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

在这个项目中，研究人员研究了在可视化分析过程中结合不确定性和敏感性分析的基本方面。他们还致力于开发新的和可扩展的灵敏度视觉表示，从可视化的治疗敏感性系数到从分析中获得的多变量导数的视觉摘要。不确定性感知可视化分析有助于提高分析师从分析中获得的洞察力的信心水平。此外，它还为工具制造商提供了测量和比较数据和视觉转换的鲁棒性的方法。数据和可视化转换的敏感性系数对于发现主要导致输出可变性的因素，识别原始数据空间内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有用的。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，将不确定性纳入可视化分析的能力对于深刻的推理和关键决策至关重要。该项目将对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，提出了可视化分析过程的变异视角，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改进理解将有助于将该领域建立为一门科学学科。

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.