A computer-based platform for visualization and interactive analysis of complex data

基于计算机的复杂数据可视化和交互式分析平台

• 批准号: RGPIN-2015-06601
• 负责人: Hu, Yaoping
• 金额: $ 1.6万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=678568
• 关键词: computer; based; platform; visualization; interactive

Scientific/engineering data are becoming increasingly complex and massive. Typically, such complex data are represented discretely on large grids over space and time. For decision-making, investigations based on these data need to critically analyze data attributes and their interrelations. Routinely, human experts in different disciplines undertake such analyses collaboratively in a co-located setting. The characteristics of the data imposes, however, difficulties for experts' analyses.  Current practice for analyzing complex data relies mainly on visualization, which results in visual clutter and occlusion overloading the human cognition.  Common methods for interacting with visualized data are inadequate to meet the socio-human needs, causing ineffective collaboration. These difficulties are a bottle-neck issue, which deters experts from making use of complex data in decision-making. Hence, this research program aims to create enabling technologies to target this issue.  Its five-year objective is to catalyze a computer-based virtual environment (VE) platform, which facilitates the experts to visualize and interact with key patterns of the data for collaborative analyses. As outcomes of the program, the vital enablers of the platform are new algorithms for recognizing key patterns reliably and rendering the patterns effectively to ensure intuitive visualization and interaction. Using complex flow data as a heuristic case, the creation of the algorithms is an interdisciplinary endeavor in collaboration with engineering scientists of fluid dynamics at both national and international levels. ******It is generally challenging to recognize key patterns of complex data; to depict the patterns by complying with the human cognitive constraints; and to analyze interrelations among the patterns. The research program meets the challenge through two interwoven approaches: (1) integrating pattern recognition with visualization and interaction; and (2) considering the human cognitive constraints and socio-human needs. From this program, two novelties arise: one is to compute dynamics of a data domain independently from other domains, permitting versatile applications of the recognition algorithms to identify key patterns. Another is to represent energy associated to the key patterns by using haptic (pertinent to the sense of touch) feedback, prompting a unique way of stimulating the human senses to achieve cognitive responses. Although based on flow data, the creation of both recognition and rendering algorithms signifies the solution to the bottle-neck issue for experts' analyses of complex data. This solution enables the VE platform to be tailored for experts' actions including: flood mitigation for planning public safety; improving design processes for boosting industrial competitiveness; and effective surgical training/planning for innovative medical treatments.

科学/工程数据变得越来越复杂和庞大。通常，这种复杂的数据在空间和时间上离散地表示，基于这些数据的调查需要严格分析数据属性及其相互关系。不同学科的专家在同一地点协作进行此类分析，但是，数据的特性给专家分析带来了困难，目前分析复杂数据的实践主要依赖于可视化。与可视化数据交互的常用方法不足以满足社会人类的需求，导致协作效率低下，这些困难是专家们在决策中使用复杂数据的瓶颈问题。因此，该研究计划旨在创建解决这一问题的支持技术，其五年目标是促进基于计算机的虚拟环境（VE）平台的发展。作为该计划的成果，该平台的重要推动因素是可靠地识别关键模式并有效地呈现模式以确保使用复杂的直观可视化和交互的新算法。作为一个启发式案例，算法的创建是与国家和国际层面的流体动力学工程科学家合作的跨学科努力********识别复杂数据的关键模式通常是具有挑战性的；通过遵守人类认知限制来描绘模式；以及该研究计划通过两种相互交织的方法来应对挑战：（1）将模式识别与可视化和交互相结合；（2）考虑人类认知限制和社会人类需求。出现：一种是独立于其他域计算数据域的动态，允许识别算法的多种应用来识别关键模式，另一种是通过使用触觉（与触觉相关）反馈来表示与关键模式相关的能量。 ，激发一种独特的方式来刺激虽然基于流数据，但识别和渲染算法的创建意味着解决专家分析复杂数据的瓶颈问题，该解决方案使VE平台能够为专家量身定制。行动包括：规划公共安全的防洪措施；改进设计流程以提高工业竞争力；以及有效的外科培训/创新医疗规划。