Variable Interactions in Query-Driven Visualization

One fundamental element of scientific inquiry is discoveringrelationships, particularly the interactions between different variablesin observed or simulated phenomena. Building upon our prior work in thefield of Query-Driven Visualization, where visual data analysisprocessing is focused on subsets of large data deemed to be"scientifically interesting," this new work focuses on a novel knowledgediscovery capability suitable for use with petascale class datasets. Itenables visual presentation of the presence or absence of relationships(correlations) between variables in data subsets produced by Query-Drivenmethodologies. This technique holds great potential for enablingknowledge discovery from large and complex datasets currently emergingfrom SciDAC and INCITE projects. It is sufficiently generally to beapplicable to any time of complex, time-varying, multivariate data fromstructured, unstructured or adaptive grids