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复杂的调查，大型数据集，开放式探索和迭代假设检验。人类分析处理的高度复杂性和潜在的可变性可能使人们难以记住导致形成假设、生成特定数据视图和实现结论的步骤和原理。有一个既定的需要，审查分析过程，许多工具提供可视化这样做，但是，它是不知道如何帮助的工具，为实际目的。特别是，很少有可视化的研究已经评估了有效性的可视化表示的目的，沟通和介绍的分析历史。该项目将探索新的设计，以直观地表示数据分析的历史，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利益。分析过程的有效呈现和沟通对于理解决策背后的基本论点至关重要，视觉呈现将通过理解分析策略及其有效性来促进分析过程的审查。回顾分析方法和策略将使分析人员能够识别现有方法的问题，改进这些方法，并更好地培训新的分析人员和科学家。该项目将为分析过程历史的呈现和交流的可视化设计和评估的广泛努力提供基础，这也被称为分析出处。通过开发新的评价方法，该项目将能够评估来源可视化的具体视觉元素如何有助于成功呈现不同类型的来源信息。这项工作将包括详细收集和编码来源数据样本，这些数据将用作评价可视化的基础。该研究将探索新颖的可视化设计，以呈现不同类型的捕获信息。此外，这项工作将调查自动生成出处可视化的方法，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.