Visualization techniques for collaborative data analysis

协作数据分析的可视化技术

• 批准号: RGPIN-2020-03966
• 负责人: Zhao, Jian
• 金额: $ 2.48万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=750241
• 关键词: Visualization; techniques; collaborative; data; analysis

We are continuously generating large amounts of data in different forms, and facing more complicated problems related to the data. With recent technological advances in big data analytics, many well--defined questions can be automatically resolved by machines. However, there are still many data problems ill-defined, vague, and exploratory, and they require close human engagement and supervision. Information Visualization (InfoVis) and Human-Computer Interaction (HCI) are powerful techniques to help people understand abstract data and algorithms with visual representations and user interactions. However, due to the scale and complexity of the data problems, multiple analysts with diverse backgrounds are requested to collaborate together. Further, of the three key players------data, machines, and humans---"people as the center" has become a shared view for the approach to solve real world problems. But human bandwidth for processing information is limited. Additionally, the communication cost is usually high in work collaboration. While many methods have been proposed in InfoVis and HCI, there still lacks a holistic understanding of the relationships between data, machines, and humans, as well as effective techniques to leverage and support them. This proposed research aims to investigate visualization techniques for collaborative data analysis, by promoting the interplay between data, machines, and humans, as an overarching goal. We will pursue three interrelated (but not strictly dependent) objectives. The first is to develop visual analysis tools to provide insights into analysts' collaborative behaviors and patterns. The second is to develop interactive visualization to support communication of abstract data and models in collaborative data analysis. The third is to design algorithms and visual metaphors to improve quality and efficiency of knowledge discovery in collaboration. This research will produce new visualization techniques to leverage and support the interplay of data, machines, and humans in collaborative data analysis activities. The resulting methods and systems will provide understandings to human behaviors during collaboration and to improve analystss' efficiency in collaborative data analysis, thus saving time and money. The outcomes will form an infrastructure or repository to be reused for broader interests in both immediate domains (e.g., data science) and other fields (e.g., social science). This research program will also train students in a diverse and interdisciplinary environment, allowing them to gain theoretical and practical skills in designing new visual representations, interactions, and algorithms.

我们不断地产生大量不同形式的数据，并面临着与数据相关的更复杂的问题。随着大数据分析的最新技术进步，许多定义明确的问题可以由机器自动解决。然而，仍然有许多数据问题定义不清，模糊和探索性，它们需要人类的密切参与和监督。信息可视化（InfoVis）和人机交互（HCI）是通过可视化表示和用户交互帮助人们理解抽象数据和算法的强大技术。然而，由于数据问题的规模和复杂性，要求具有不同背景的多个分析师一起合作。此外，在数据、机器和人类这三个关键角色中，“以人为中心”已经成为解决真实的世界问题的共同观点。但是人类处理信息的带宽是有限的。此外，在工作协作中，通信成本通常很高。虽然在InfoVis和HCI中已经提出了许多方法，但仍然缺乏对数据，机器和人类之间关系的全面理解，以及有效的技术来利用和支持它们。这项拟议的研究旨在研究协同数据分析的可视化技术，通过促进数据，机器和人类之间的相互作用，作为一个总体目标。我们将追求三个相互关联（但不严格依赖）的目标。首先是开发可视化分析工具，以提供对分析师协作行为和模式的洞察。第二是开发交互式可视化，以支持协同数据分析中抽象数据和模型的通信。第三是设计算法和视觉隐喻来提高协作中知识发现的质量和效率。这项研究将产生新的可视化技术，以利用和支持数据，机器和人类在协作数据分析活动中的相互作用。由此产生的方法和系统将提供对协作过程中人类行为的理解，并提高分析师在协作数据分析中的效率，从而节省时间和金钱。这些成果将形成一个基础设施或存储库，以便在两个直接领域（例如，数据科学）和其它领域（例如，社会科学）。该研究计划还将在多元化和跨学科的环境中培养学生，使他们能够获得设计新的视觉表示，交互和算法的理论和实践技能。