Dr. Nir Greenberg

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.