Statistical information derived from data samples is a promising approach to taming the big data avalanche. In this project, we will develop a distribution-based data analysis and visualization framework for optimizing in situ processing and analysis of extreme-scale scientific data. The objective of this project is to create a framework consisting of three tightly integrated components: (1) Computation, Representation, and Indexing of Distributions; (2) Data Summarization, Reduction, and Triage; and (3) Distribution-Based Visual Analytics.

The project researchers will demonstrate the applicability of this approach in three distinct computational science applications: climate, cosmology, and superconductivity.