2 research outputs found
Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data
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.Comment: To appear as IEEE Vis 2020 Shortpape
Multifaceted 4D Feature Segmentation and Extraction in Point and Field-based Datasets
The use of large-scale multifaceted data is common in a wide variety of
scientific applications. In many cases, this multifaceted data takes the form
of a field-based (Eulerian) and point/trajectory-based (Lagrangian)
representation as each has a unique set of advantages in characterizing a
system of study. Furthermore, studying the increasing scale and complexity of
these multifaceted datasets is limited by perceptual ability and available
computational resources, necessitating sophisticated data reduction and feature
extraction techniques. In this work, we present a new 4D feature
segmentation/extraction scheme that can operate on both the field and
point/trajectory data types simultaneously. The resulting features are
time-varying data subsets that have both a field and point-based component, and
were extracted based on underlying patterns from both data types. This enables
researchers to better explore both the spatial and temporal interplay between
the two data representations and study underlying phenomena from new
perspectives. We parallelize our approach using GPU acceleration and apply it
to real world multifaceted datasets to illustrate the types of features that
can be extracted and explored