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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。大规模数据分析在从国家安全到电子商务的各个领域越来越重要。因此，我们看到越来越多的分析和可视化工具为人类分析师提供自动化支持。然而，目前的工具往往以一种“一刀切”的方式提供支持，这种方法未能考虑分析师认知技能和风格的差异，既影响他们的工作方式，也影响他们的工作表现。这个项目设想的视觉分析系统能够感知用户和她的目标，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程以及哪些特征最具预测性的理论理解；(2)分析用户与可视化的交互以推断他们的认知概况和分析目标的算法；以及(3)使用这些推断提供更个性化建议的技术和理论。这三个目标有助于促进人-技术合作伙伴关系，其中对分析师的更好理解有助于更实际的机器合作伙伴，从而导致对数据空间的更大探索以及更高效的假设生成和决策。这个项目系统地调查了个体差异对广泛的任务和可视化设计的影响。一般的方法包括利用低级行为数据(如鼠标交互)对用户的认知配置文件、注意力和工作流程进行建模。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。在这些模型的基础上，研究团队将开发情景感知视觉分析工具，根据了解到的用户特征和目标提供个性化指导和建议，并根据给定的用户配置文件和工作流程校准提供帮助的类型和时间。总而言之，本研究议程将情景背景融入到设计和评估管道中，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，将在数据分析过程中导致人与视觉分析工具之间更多的共生关系。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.