3 research outputs found
Multivariate Pointwise Information-Driven Data Sampling and Visualization
With increasing computing capabilities of modern supercomputers, the size of
the data generated from the scientific simulations is growing rapidly. As a
result, application scientists need effective data summarization techniques
that can reduce large-scale multivariate spatiotemporal data sets while
preserving the important data properties so that the reduced data can answer
domain-specific queries involving multiple variables with sufficient accuracy.
While analyzing complex scientific events, domain experts often analyze and
visualize two or more variables together to obtain a better understanding of
the characteristics of the data features. Therefore, data summarization
techniques are required to analyze multi-variable relationships in detail and
then perform data reduction such that the important features involving multiple
variables are preserved in the reduced data. To achieve this, in this work, we
propose a data sub-sampling algorithm for performing statistical data
summarization that leverages pointwise information theoretic measures to
quantify the statistical association of data points considering multiple
variables and generates a sub-sampled data that preserves the statistical
association among multi-variables. Using such reduced sampled data, we show
that multivariate feature query and analysis can be done effectively. The
efficacy of the proposed multivariate association driven sampling algorithm is
presented by applying it on several scientific data sets.Comment: 25 page