Faithful Visual Analytics: models, metrics and algorithms

忠实的视觉分析：模型、指标和算法

• 批准号: DP190103301
• 负责人: Prof Seok-Hee Hong
• 金额: $ 29.52万
• 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: 2019
• 资助国家: 澳大利亚
• 起止时间: 2019-06-19 至 2024-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP190103301
• 关键词: Faithful; Visual; Analytics; models; metrics

This project aims to deliver new models, metrics and algorithms for Faithful Visual Analytics of complex data. For a purported visual representation of some data, "faithfulness" measures how accurately the visual representation describes the data. This project will develop new models for Faithful Visual Analytics, design new faithfulness metrics for faithful visual analytics of complex networks, design new algorithms to compute faithful visualisations, and evaluate using real world social network and biological network data sets. The new models, metrics and algorithms produced by this project will be used in the next generation Visual Analytic tools to enable analysts develop accurate insights and new knowledge of complex data.

该项目旨在为复杂数据的忠实视觉分析提供新的模型、指标和算法。对于某些数据的所谓视觉表示，“忠实性”衡量视觉表示对数据的描述有多准确。该项目将为忠实视觉分析开发新的模型，为复杂网络的忠实视觉分析设计新的忠诚度度量，设计新的算法来计算忠实可视化，并使用真实世界的社交网络和生物网络数据集进行评估。该项目产生的新模型、指标和算法将用于下一代可视化分析工具，使分析师能够对复杂数据发展准确的洞察力和新知识。