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Metrics and Workflow to Quantify Quality in Data Reduction for Climate and Cosmological Simulations

This paper presents a solution to the challenge of data reduction in large-scale scientific simulations, particularly in climate and cosmology. It identifies the limitations of storage and I/O capabilities in handling the volume of generated data. The proposed technical solution involves assessing and recording errors after each reduction transformation using compact metrics, allowing scientists to evaluate the quality of reduced data against original datasets. This approach enhances the reliability of data used in scientific analyses, promoting better understanding and decision-making in climate and cosmological research.

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Metrics and Workflow to Quantify Quality in Data Reduction for Climate and Cosmological Simulations

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  1. Metrics and Workflow for Quantifying the Quality of ReductionTransformations on Large-Scale, Scientific Scalar Data Science Problem: Storage and I/O have not kept up with climate and cosmological simulation capacity to generate data. Data must be reduced, but what has been lost in the process of reduction? Technical Solution: After every reducing transformation (T), assess and record the residuals/errors via compact measures (M) comparing reduced (a) to “original” data (A) by reconstructed data (B). Science Impact: Measuring the data uncertainty via a quantitative quality provenance shows that climate and cosmological scientists are able to use reduced data for scientific analysis. Woodring, Shafii, Biswas, Myers, Wendelberger, Hamann, and Shen. “Metrics and Workflow for Quantifying the Quality of Reduction Transformations on Large-Scale, Scientific Scalar Data.” Submitted to LDAV 2013.

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