Sas Institute Inc.

James Howard Goodnight

John Clare Brocklebank

Oliver Schabenberger

Wolfgang Michael Hartmann

Christopher Daniel Bailey

Steve Krueger

Christopher D Bailey

A data processing system having multiple executable threads is configured to generate a cross-product matrix in a single pass through data. An example system comprises memory for receiving the data, a processor having a plurality of executable threads, and software code for generating a cross-product matrix in a single pass through the data. The software code includes threaded variable levelization code for generating thread specific binary trees for classification variables, variable tree merge code for combining the thread-specific trees into overall trees for the classification variables, effect levelization code for generating sub-matrices of the cross-product matrix using the overall trees for the classification variables, and cross-product matrix generation code for generating the cross-product matrix by storing and ordering the elements of the sub-matrices in contiguous memory space.

Systems and methods for generating a cross-product matrix in a single pass through data using single pass levelization

Systems and methods are provided for generating multiple system state projections using a grid computing environment. A central coordinator software component executes on a root data processor and provides commands and data to a plurality of node coordinator software components. A node coordinator software component manages threads which execute on its associated node data processor and which perform a set of matrix operations. Stochastic simulations use results of the matrix operations to generate multiple state projections. Additional processing can be performed by the grid computing environment based upon the generated state projections, such as to develop possible change information for users.

Distributed systems and methods for state generation based on multi-dimensional data

Systems and methods are provided for a data processing system having multiple executable threads that is configured to generate a cross-product matrix in a single pass through data to be analyzed. An example system comprises memory for receiving the data to be analyzed, a processor having a plurality of executable threads for executing code to analyze data, and software code for generating a cross-product matrix in a single pass through data to be analyzed. The software code includes threaded variable levelization code for generating a plurality of thread specific binary trees for a plurality of classification variables, variable tree merge code for combining a plurality of the thread-specific trees into a plurality of overall trees for the plurality of classification variables, effect levelization code for generating sub-matrices using the plurality of the overall trees for the plurality of classification variables, and cross-product matrix generation code for generating the cross-product matrix.

A computer-implemented method and system for building a neural network is disclosed. The neural network predicts at least one target based upon predictor variables defined in a state space. First, an input data set is retrieved that includes the predictor variables and at least one target associated with the predictor variables for each observation. In the state space, a number of points is inserted in the state space based upon the values of the predictor variables. The number of points is less than the number of observations. A statistical measure is determined that describes a relationship between the observations and the inserted points. Weights and activation functions of the neural network are determined using the statistical measure.

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Cary, NC, United States of America

James Howard Goodnight