CAREER: Context-Aware Visual Analytics Systems: Evolving the One-Size-Fits-All Approach to Design and Evaluation

职业：上下文感知视觉分析系统：发展通用的设计和评估方法

基本信息

批准号：
2142977
负责人：
Alvitta Ottley
金额：
$ 52.82万
依托单位：
Washington University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2027-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2142977&HistoricalAwards=false
关键词：
CAREER Context Aware Visual Analytics

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Large-scale data analysis is increasingly vital in domains from national security to e-commerce. Consequently, we have seen an increasing number of analysis and visualization tools to provide automated support for human analysts. However, the current tools tend to offer support with a “one size fits all” approach that fails to consider differences in analysts’ cognitive skills and styles, affecting both how they do their jobs and how well they do them. This project envisions visual analytics systems that sense the user and her goals to actively support her analysis by providing the right information at the right time. The work will produce: (1) a theoretical understanding of how individual differences impact analytic workflows and which traits are most predictive; (2) algorithms for analyzing a user’s interactions with visualizations to infer their cognitive profile and analysis goals; and (3) techniques and theory for using those inferences to give more personalized suggestions. These three aims help facilitate a human-technology partnership where a better understanding of the analyst enables a more practical machine partner, leading to greater exploration of the data space and more efficient hypothesis generation and decision-making. This project systematically investigates the impact of individual differences on a broad range of tasks and visualization designs. The general approach involves leveraging low-level behavioral data such as mouse interactions to model the user’s cognitive profile, attention, and workflow. These data and models will allow the researchers to assess and predict cognitive traits that affect analysts’ visualization strategies and effectiveness. Based on these models, the research team will develop context-aware visual analytics tools that offer personalized guidance and suggestions based on the learned user characteristics and goals, calibrating the type and timing of assistance provided based on a given user profile and workflow. Altogether, this research agenda incorporates situational context into the design and evaluation pipelines to create broadly usable visualization tools. It develops new theoretical foundations, algorithms, and techniques that would result in a more symbiotic relationship between the human and the visual analytic tool during data analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项是根据2021年《美国救援计划法》的全部或部分资助的（公共法117-2）。规模数据分析在从国家安全到电子商务的领域中越来越重要。因此，我们看到了越来越多的分析和可视化工具为人类分析师提供自动支持。但是，当前的工具倾向于提供“适合所有尺寸”方法的支持，该方法未能考虑分析师的认知技能和样式上的差异，从而影响了他们的工作方式以及他们的工作方式。该项目设想了视觉分析系统，这些系统感知用户及其目标，以通过在正确的时间提供正确的信息来积极支持她的分析。这项工作将产生：（1）对个体差异如何影响分析工作流以及哪些特征最可预测性的理论理解；（2）用于分析用户与可视化的相互作用以推断其认知概况和分析目标的算法；（3）使用这些推论提供更多个性化建议的技术和理论。这三个目标有助于支持人类技术合作伙伴关系，在该伙伴关系中，更好地了解分析师可以使一个更实用的机器合作伙伴，从而更好地探索数据空间和更有效的假设产生和决策。该项目系统地研究了个体差异对广泛任务和可视化设计的影响。一般方法涉及利用低级行为数据，例如鼠标相互作用来对用户的认知概况，注意力和工作流进行建模。这些数据和模型将使研究人员能够评估和预测影响分析师可视化策略和有效性的认知特征。基于这些模型，研究团队将开发上下文感知的视觉分析工具，这些工具基于学习的用户特征和目标提供个性化的指导和建议，从而根据给定的用户资料和工作流程校准提供的援助类型和时机。总的来说，该研究议程将情境环境纳入了设计和评估管道中，以创建广泛可用的可视化工具。它开发了新的理论基础，算法和技术，这些基础将在数据分析期间在人与视觉分析工具之间产生更共生的关系。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响来评估来获得支持的支持。