CPA-G&V: Intelligence Augmented Visualization

标题：CPA-G&V Intelligence Augmented VisualizationPI：Kwan-Liu Ma，加州大学戴维斯分校摘要：可视化已成为科学和工程许多领域不可或缺的工具。先进的可视化技术使科学家能够查看和探索他们的计算结果，但真正有效的系统可以发现数据中意想不到且通常微妙的方面。 只有那些非常熟悉数据生成的人才能做出这样的发现。 在可视化系统中添加在复杂数据分析任务中向领域专家学习的能力可以促进类似的任务并提高系统的利用率和效率。这项研究旨在创造一种新的可视化技术，有望显着降低可视化成本。研究人员研究如何整合“智能”。集成到可视化系统中，自动处理简单或重复的任务，并有效地帮助用户执行涉及大、高维数据的复杂任务。用户只需要做出高层次的、以目标为导向的决策，从而使广泛的应用科学家可以直接使用尖端的可视化技术。 一项研究任务是为代表性可视化任务选择合适的机器学习方法。本研究使用了两种要求较高的可视化应用程序：湍流分析和社会数据分析。 另一个关键任务是咨询领域专家以了解可视化任务要求和视觉语言，然后为每个可视化任务设计适当的用户界面。 因此，研究结果有助于实现真正连贯且可用的可视化系统，并扩大可视化技术的潜在用户基础。

Title: CPA-G&V Intelligence Augmented VisualizationPI: Kwan-Liu Ma, University of California at DavisAbstract:Visualization has become an indispensable tool in many areas of science and engineering. Advanced visualization techniques allow scientists to view and explore their computational results, but truly effective systems allow the discovery of unexpected and often subtle aspects of the data. Such discoveries can only be made by those intimately familiar with the generation of the data. Adding to the visualization system the capability to learn from the domain experts in complex data analysis tasks can facilitate similar tasks and increase the utilization and efficacy of the system. This research aims to create such a new visualization technology that is anticipated to significantly lower the cost of visualization.The investigators study how to integrate ?intelligence? into visualization systems to automatically handling simple or repetitive tasks, and to effectively assist users in performing complex tasks involving large, high-dimensional data. Only high-level, goal-oriented decisions need to be made by the user, making cutting-edge visualization technology directly accessible to a wide range of application scientists. One research task is to select suitable machine learning methods for representative visualization tasks. Two demanding visualization applications, turbulent flow analysis and social data analysis, are used in this study. The other critical task is to consult domain experts for understanding the visualization task requirements and visual language, followed by the design of an appropriate user interface for each visualization task. The research results can therefore help realize truly coherent and usable visualization systems and broaden the base of potential users of visualization technology.