Principles for Scalable Dynamic Visual Analytics

提案编号：0808824标题：可伸缩动态视觉分析原则PI名称：Jagadish，H.V.Inst：密歇根伊利诺伊大学摘要：人眼通常能够从良好呈现的数据集中识别感兴趣的模式和趋势，而计算算法在这样的任务上可能会有困难。然而，人类的能力是有限的，无论是对象和属性方面的数据集的规模，还是随着时间的动态变化。该项目开发了一个分析和计算框架，以支持对具有网络结构的大规模动态数据的可视化分析。该项目在智力上的价值在于开发了一系列运算符，用它来减少要可视化的数据集的对象和属性的大小；分析这类运算符的属性以使它们能够有效地使用；以及开发算法和数据结构来支持这些运算符的有效计算。通过利用计算能力来帮助人眼看到数据中的模式和趋势，该项目有可能改变当今分析具有网络结构的大型动态数据集的方式。该项目更广泛的影响在于多个应用领域，在这些领域中，网络数据无处不在。特别是，我们计划将重点放在两个领域来说明拟议的框架：通过蛋白质相互作用网络的生物学，以及通过可疑参与者的社会网络的国家情报。此外，这个跨学科项目在统计学和计算机科学的边界上开拓进取，并在这个具有巨大未来潜力的领域培养研究生。

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.