Scalable Visual Analytics for Uncertain Dynamic Networks

针对不确定动态网络的可扩展可视化分析

• 批准号: DP0988838
• 负责人: Prof Peter Eades
• 金额: $ 18.24万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2009
• 资助国家: 澳大利亚
• 起止时间: 2009-03-18 至 2012-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0988838
• 关键词: Scalable; Visual; Analytics; Uncertain; Dynamic

Technological advances have provided a data deluge over the past few years, and have led to many large uncertain and dynamic network models. This includes terrorist networks, marketing networks, facebook networks, various biological networks, and software engineering structures. Human understanding of such networks is difficult. This project aims to provide new methods for visual analysis of large uncertain dynamic networks such as these. The algorithms developed in the project will help security analysts to monitor illegal behaviour such as money laundering and terrorist activities, help biologists understand key biological systems, and help engineers to understand large software systems.

过去几年，技术进步带来了海量的数据，并产生了许多大型的不确定性和动态网络模型。这包括恐怖分子网络、营销网络、Facebook 网络、各种生物网络和软件工程结构。人类理解这样的网络是很困难的。该项目旨在为此类大型不确定动态网络的可视化分析提供新方法。该项目开发的算法将帮助安全分析师监控洗钱和恐怖活动等非法行为，帮助生物学家了解关键生物系统，并帮助工程师了解大型软件系统。