document: Statistical modeling of large-scale simulation data With the advent of fast computer systems , scientists are now able to generate terabytes of simulation data . Unfortunately , the sheer size of these data sets has made efficient exploration of them impossible . To aid scientists in gleaning insight from their simulation data , we have developed an ad-hoc query infrastructure . Our system , called AQSim ( short for Ad-hoc Queries for Simulation ) reduces the data storage requirements and query access times in two stages . First , it creates and stores mathematical and statistical models of the data at multiple resolutions . Second , it evaluates queries on the models of the data instead of on the entire data set . In this paper , we present two simple but effective statistical modeling techniques for simulation data . Our first modeling technique computes the `` true '' ( unbiased ) mean of systematic partitions of the data . It makes no assumptions about the distribution of the data and uses a variant of the root mean square error to evaluate a model . Our second statistical modeling technique uses the Andersen-Darling goodness-of-fit method on systematic partitions of the data . This method evaluates a model by how well it passes the normality test on the data . Both of our statistical models effectively answer range queries . At each resolution of the data , we compute the precision of our answer to the user 's query by scaling the one-sided Chebyshev Inequalities with the original mesh 's topology . We combine precisions at different resolutions by calculating their weighted average . Our experimental evaluations on two scientific simulation data sets illustrate the value of using these statistical modeling techniques on multiple resolutions of large simulation data sets .
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