CRII: III: Evaluating Provenance Visualizations for the Presentation and Communication of Investigative Data Analysis Processes

许多类型的数据分析涉及大型数据集的复杂调查、开放式探索和迭代假设检验。人类分析过程的高度复杂性和潜在的变异性可能会使人很难记住导致假设形成、特定数据视图的生成和结论实现的步骤和原理。存在审查分析过程的既定需求，并且许多工具提供了可视化来实现这一点；然而，这些工具对于实际目的有多大帮助还不是很清楚。特别是，很少有可视化研究评估用于交流和呈现分析历史的视觉表征的有效性。该项目将探索新的设计，以便直观地展示数据分析的历史，并评估不同设计的有效性。可视化设计和经验评估的结果将通过提高审查和交流分析记录的能力，为专业分析师和科学家带来直接好处。分析过程的有效展示和沟通对于理解决策背后的基本论点至关重要，而可视化展示将通过使理解分析战略及其有效性成为可能，从而促进对分析过程的审查。审查分析方法和策略将使分析员能够发现现有方法的问题，改进这些方法，并更好地培训新的分析员和科学家。该项目将为设计和评估可视化的广泛努力提供基础，以展示和交流分析过程的历史，也称为分析起源。通过开发新的评价方法，该项目将能够评估种源可视化的具体视觉要素在多大程度上有助于成功地展示不同类型的种源信息。这项工作将包括对产地数据样本进行详细收集和编码，以此作为评估可视化的基础。这项研究将探索新的可视化设计，以呈现不同类型的捕获信息。此外，这项工作将研究自动生成来源可视化的方法，并将研究不同形式的自动演示的有效性。

Many types of data analysis involve complex investigations with large data sets, open-ended explorations, and iterative hypothesis testing. The high complexity and potential variability in human analytic processing can make it difficult to remember the steps and rationale that led to the formation of hypotheses, the generation of specific data views, and the realization of conclusions. There is an established need for reviewing analytic processes, and many tools provide visualizations to do so; however, it is not well known how helpful the tools are for practical purposes. In particular, little visualization research has evaluated the effectiveness of visual representations for the purposes of communication and presentation of analysis history. The project will explore new designs for visually representing the history of data analysis and evaluating the effectiveness of different designs. The outcomes of the visualization designs and empirical evaluations will yield direct benefits to professional analysts and scientists by improving the ability to review and communicate analysis records. Effective presentation and communication of analytic processes is essential for understanding the underlying arguments behind decisions, and visual presentation will facilitate review of analysis processes by making it possible to understand analytics strategies and their effectiveness. Reviewing analysis approaches and strategies will allow analysts to identify problems with existing methods, improve those methods, and better train new analysts and scientists.This project will provide the foundation for an extensive effort of the design and evaluation of visualizations for the presentation and communication of the history of an analysis process, which is also known as analytic provenance. Through the development of new evaluation methodology, the project will enable the assessment of how well specific visual elements of provenance visualizations contribute to successful presentation of different types of provenance information. The effort will include detailed collection and coding of samples of provenance data that will be used as a basis for evaluating visualizations. The research will explore novel visualization designs for presenting different types of captured information. In addition, the effort will investigate methods to automatically generate provenance visualization, and it will study the effectiveness of different forms of automated presentations.