Principles for Scalable Dynamic Visual Analytics

提案编号：0808824标题：可扩展动态视觉分析原理名称：Jagadish， H. V.研究所：伊利诺伊大学密歇根分校摘要：人眼通常能够从呈现良好的数据集中识别有趣的模式和趋势，而计算算法在完成此类任务时可能存在困难。然而，无论是对象和属性方面的数据集规模，还是随时间的动态变化，人类的能力都是有限的。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可视化分析。这个项目的智力优势在于开发了一组操作符，用于减少要可视化的数据集的对象和属性的大小；分析了该系列操作器的特性，使其能够有效使用；并开发算法和数据结构来支持ef&amp；这些运算符的客户计算。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网络数据无处不在的多个应用程序领域。特别是，我们计划重点关注两个领域来说明所提议的框架；生物学通过蛋白质相互作用网络，国家情报通过可疑参与者的社会网络。此外，这个跨学科的项目在统计学和计算机科学的边界上打下了基础，并在这个界面上培养研究生，这是一个具有巨大未来潜力的领域。

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.