A Space-Efficient Method for Navigable Ensemble Analysis and Visualization

TL;DR

This paper introduces NEA, a space-efficient, interactive visualization tool for ensemble data analysis that leverages data similarity to enable exploration within memory constraints.

Contribution

The paper presents a novel spatially-efficient data structure for ensemble visualization that reduces memory usage and enhances interactive exploration capabilities.

Findings

Enables interactive ensemble analysis within strict memory limits.

Uses data similarity to optimize data representation.

Provides new insights through data-similarity analysis.

Abstract

Scientists increasingly rely on simulation runs of complex models in lieu of cost-prohibitive or infeasible experimentation. The data output of many controlled simulation runs, the ensemble, is used to verify correctness and quantify uncertainty. However, due to their size and complexity, ensembles are difficult to visually analyze because the working set often exceeds strict memory limitations. We present a navigable ensemble analysis tool, NEA, for interactive exploration of ensembles. NEA's pre-processing component takes advantage of the data similarity characteristics of ensembles to represent the data in a new, spatially-efficient data structure which does not require fully reconstructing the original data at visualization time. This data structure allows a fine degree of control in working set management, which enables interactive ensemble exploration while fitting within memory limitations. Scientists can also gain new insights from the data-similarity analysis in the pre-processing component.