Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data

TL;DR

This paper introduces a multivariate sampling strategy for scientific simulation data that preserves variable relationships, enabling more accurate post-hoc analyses and data reduction compared to traditional univariate methods.

Contribution

It proposes a novel multivariate sampling approach using PCA that can be integrated with existing algorithms and includes data partitioning variants for local relationship modeling.

Findings

Effective data reduction demonstrated on real-world datasets

Preserves multivariate relationships for subsequent analysis

Facilitates scalable post-hoc multivariate analysis

Abstract

With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing. To reduce the storage footprint and facilitate scalable post-hoc analyses of such scientific data sets, various data reduction/summarization methods have been proposed over the years. Different flavors of sampling algorithms exist to sample the high-resolution scientific data, while preserving important data properties required for subsequent analyses. However, most of these sampling algorithms are designed for univariate data and cater to post-hoc analyses of single variables. In this work, we propose a multivariate sampling strategy which preserves the original variable relationships and enables different multivariate analyses directly on the sampled data. Our proposed strategy utilizes principal component analysis to capture the variance of multivariate data and can be built on top of any existing state-of-the-art sampling algorithms for single variables. In addition, we also propose variants of different data partitioning schemes (regular and irregular) to efficiently model the local multivariate relationships. Using two real-world multivariate data sets, we demonstrate the efficacy of our proposed multivariate sampling strategy with respect to its data reduction capabilities as well as the ease of performing efficient post-hoc multivariate analyses.