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）被定义为通过交互式视觉界面促进分析推理的科学。与单独的计算相比，可视化可以利用人类思维与生俱来的视觉智能，对以复杂数据为特征的情况获得新颖的见解，这些数据可能包含事实或发生的时间和地点的不确定性。虽然创建图形可视化的方法已被理解，但视觉分析 (VA) 直接解决了此过程的最终目标：对复杂情况的人类推理和分析。 在本提案中，我的目标是开发一种新的可视化分析软件架构模型，该模型更直接地考虑分析师在分析大量文档期间参与的各种认知活动。目前，检查大型数据集合的既定做法是使用信息检索工具来分析文档集的趋势，并搜索支持或不支持检查文档时可能出现的假设的证据。诸如节点链接图之类的信息可视化工具用于绘制文档中实体之间的连接，并且使用时间编码事件绘制地图以可视化人们在城市中的移动，仅举几个例子。 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：它们通过兴趣关系的具体空间表征来增强内部认知过程，这些关系可以在以后进行进一步的认知活动时参考。当前视觉工具的缺点是它们本身并不为更“高级”的认知活动提供太多直接支持。例如，许多分析工作需要追求许多非正式的假设，这些假设必须经过测试并与其他可能的假设进行比较以确认或否定。此外，许多重要的分析任务需要很长一段时间才能完成，并且必须由许多人分担。在我与情报分析师的讨论中，经常有人呼吁系统能够概括过去分析的要点，并能够对新的或更新的数据重复使用分析步骤。 该提案侧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可视化分析提供可编辑和可管理的规范系统，除了显示文档集合及其中的实体的多个可视化表示之外，该系统还可以管理分析过程。其次，该系统将允许分析师与同事比较、分析和共享分析策略，从而实现多层次的分析来源。