Scalable Visualization and Constrained Interaction for Large Graphs -- Supporting the Collaborative Analysis of High-dimensional Data Sets

大图的可扩展可视化和约束交互——支持高维数据集的协同分析

• 批准号: 0635492
• 负责人: Tobias Hollerer
• 金额: --
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-15 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0635492&HistoricalAwards=false
• 关键词: Scalable; Visualization; Constrained; Interaction; Large

In this research project, the PI proposes to develop novel methodology to visualize and interact with large interconnected high-dimensional datasets represented as large unstructured graphs. The underlying semantics for the nodes and edges in such a graph are to be kept flexible, but the ultimate goal is for an analyst to acquire an understanding of the data universe as a whole, and to enable him or her to issue and resolve specific data queries. An analyst should also have an easy way to inform a colleague about the insights gathered during their own interaction with the system, including evidence chains and new hypotheses.

在这个研究项目中，PI提出开发一种新的方法来可视化和交互表示为大型非结构化图的大型互连高维数据集。这种图中节点和边的底层语义保持灵活，但最终目标是让分析师从整体上了解数据宇宙，并使他或她能够发出和解决特定的数据查询。分析师还应该有一个简单的方法来告知同事他们在与系统交互过程中收集的见解，包括证据链和新的假设。