CHS: Small: Enhancing Data Analysis Strategies with Mixed-Initiative Visual Analytics

CHS：小型：通过混合主动可视化分析增强数据分析策略

基本信息

批准号：
1813281
负责人：
Alexander Endert
金额：
$ 49.14万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813281&HistoricalAwards=false
关键词：
CHS Small Enhancing Data Analysis

项目摘要

People make important decisions based on data every day, from simple choices such as which restaurant to dine at when visiting a city, to important and complex decisions in healthcare about which course of treatment to pursue, and even decisions that impact national security and policy. Visual analytic systems play a critical role in these decision-making processes. They allow people to interact with their data and analytic models to view different perspectives of data and gain insights. This interactive data analysis process consists of people incrementally guiding analytic models to produce alternate views of the data in support of their tasks. In most cases, such human-in-the-loop processes have successful, insightful outcomes. However, the cognitive sciences tell us that people can exhibit innate biased behavior. As a result, their data analysis behaviors and strategies may suffer. Ultimately, this could lead to decisions made from incomplete information and limited perspectives on how the data can be interpreted. Biased analysis processes can lead to biased results and misinformation. This project will perform fundamental research to discover how to detect such potential bias and develop visual analytic systems that mitigate it. It will also produce educational impacts for graduate and undergraduate students from groups underrepresented in STEM fields, in part through outreach workshops with instructors from minority-serving institutions and historically black colleges and universities to help them integrate visual analytics and general data literacy learning objectives into course curricula. The proposed multi-disciplinary research will develop techniques that enhance mixed-initiative visual analytic analysis processes by intervening and providing guidance when necessary. To accomplish this goal, three primary lines of research are proposed. First, the team will develop and evaluate computational metrics to detect poor and potentially biased analysis strategies from user interaction patterns and system parameters. These metrics consist of probabilistic computational models that take into consideration metrics such as data coverage over the duration of the data exploration. Second, the team will develop and study different visual analytic system designs to guide and improve people's analysis processes. Each prototype will give people guidance using the metrics, but display information to users via different interface designs (e.g., dialog boxes, visual overlays of coverage, etc.) They will be developed, evaluated, and made available via publications and open-source code. Third, the studies proposed will generate empirical results and design guidelines for future mixed-initiative visual analytic systems.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.

人们每天都根据数据做出重要的决定，从访问城市时要用餐的简单选择，到医疗保健中重要而复杂的决定，就可以追求哪种治疗方法，甚至影响国家安全和政策的决定。视觉分析系统在这些决策过程中起着至关重要的作用。他们允许人们与数据和分析模型进行互动，以查看数据的不同观点并获得见解。这个交互式数据分析过程包括逐步指导分析模型的人，以产生数据以支持其任务的替代视图。在大多数情况下，这种人类的过程具有成功，有见地的结果。但是，认知科学告诉我们，人们可以表现出天生的偏见行为。结果，他们的数据分析行为和策略可能会受到影响。最终，这可能会导致从不完整的信息做出的决定，并就如何解释数据进行有限的观点。偏见的分析过程可能导致偏见的结果和错误信息。该项目将进行基本研究，以发现如何检测这种潜在的偏见并开发减轻其的视觉分析系统。它还将对来自STEM领域中代表性不足的团体的研究生和本科生产生教育影响，部分是通过与少数族裔服务机构的讲师以及历史上黑人学院和大学的讲师来帮助他们整合视觉分析，并将一般数据素养学习目标整合到课程中。拟议的多学科研究将开发技术，通过介入和提供指导，从而增强混合量化的视觉分析分析过程。为了实现这一目标，提出了三个主要的研究线。首先，团队将开发和评估计算指标，以检测用户交互模式和系统参数的贫困和潜在偏见的分析策略。这些指标由概率计算模型组成，这些计算模型考虑到数据探索期间的数据覆盖率等指标。其次，团队将开发和研究不同的视觉分析系统设计，以指导和改善人们的分析过程。每个原型都会使用指标为人们提供指导，但是通过不同的接口设计（例如，对话框，视觉覆盖层的视觉覆盖层等）向用户展示信息。他们将通过出版物和开源代码开发，评估和提供。第三，提出的研究将针对未来的混合初始性视觉分析系统产生经验结果和设计指南。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子和更广泛影响的审查标准通过评估来获得支持的。