CPA-G&V: Intelligence Augmented Visualization

题目：CPA-G&amp；V智能增强可视化（intelligent Augmented Visualization） pi: Kwan-Liu Ma，美国加州大学戴维斯分校摘要：可视化已经成为许多科学和工程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，但真正有效的系统允许发现数据中意想不到的、往往是微妙的方面。这样的发现只能由那些非常熟悉数据生成的人来完成。在可视化系统中加入向复杂数据分析任务的领域专家学习的能力，可以简化类似的任务，提高系统的利用率和效率。本研究旨在创造这样一种新的可视化技术，预计将大大降低可视化的成本。调查人员研究如何整合情报？将可视化系统用于自动处理简单或重复的任务，并有效地协助用户执行涉及大型、高维数据的复杂任务。用户只需要做出高层次的、面向目标的决策，这使得尖端的可视化技术可以直接为广泛的应用科学家所使用。一个研究任务是为具有代表性的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很高的可视化应用，湍流分析和社会数据分析。另一项关键任务是咨询领域专家，了解可视化任务需求和可视化语言，然后为每个可视化任务设计适当的用户界面。因此，研究结果可以帮助实现真正连贯和可用的可视化系统，并扩大可视化技术潜在用户的基础。

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.