Adaptive Sampling via Adaptive Optimal Experimental Designs to Extract Maximum Information from Large Data Repositories

摘要

A system, method, and computer-readable medium for extracting the samples from big data to extract most information about the relationships of interest between dimensions and variables in the data repository. More specifically, extracting information from large data repositories follows an adaptive process that uses systematic sampling procedures derived from optimal experimental designs to target from a large data set specific observations with information value of interest for the analytic task under consideration. The application of adaptive optimal design to guide exploration of large data repositories provides advantages over known big data technologies.

主权项

1. A computer-implementable method for identifying information within a data repository, comprising:
selecting variables of interest within the data repository to provide selected variables; defining a depth of interactions of interest with respect to the variables of interest to provide a defined depth of interactions; applying an optimal experimental design operation to the selected variables and the defined depth of interactions, the applying providing returned data based upon the optimal experimental design operation; and performing operations on the returned data, the returned data providing a sample matrix of the data repository.