EVM:将模型检查纳入探索性视觉分析中
IEEE transactions on visualization and computer graphics
|October 23, 2023
概括
视觉分析 (VA) 工具有助于探索数据,但缺乏解释检查. EVM 将统计模型检查集成到VA中,通过仔细检查数据生成过程来改进数据探索.
科学领域:
- 计算机科学 计算机科学
- 统计 统计 统计 统计
- 人与计算机的交互
背景情况:
- 视觉分析 (VA) 工具促进了快速的数据探索和模式发现.
- 现有的VA工具往往缺乏明确验证数据解释与基础模型之间的机制.
- 分析师可能很难验证观察到的模式是否与假设的数据生成过程保持一致.
研究的目的:
- 引入EVM,这是一个新的数据探索工具.
- 使用户能够使用统计模型表达和视觉检查数据的临时解释.
- 在用户研究中评估模型检查对数据探索过程的影响.
主要方法:
- 开发EVM,整合统计模型表达和基于可视化的模型检查.
- 模型预测的染分布与用户生成的数据视图一起.
- 与数据科学家一起进行用户研究,观察在探索过程中使用模型检查的情况.
主要成果:
- 电子票机通过统计模型促进数据解释的表达和检查.
- 基于可视化的模型检查有助于分析师仔细审查对数据生成过程的预期.
- 用户研究参与者积极使用模型检查来完善他们对数据关系的理解.
结论:
- 将模型检查集成到VA工具中可以提高数据探索的严谨性.
- 电子票机证明了基于模型的解释检查的实用性.
- 进一步的开发可以在VA环境中支模型探索,以改善分析结果.
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