AF:Small:Algorithms for visualizing data with contact graphs and data maps

AF:Small：使用接触图和数据图可视化数据的算法

• 批准号: 1115971
• 负责人: Stephen Kobourov
• 金额: $ 29.6万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1115971&HistoricalAwards=false
• 关键词: AF; Small; Algorithms; visualizing; data

The goal of this project is to develop new techniques for visualizing static and dynamic data. Visualization allows us to perceive relationships in large interconnected datasets. While statistics may determine correlations among the data, intuitive visualizations of these correlations helps us frame what questions to ask about the data. Moreover, algorithms for visualizing processes that evolve over time will have applications beyond the traditional uses of information visualization in exploratory data analysis and searching for patterns and trends.Graphs are often used to capture relationships between objects and visualized as node-link diagrams. Contact graphs provide an alternative and often more intuitive way to visualize planar graphs. In contact graphs, nodes are represented by regions and edges are represented by two regions sharing a common border. Such representation suggests the familiar metaphor of a geographical map. In this research project the PIs will study characterizations of the classes of graphs that admit contact graph representations where nodes have "nice" shapes (e.g., convex, with small complexity, with predetermined areas) and techniques for creating such representations. While contact graphs are restricted to planar graphs, the PIs will extend the notion to general graphs using clustering and embedding methods.As an integral part of this project, graduate and undergraduate students will be involved in all aspects of the described research activities. This continues a tradition of integrating undergraduates into research projects that are easily accessible and visually appealing. As with past projects, all new software developed will be made available to the broader community.

该项目的目标是开发用于可视化静态和动态数据的新技术。 可视化使我们能够感知大型互连数据集中的关系。 虽然统计数据可以确定数据之间的相关性，但这些相关性的直观可视化有助于我们确定要针对数据提出哪些问题。 此外，随着时间的推移而演变的可视化过程的算法的应用将超出信息可视化在探索性数据分析和搜索模式和趋势中的传统用途。图形通常用于捕获对象之间的关系并可视化为节点链接图。 接触图提供了一种替代且通常更直观的方式来可视化平面图。 在接触图中，节点由区域表示，边由共享公共边界的两个区域表示。 这种表示方式暗示了人们熟悉的地理地图的隐喻。 在这个研究项目中，PI 将研究允许接触图表示的图类的特征，其中节点具有“良好”形状（例如，凸面、复杂性小、具有预定区域）以及创建此类表示的技术。 虽然接触图仅限于平面图，但 PI 将使用聚类和嵌入方法将概念扩展到一般图。作为该项目的组成部分，研究生和本科生将参与所描述的研究活动的各个方面。 这延续了将本科生融入易于访问且具有视觉吸引力的研究项目的传统。 与过去的项目一样，开发的所有新软件都将提供给更广泛的社区。