Highly interactive visual analytics systems

高度交互的可视化分析系统

• 批准号: 327828-2011
• 负责人: Shaw, Christopher
• 金额: $ 1.75万
• 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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572128
• 关键词: Highly; interactive; visual; analytics; systems

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. 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 hypotheses that may arise from examining the documents. Information visualization tools such as maps are plotted with time-coded events to visualize the movement of people across a city, for example. 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. The drawback of current visual tools is that they do not themselves offer much direct support for more "high level" cognitive activity. 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 re-playable history navigation channel in addition to multiple visual representations of document collections and the entities within them.

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大脑固有的视觉智能来获得以复杂数据为特征的情况的新见解。当创建图形可视化的方法被理解时，视觉分析（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。