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

科学/工程数据正变得越来越复杂和海量。通常，这样的复杂数据在空间和时间的大网格上被离散地表示。对于决策，基于这些数据的调查需要批判性地分析数据属性及其相互关系。通常，不同学科的人类专家在同一地点的环境中协作进行这样的分析。然而，数据的特点给专家的分析带来了困难。目前复杂数据的分析主要依赖可视化，这导致视觉混乱和遮挡超载了人类的认知。常用的可视化数据交互方法不足以满足社会和人类的需求，导致协作效率低下。这些困难是一个瓶颈问题，这也阻碍了专家在决策中使用复杂的数据。因此，该研究计划旨在创建使能技术来针对这一问题。其五年目标是催化一个基于计算机的虚拟环境(VE)平台，该平台便于专家可视化数据的关键模式并与其进行交互，以进行协作分析。作为该计划的结果，该平台的关键推动因素是新的算法，用于可靠地识别关键模式并有效地渲染模式，以确保直观的可视化和交互。使用复杂的流动数据作为启发式案例，算法的创建是与国内和国际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，通过遵守人类认知约束来描述模式，以及分析模式之间的相互关系，通常都是具有挑战性的。该研究项目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。从这个程序中，产生了两个新颖性：一个是独立于其他域计算数据域的动力学，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)反馈来表现与关键模式相关的能量，促使一种独特的方式刺激人类感官实现认知反应。虽然基于流数据，但识别和渲染算法的创建意味着解决了专家分析复杂数据的瓶颈问题。该解决方案使VE平台能够为专家的行动量身定做，包括：用于规划公共安全的洪灾缓解；提高行业竞争力的改进设计流程；以及针对创新医疗的有效外科培训/规划。

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.