Highly Interactive Visual Analytics

高度互动的视觉分析

• 批准号: RGPIN-2016-05739
• 负责人: Shaw, Christopher
• 金额: $ 1.6万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709942
• 关键词: Highly; Interactive; Visual; Analytics

Visual Analytics (VA) is defined as the science of analytical reasoning facilitated by interactive visual interfaces. In contrast to computation alone, visualization can harness the human mind's innate visual intelligence to gain novel insights into situations characterized by complex data that may contain uncertainty in fact, or time and location of occurrence. While methods for creating graphical visualizations are understood, visual analytics (VA) directly addresses the ultimate goal of this process: human reasoning and analysis about complex situations. In this proposal, I aim to develop a new architectural model of Visual Analytics software that more directly takes into account the various cognitive activities that analysts engage in during the analysis of large collections of documents. Currently, established practice for examining large data collections is to use information retrieval tools to analyze a document set for trends and to search for evidence that supports or does not support hypotheses that may arise from examining the documents. Information visualization tools such as node-link diagrams are used to plot connections between entities in documents, and maps are plotted with time-coded events to visualize the movement of people across a city, to name a few examples. The strength of these visual tools is that they supply a means of external cognition: they augment internal cognitive processes with concrete spatial representations of relationships of interest that can be referred to later for further cognitive activity. The drawback of current visual tools is that they do not themselves offer much direct support for more "high level" cognitive activity. For example, much analytical work requires the pursuit of a number of informal hypotheses that must be tested and compared with other possible hypotheses for confirmation or disconfirmation. Moreover, many significant analysis tasks take place over a long period of time and must be shared among many people. In my discussions with intelligence analysts, there has been a frequent call for systems that are capable of recapitulating key points of past analyses, and for the re-use of analytical steps on new or updated data. This proposal focuses on the design and development of new models and software to aid in the analysis process. The first new element of this work is to provide an editable and manageable specification system for visual analysis that can manage the analytical process in addition to displaying multiple visual representations of document collections and the entities within them. Second, this system will allow analysts to compare, analyze and share analytical strategies with their colleagues, to enable the delivery of multiple levels of analytical provenance.

视觉分析（VA）定义为交互式视觉界面促进的分析推理科学。与仅计算相反，可视化可以利用人类思想的先天视觉智能，以获取以复杂数据为特征的情况的新颖见解，这些数据可能包含实际上不确定性或发生的时间和位置。尽管了解创建图形可视化的方法，但视觉分析（VA）直接解决了此过程的最终目标：关于复杂情况的人类推理和分析。 在此建议中，我旨在开发一种新的视觉分析软件建筑模型，该模型更直接地考虑了分析师在大量文档集合期间参与的各种认知活动。目前，研究大型数据收集的既定实践是使用信息检索工具来分析设置趋势的文档，并寻找支持或不支持通过检查文档可能引起的假设的证据。信息可视化工具（例如节点链接图）用于绘制文档中实体之间的连接，并用时间编码的事件绘制地图，以可视化人们在城市中的运动，以举一些示例。 这些视觉工具的优势在于它们提供了一种外部认知的手段：它们以具体的空间表示感兴趣的关系增强了内部认知过程，以稍后可以提及以供进一步的认知活动。当前的视觉工具的缺点是，它们本身并没有为更多的“高水平”认知活动提供太大的直接支持。例如，许多分析工作需要追求许多非正式假设，这些假设必须进行测试，并将其与其他可能的假设进行比较，以确认或拒绝确认。此外，许多重大的分析任务发生在很长一段时间内，必须在许多人中共享。在我与情报分析师的讨论中，经常呼吁能够概括过去分析的关键点的系统，并在新数据或更新数据上重新使用分析步骤。 该建议着重于新模型和软件的设计和开发，以帮助分析过程。这项工作的第一个新元素是为视觉分析提供可编辑且易于管理的规范系统，该系统除了显示文档集合和其中的实体的多个视觉表示外，还可以管理分析过程。其次，该系统将允许分析师与同事进行比较，分析和共享分析策略，从而实现多个级别的分析出处。