National Science Foundation

NISS was hired to help develop and build systems that expanded to Federal data but that preserved the confidentiality of the data and privacy of subjects. The systems would respond to queries from networked users of Federal data bases by performing and reporting statistical analyses that extract knowledge from the data, but preserve confidentiality. Their distinguishing characteristic was history-dependence. The response to each query will depend on the history of previous queries and responses.

Large data sets are a given in modern industry, technology and science and central issues concerning them demand statistical attention. While the need is apparent, paths to bringing statistical science to bear on large data sets are not clearly mapped. General approaches will emerge most rapidly if adequate focus is placed on specific industrial and scientific problems, in collaboration with those who hold the data. This research effort comprised two interconnected pilot projects dealing with large data sets, each involving a major industrial partner.

These included:

This research provides surrogate statistical models for the usually deterministic output of complex computer models which cannot be directly explored in great detail because of their size and limitations on the number of runs. The models ate based on a set of runs at selected inputs to aid in predicting the output at untried inputs, optimizing characteristics of the output, identifying important input factors and tuning the model to physical data. The strategies will be devised in the context of large numbers of input factors.