Malenboim Tali, Grinberg Nir, Moskovitch Robert
Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel.
Patterns (N Y). 2025 Jun 11;6(8):101292. doi: 10.1016/j.patter.2025.101292. eCollection 2025 Aug 8.
Most studies on temporal pattern visualization have focused on a single pattern and its metrics and supporting instances. However, the output of a mining process is typically an enumeration tree of frequent temporal patterns. A key challenge is exploring these patterns to identify those of interest for an expert or data scientist. Recently, it was suggested that the enumeration tree be browsed from the root downward through extended patterns. We introduce PanTeraV, a visualization system for statistical and analytical exploration of a large enumeration tree of complex temporal patterns. Demonstrated with time-interval-related patterns (TIRPs), it enables bidirectional exploration based on user-selected symbolic time intervals. The system consists of two visualizations: tabular, for navigating symbolic time intervals, and graphical, which presents relevant patterns in a bubble chart encoding multiple metrics. A user study on two real-world datasets shows that PanTeraV enables faster exploration of temporal patterns and allows users to discover associations of symbolic time intervals that were previously inaccessible.
大多数关于时间模式可视化的研究都集中在单一模式及其度量和支持实例上。然而，挖掘过程的输出通常是频繁时间模式的枚举树。一个关键挑战是探索这些模式，以识别专家或数据科学家感兴趣的模式。最近，有人建议从根开始向下浏览枚举树，通过扩展模式进行探索。我们引入了PanTeraV，这是一个用于对复杂时间模式的大型枚举树进行统计和分析探索的可视化系统。通过与时间间隔相关的模式（TIRP）进行演示，它能够基于用户选择的符号时间间隔进行双向探索。该系统由两种可视化组成：表格形式，用于浏览符号时间间隔；图形形式，在气泡图中呈现编码多个度量的相关模式。对两个真实世界数据集的用户研究表明，PanTeraV能够更快地探索时间模式，并允许用户发现以前无法访问的符号时间间隔的关联。
