Principles for Scalable Dynamic Visual Analytics

可扩展动态视觉分析的原则

• 批准号: 0808824
• 负责人: Hosagrahar Jagadish
• 金额: $ 45万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0808824&HistoricalAwards=false
• 关键词: Principles; Scalable; Dynamic; Visual; Analytics

Proposal No: 0808824 Title: Principles for Scalable Dynamic Visual AnalyticsPI name: Jagadish, H. V. Inst: University of Illinois MichiganAbstract:The human eye is often capable of identifying interesting patterns and trends from a well-presented data set, whereas computational algorithms may have difficulties with such a task. Yet, there are limits to human ability, both with the scale of the data set in terms of objects and attributes and with dynamic changes over time. This project develops an analytic and computational framework to support the visual analysis of large-scale dynamic data with network structure. The intellectual merit of this project is in the development of a family of operators with which to reduce the size both in terms of objects and attributes of the data set to be visualized; an analysis of the properties of this family of operators to enable their effective use; and the development of algorithms and data structures to support the efficient computation of these operators. By harnessing computational power to assist the human eye in seeing patterns and trends in the data, this project has the potential to transform the way in which large dynamic data sets with network structure are analyzed today. The broader impact of the project lies in the multiple application domains where network data are ubiquitous in their presence. In particular, we plan to focus on two domains to illustrate the proposed framework; biology through protein interaction networks, and national intelligence through social networks of suspect participants. In addition, this interdisciplinary project plows the ground at the boundary of statistics and computer science, and trains graduate students at this interface, an area with great future potential.

提案编号：0808824标题：可扩展动态视觉分析原理名称：Jagadish，H。V. Inst：伊利诺伊州密歇根州大学密歇根州大学：人眼通常能够从良好的数据集中识别出有趣的模式和趋势，而计算算法可能很难与此类任务相处。然而，人类能力的局限性，无论是按照对象和属性而言，数据集的规模以及随着时间的推移而动态的变化。该项目开发了一个分析和计算框架，以支持具有网络结构的大规模动态数据的目视分析。该项目的智力优点在于开发了一个经营者家族，该家族通过将要可视化的数据集的对象和属性减少尺寸；对该运营商家族的性质进行分析，以使其有效使用；以及算法和数据结构的开发，以支持这些操作员的EF＆＃64257; CIENT计算。通过利用计算能力来帮助人眼查看数据中的模式和趋势，该项目有可能改变今天分析具有网络结构的大型动态数据集的方式。该项目的更广泛影响在于多个应用领域，在这些域中，网络数据在其存在下无处不在。特别是，我们计划专注于两个领域，以说明所提出的框架。通过蛋白质相互作用网络的生物学和通过可疑参与者的社交网络的国家情报。此外，这个跨学科项目在统计和计算机科学的边界上耕种了基础，并在该界面上训练研究生，该界面具有巨大的未来潜力。