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

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。从国家安全到电子商务等领域，大规模数据分析越来越重要。因此，我们已经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，目前的工具倾向于以“一刀切”的方式提供支持，这种方式没有考虑到分析师认知技能和风格的差异，这既影响了他们的工作方式，也影响了他们的工作表现。这个项目设想的可视化分析系统可以感知用户和他们的目标，通过在正确的时间提供正确的信息来积极地支持他们的分析。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)分析用户与可视化交互的算法，以推断他们的认知概况和分析目标；(3)利用这些推论给出更个性化建议的技术和理论。这三个目标有助于促进人类与技术的合作伙伴关系，更好地了解分析人员可以使机器合作伙伴更加实用，从而对数据空间进行更大的探索，并更有效地生成假设和决策。这个项目系统地研究了个体差异对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）来模拟用户的认知概况、注意力和工作流程。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，研究团队将开发情境感知可视化分析工具，根据了解到的用户特征和目标提供个性化指导和建议，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。总之，该研究议程将情景上下文纳入设计和评估管道，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，在数据分析过程中，这些将导致人类和可视化分析工具之间更加共生的关系。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.