Visualization techniques for collaborative data analysis

我们不断地以不同的形式产生大量的数据，并面临与数据相关的更复杂的问题。随着最近大数据分析技术的进步，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、含糊不清和探索性的，它们需要密切的人类参与和监督。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来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协同工作。此外，在三个关键参与者-数据、机器和人类-“以人为中心”已成为解决现实世界问题的方法的共同观点。但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和人机交互中已经提出了许多方法，但仍然缺乏对数据、机器和人之间的关系的整体理解，以及利用和支持它们的有效技术。这项拟议的研究旨在通过促进数据、机器和人之间的相互作用来研究用于协作数据分析的可视化技术，作为首要目标。我们将追求三个相互关联(但不是严格依赖)的目标。首先是开发可视化分析工具，以洞察分析师的协作行为和模式。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交流。三是设计算法和视觉隐喻，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，并提高分析人员在协作数据分析中的效率，从而节省时间和金钱。成果将形成一个基础设施或储存库，用于直接领域(例如，数据科学)和其他领域(例如，社会科学)的更广泛利益。这项研究计划还将在多样化和跨学科的环境中培训学生，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。

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.