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

越来越多地在基础研究和许多其他部门，需要对大型数据集进行快速可视化，以便(A)概念化数据的性质，(B)开始确定数据模式，以指导计算成本更高的深海分析。存在用于基于数据和绘制图形的高端工具，但通常在以下方面需要大量的时间和工作开销：导入数据；正确指定元数据；跟踪大量变量名称；发布和编写用于绘制图形的命令；在多次迭代中绘制成对变量；过滤坏数据；重新绘制受限数据范围的图形；一次表示多个变量；等等。该团队开发了Filtergraph，这是一个基于Web的工具，可以快速直观地显示海量的多维数据集。该团队开发的基于网络的工具将允许研究人员立即可视化多个维度的任何数据集，并允许研究人员快速浏览数据，并快速对其进行过滤和切片。这种可视化交互不需要繁琐的元数据规范，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效果。开发的工具将是达到目的的一种手段：一种新的、高度可视化的方式，超越对数据的先入为主的概念，让数据自己说话。这一机制将解放研究人员，使他们能够创造性地和协作地利用他们的专家直觉，因为他们可视化地、快速地探索海量数据集，寻找标志着重要发现的意外模式或离群值。

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.