Effective Visualization of Temporal Ensembles

Abstract

An ensemble is a collection of related datasets, called members, built from a series of runs of a simulation or an experiment. Ensembles are large, temporal, multidimensional, and multivariate, making them difficult to analyze. Another important challenge is visualizing ensembles that vary both in space and time. Initial visualization techniques displayed ensembles with a small number of members, or presented an overview of an entire ensemble, but without potentially important details. Recently, researchers have suggested combining these two directions, allowing users to choose subsets of members to visualization. This manual selection process places the burden on the user to identify which members to explore. We first introduce a static ensemble visualization system that automatically helps users locate interesting subsets of members to visualize. We next extend the system to support analysis and visualization of temporal ensembles. We employ 3D shape comparison, cluster tree visualization, and glyph based visualization to represent different levels of detail within an ensemble. This strategy is used to provide two approaches for temporal ensemble analysis: (1) segment based ensemble analysis, to capture important shape transition time-steps, clusters groups of similar members, and identify common shape changes over time across multiple members; and (2) time-step based ensemble analysis, which assumes ensemble members are aligned in time by combining similar shapes at common time-steps. Both approaches enable users to interactively visualize and analyze a temporal ensemble from different perspectives at different levels of detail. We demonstrate our techniques on an ensemble studying matter transition from hadronic gas to quark-gluon plasma during gold-on-gold particle collisions.

Visualizing Static Ensembles For Effective Shape and Data Comparison

“…The system described here does not support the important need for temporal ensemble analysis, which if often required. We have recently investigated a number of more complex methods to identify temporal patterns within an ensemble (e.g., cluster participation pattern mining and time-step pattern mining), with positive results [5].…”

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

“…Basic visual abstractions such as line charts, quartile charts, and histograms are commonly used in ensemble visualization to encode statistical parameters [28], as well as reduced spatial aggregate views [19,37] to display specific attributes at a specific time and location. To facilitate further exploration of ensemble members across space and time, these aggregate views are linked to range-based representations [26]. These representations may include colored overlays, multidimensional scaling projections [8], and various types of tracking graphs [9,37,69].…”

Projected Field Similarity for Comparative Visualization of Multi‐Run Multi‐Field Time‐Varying Spatial Data

“…A static ensemble visualization system that automatically helps users locate interesting subsets of members to visualize was proposed by Hao et al . [HHB16]. Both approaches currently have no extension to ensembles that vary over time.…”

“…A novel technique for the interactive visual exploration of large 3D scalar ensembles was introduced by Demir et al [DDW14]. A static ensemble visualization system that automatically helps users locate interesting subsets of members to visualize was proposed by Hao et al [HHB16]. Both approaches currently have no extension to ensembles that vary over time.…”