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    科学领域:

    • 数据科学数据科学数据科学
    • 人与计算机的交互
    • 信息可视化 信息可视化

    背景情况:

    • 交互式可视化是探索性数据分析 (EDA) 的关键工具.
    • 了解它们对分析师观察的影响对于工具设计至关重要.
    • 以前的研究往往侧重于任务的完成,而不是观察过程.

    研究的目的:

    • 调查交互式可视化如何影响数据科学家在EDA期间所做的观测.
    • 开发一种形式主义来描述EDA过程中的状态和过渡.
    • 确定交互式数据分析工具的设计影响.

    主要方法:

    • 一个定性实验涉及13名专业数据科学家使用木星笔记本.
    • 在数据分析过程中收集交互痕迹和大声思考的发言.
    • 语句的定性编码,以开发EDA状态 (表示和观察) 的形式主义.

    主要成果:

    • 交互式可视化带来了与静态关系相比,对属性关系的早期和更复杂的见解.
    • 分析表明,一些表示函数作为"规划辅助工具",而不仅仅是用于测试假设.
    • 诸如重访数和代表性多样性等指标突出显示了EDA行为中的"80-20规则"等模式.

    结论:

    • 交互式可视化增强了EDA中的洞察力的深度和及时性.
    • 提出的形式主义为分析EDA流程和工具有效性提供了一个框架.
    • 这些发现有助于设计更有效的交互工具,用于数据探索和分析.