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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=664919
• 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.

可视化分析（Visual Analytics，VA）被定义为通过交互式可视化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大脑天生的视觉智能，以获得对复杂数据所表征的情况的新见解，这些复杂数据可能包含事实上的不确定性，或者发生的时间和位置。虽然创建图形化可视化的方法已经被理解，但可视化分析（VA）直接解决了这个过程的最终目标：人类对复杂情况的推理和分析。** 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到分析人员在分析大量文档时所从事的各种认知活动。目前，检查大型数据集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可能从检查文档中产生的假设的证据。信息可视化工具（如节点链接图）用于绘制文档中实体之间的连接，地图使用时间编码事件绘制，以可视化城市中人员的移动，仅举几例。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：它们用感兴趣的关系的具体空间表征来增强内部认知过程，这些空间表征可以在以后的认知活动中参考。目前视觉工具的缺点是，它们本身并不能为更“高层次”的认知活动提供直接的支持。例如，许多分析工作需要追求一些非正式的假设，这些假设必须经过检验，并与其他可能的假设进行比较，以确认或不确认。此外，许多重要的分析任务需要很长一段时间，必须由许多人分担。在我与情报分析人员的讨论中，经常有人呼吁建立能够概括过去分析要点的系统，并在新的或更新的数据上重新使用分析步骤。该提案侧重于设计和开发新的模型和软件，以协助分析过程。这项工作的第一个新元素是提供一个可编辑和可管理的规范系统，用于可视化分析，除了显示文档集合和其中实体的多个可视化表示之外，还可以管理分析过程。第二，该系统将允许分析人员与同事比较、分析和分享分析战略，以便能够提供多层次的分析来源。