5万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1443314&HistoricalAwards=false
• 关键词: Corps; Filtergraph; fast; intuitive; data

Increasingly in basic research and many other sectors, there is a need for quick-look visualization of large datasets in order to (a) conceptualize the nature of the data, and (b) begin identifying data patterns to guide more computationally costly deep-dive analysis. High-end tools for data basing and graphing exist, but usually entail a large overhead in time and effort associated with: importing the data; correctly specifying meta-data; keeping track of a large number of variable names; issuing and scripting commands for graphing; plotting pairwise variables over many iterations; filtering out bad data; re-rendering plots to restricted data ranges; representing multiple variables at once; and so on. This team has developed Filtergraph, a web based tool that allows rapid and intuitive visualization of massive, multi-dimensional datasets. The web-based tools developed by this team will allow researchers to instantly visualize any dataset in many dimensions at once, and will permit researchers to move through the data rapidly, and quickly filter and slice them. This visual interaction will occur without the need for cumbersome meta-data specification, without the need to remember details of the data structure, and without the need to write scripts to render sophisticated visualizations. The tools developed will be a means to an end: A new, highly visual way of transcending preconceptions about the data and instead let the data speak for themselves. This mechanism would free researchers to creatively and collaboratively engage their expert intuition as they visually and rapidly explore massive datasets for unexpected patterns or outliers that signal important discovery.

在基础研究和许多其他部门，越来越需要快速查看大型数据集的可视化，以便（a）概念化数据的性质，（B）开始识别数据模式，以指导计算成本更高的深入分析。 存在用于数据基础和图形化的高端工具，但通常需要大量的时间和工作开销，这些开销与以下相关：导入数据;正确指定元数据;跟踪大量变量名称;发布和编写图形化命令;在多次迭代中绘制成对变量;过滤掉不良数据;将图重新绘制到有限的数据范围;一次表示多个变量;这个团队已经开发了一个基于网络的工具，可以快速直观地可视化大量的多维数据集。该团队开发的基于网络的工具将允许研究人员立即在多个维度上可视化任何数据集，并允许研究人员快速浏览数据，并快速过滤和切片。这种可视化交互将在不需要繁琐的元数据规范的情况下发生，不需要记住数据结构的细节，也不需要编写脚本来呈现复杂的可视化。开发的工具将是实现目标的一种手段：一种新的、高度可视化的方式，超越对数据的先入之见，让数据自己说话。这种机制将使研究人员能够创造性地和协作地利用他们的专家直觉，因为他们可以直观地快速探索大量数据集，以获得意想不到的模式或异常值，这些模式或异常值标志着重要的发现。