通过频繁模式对时间概念的关联探索的可视化
Tali Malenboim1, Nir Grinberg1, Robert Moskovitch1
1Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.
Patterns (New York, N.Y.)
|August 22, 2025
概括
探索复杂的时间模式是一个挑战. PanTeraV可视化频繁的时间模式 (TIRP) 的列举树,使得以前无法访问的符号时间区间的数据探索和发现更快.
科学领域:
- 数据可视化
- 数据挖掘
- 时间模式分析
背景情况:
- 传统的时间模式可视化侧重于单个模式,忽略了复杂的数列树.
- 对于数据科学家来说,探索大量的时间模式是一个重大挑战.
研究的目的:
- 介绍PanTeraV,一个用于探索复杂时间模式的大数列树的新型可视化系统.
- 根据用户选择的符号时间间隔,启用双向探索模式.
主要方法:
- 开发了PanTeraV,具有两个连接的可视化:用于导航符号时间间隔的表格视图和用于模式指标的图形泡图.
- 在现实数据集上使用时间间隔相关模式 (TIRPs) 演示了PanTeraV.
主要成果:
- 与现有的方法相比,PanTeraV可以更快地探索时间模式.
- 用户发现了之前无法访问的符号时间间隔之间的关联.
结论:
- 增强复杂时间模式的统计和分析探索.
- 该系统支持数据科学家识别大计数树中的相关模式.
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