Data Visualization

数据可视化

• 批准号: CRC-2021-00141
• 负责人: Paulovich, Fernando
• 金额: $ 1.9万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753435
• 关键词: Data; Visualization

Consider the tasks of detecting risks of residents' falls in nursing homes and the prescription of mitigation actions; monitoring online youth activities to identify potential mental health disorders and suggest procedures to trigger early treatments; or identifying the roots for the differences in cities' quality of life and indicate possible solutions to equate the discrepancies.The key concept underlying these applications is predicting an event, understanding the reasons for the prediction, and potentially prescribing mitigation actions. Although prediction is pervasive, benefiting many application scenarios, from disease prognosis to mortgage application analysis, only recently the need for identifying biases and understanding the reasons for an outcome became a concern, with governmental regulations requiring explanations about automated decisions.Machine learning and visual analytics techniques have been proposed to address this issue. However, existing solutions consider the different aspects of this process, e.g., bias and explanation, in isolation while rarely support the concept of prescribing actions based on a prediction. The proposed research will develop new visual analytics approaches aiming to devise predictive and prescriptive tools and techniques to support the whole pipeline of classifying with confidence, understanding a classification, and prescribing mitigation actions.Appropriately addressing these three pillars imposes several challenges, especially the design of visual interfaces to facilitate user involvement, making the analytical process more reliable by incorporating background knowledge. The proposed research will investigate new visual metaphors and techniques to place users as active players in building high-quality classification models by (i) aiding the labeling process to correct problems and identifying data biases, (ii) helping in understanding complex model inner workings and auditing results, and (iii) developing the novel concept of prescriptive visual analytics, visually recommending potential solutions to problems detected through predictions.With industrial and academic collaborations, the results of this research will be translated into novel applications to benefit different domains, including health monitoring, urban data analysis, among others, helping to promote visual analytics into other knowledge domains and leveraging Dalhousie's expertise in the use, analysis, and interpretation of data.

考虑检测疗养院居民跌倒风险的任务并制定缓解措施；监测青少年在线活动，以识别潜在的心理健康障碍，并提出早期治疗的建议；这些应用程序的关键概念是预测事件，了解预测的原因，并可能制定缓解措施。尽管预测很普遍，使许多应用场景受益，从疾病预后到抵押贷款申请分析，但直到最近，识别偏差和理解结果原因的需求才成为人们关注的问题，政府法规要求对自动化决策进行解释。人们提出了机器学习和视觉分析技术来解决这个问题。然而，现有的解决方案孤立地考虑了这个过程的不同方面，例如偏见和解释，而很少支持基于预测来规定行动的概念。拟议的研究将开发新的可视化分析方法，旨在设计预测性和规范性工具和技术，以支持自信地分类、理解分类和规定缓解措施的整个流程。适当解决这三个支柱会带来一些挑战，特别是设计可视化界面以促进用户参与，通过结合背景知识使分析过程更加可靠。拟议的研究将调查新的视觉隐喻和技术，使用户成为构建高质量分类模型的积极参与者，方法是（i）帮助标记过程纠正问题并识别数据偏差，（ii）帮助理解复杂模型的内部工作原理和审计结果，以及（iii）开发规范性视觉分析的新概念，以视觉方式推荐通过预测发现的问题的潜在解决方案。通过工业和学术合作，这项研究的结果将是 转化为新颖的应用程序，使不同领域受益，包括健康监测、城市数据分析等，帮助促进可视化分析进入其他知识领域，并利用达尔豪斯在数据使用、分析和解释方面的专业知识。