Modeling the Uncertainty Due to Data/Visual Transformations Using Sensitivity Analysis

使用敏感性分析对数据/视觉转换引起的不确定性进行建模

• 批准号: 1025269
• 负责人: Kwan-Liu Ma
• 金额: $ 31.69万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025269&HistoricalAwards=false
• 关键词: Modeling; Uncertainty; Due; Data; Visual

In this project, the investigators study the fundamental aspects ofincorporating uncertainty with sensitivity analysis in the visualanalytics process. They also aim to develop novel and scalablevisual representations of sensitivity, from the visualization of theraw sensitivity coefficients to visual summaries of multivariatederivatives obtained from the analysis. Uncertainty-aware visualanalytics helps enhance analysts' confidence levels on the insightgained from the analysis. Furthermore, it gives toolmakers amethodology for measuring and comparing the robustness ofdata and visual transformations. Sensitivity coefficients of data andvisual transformations are useful for discovering the factors that mostlycontribute to output variability, identifying stability regions of thedifferent transformations within the original data space, andtelling the analyst what the interaction is between variables,outputs and transformations.Uncertainty is introduced throughout the process of data generation,transformation, and analysis in most real-world applications. The ability toincorporate uncertainty into visual analysis is therefore critically importantfor insightful reasoning and key decision making. This project will have awide-reaching impact on those areas relying on the ability to reasonabout large amounts of data. On one hand, it suggests to provide a variationalview of the visual analytics process, which opens up new directions andparadigms for visual data analysis and mining. On the other hand,the improved understanding of the visual analytics process will helpestablish the field as a scientific discipline.

在这个项目中，研究人员研究了在可视化分析过程中将不确定性与敏感性分析相结合的基本方面。他们还致力于开发新的和可扩展的视觉表示的敏感性，从可视化的法律敏感性系数的视觉总结的多元衍生物从分析。 不确定性感知的可视化分析有助于提高分析师对从分析中获得的洞察力的信心水平。此外，它还为工具制造者提供了一种方法来衡量和比较数据和视觉转换的鲁棒性。数据和可视化转换的敏感性系数对于发现对输出可变性贡献最大的因素、识别原始数据空间中不同转换的稳定区域以及告诉分析人员变量、输出和转换之间的相互作用是什么非常有用。在大多数实际应用中，不确定性贯穿于数据生成、转换和分析的整个过程。因此，将不确定性纳入视觉分析的能力对于有洞察力的推理和关键决策至关重要。该项目将对那些依赖于对大量数据进行推理的能力的领域产生广泛的影响。一方面，它建议提供可视化分析过程的变化视图，这为可视化数据分析和挖掘开辟了新的方向和范式。另一方面，对可视化分析过程的更好理解将有助于将该领域建立为一门科学学科。