CAREER: Analyzing Interactions in Visual Analytics for User and Data Modeling

职业：在用户和数据建模的可视化分析中分析交互

• 批准号: 1452977
• 负责人: Remco Chang
• 金额: $ 49.99万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1452977&HistoricalAwards=false
• 关键词: CAREER; Analyzing; Interactions; Visual; Analytics

Visual analytics systems combine human analysis with computational techniques. When the two are fully integrated these next generation human-in-the-loop systems can be tremendously powerful, but current visual analytics systems suffer from a significant communication gap between the human and the computer which prevents them from reaching their full potential. Specifically, there currently exist no generalized techniques to enable the computer to easily and intuitively understand the user's background knowledge, analysis process, or intent during an analysis task. Lacking this, the computer has limited means to identify the user's needs and provide timely and appropriate computational support. The PI's goal in this project is to develop computational techniques to quantify and extract the user's high-level knowledge by analyzing his/her interactions with a visual interface. Toward this end, the research agenda consists of two complementary components: data modeling and user modeling. In data modeling, the user's interactions are used to learn the parameters of an algorithm, computational process, or representation of the data that best reflect the user's domain knowledge about the data. In user modeling, the same interactions are used to infer aspects of the user's reasoning process and cognitive style. Together, the results of these two modeling processes will give the computer the means to better understand the user's analysis process and will enable it to better support the user in performing his/her task.The results of this work will have both immediate and long term impact on the research and application of visual analytics. In the short term new techniques for data and user modeling will advance existing systems and practices in interactive data analysis, while in the long term project outcomes will help establish the foundations of a human+computer approach to visual analytics that more effectively supports the user's analysis process, which in turn will impact the development of real-time, mixed-initiative visual analytics systems for addressing the challenges of big data. In addition, through an integrated research and education agenda, students will be trained with the appropriate balance of expertise in human reasoning and computational science, preparing them to conduct independent interdisciplinary research and lead future efforts in visual analytics.

可视化分析系统将联合收割机人类分析与计算技术相结合。 当两者完全集成时，这些下一代人在回路系统可以非常强大，但目前的视觉分析系统受到人类和计算机之间的显着沟通差距的影响，这使它们无法充分发挥其潜力。 具体地，当前不存在使计算机能够在分析任务期间容易且直观地理解用户的背景知识、分析过程或意图的通用技术。 缺乏这一点，计算机就没有足够的手段来确定用户的需求，并提供及时和适当的计算支持。PI在这个项目中的目标是开发计算技术，通过分析他/她与可视化界面的交互来量化和提取用户的高级知识。 为此，研究议程包括两个互补的组成部分：数据建模和用户建模。 在数据建模中，用户的交互用于学习算法的参数、计算过程或最能反映用户关于数据的领域知识的数据表示。 在用户建模中，相同的交互用于推断用户的推理过程和认知风格的各个方面。 这两个建模过程的结果将使计算机能够更好地理解用户的分析过程，并使其能够更好地支持用户执行任务，这项工作的结果将对可视化分析的研究和应用产生直接和长期的影响。 在短期内，数据和用户建模的新技术将推进交互式数据分析的现有系统和实践，而从长期来看，项目成果将有助于建立人机视觉分析方法的基础，更有效地支持用户的分析过程，这反过来将影响实时，混合主动视觉分析系统，以应对大数据的挑战。 此外，通过一个综合的研究和教育议程，学生将接受培训，在人类推理和计算科学的专业知识的适当平衡，准备他们进行独立的跨学科研究，并在视觉分析的未来努力。