Multivariate Pointwise Information-Driven Data Sampling and Visualization
This addresses the need for efficient data reduction techniques for application scientists dealing with big data from simulations, though it appears incremental as it builds on existing information-theoretic methods for a specific domain.
The paper tackles the problem of summarizing large-scale multivariate spatiotemporal data from scientific simulations by proposing a data sub-sampling algorithm that uses pointwise information theory to preserve statistical associations among multiple variables, enabling effective multivariate feature queries and analysis on reduced data.
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.