Tibco Software Inc.

Cloud Software Group, Inc.

Daniel Scott

Thomas A Hill

Vladimir S Rastunkov

Sayan Chakraborty

Stephen Nuchia

A predictive engine includes an optimizer and a predictor. The optimizer is configured to receive an observed dataset having inputs and multivariate responses and determine latent response variables based on the predictive inputs and the multivariate responses. The optimizer is further configured to select latent response variables, measure dependencies between multivariate responses, estimate coefficients that relate the input predictors to determined latent response variables, and correlate dependencies and coefficients with the latent response variables. The predictor is configured to generate a predictive distribution of probabilities for the latent variables, map the probabilities to multivariate responses, generate a predictive distribution of probabilities for the multivariate responses, and determine at least one optimized input from the multivariate responses.

Predictive engine for generating multivariate predictive responses

A system, method, and computer-readable medium for performing a variable and interaction detection operation. In certain embodiments, the variable and interaction detection operation detects important variables and as well as interactions among certain important variables in big data. In certain embodiments, the variable and interaction detection operation includes a robust automated process flow (i.e., an analytic recipe) which identifies important variables and their interactions in large data sets.

Detecting important variables and their interactions in big data

A system, method, and computer-readable medium are disclosed for identifying sources of variation in complex manufacturing processes via a variation identification operation. In certain embodiments, the variation identification operation is performed via a variation identification system. The variation identification operation addresses special class of analytic problems, namely the estimation of variance components and related statistics from very large (big data) hierarchically nested designs of random factors. These types of data structures occur frequently across various industries, and in particular in automated and batch manufacturing where the variability in product quality as measured in final product testing should be related to batches, lots, wafers, suppliers, etc. upstream of the process.

System for rapid identification of sources of variation in complex manufacturing processes

An information handling system performs a method for finding a nearest neighbor of a point. In some embodiments, the method may be used for agglomerative clustering. The method includes projecting a space &#x398; of a first dimension with a first distance &#x3bc; to a space P of a second, smaller dimension with a distance &#x3bc;' by a projection function p. For all pairs of points vand vin &#x398;, &#x3bc;&#x2032; (p(v), p(v))&#x2264;&#x3bc;(v, v), where p is the function that projects points in &#x398; to points in P. The method also includes selecting a point v in &#x398; and performing a search for its nearest neighbor in &#x398; by projecting v to P and locating a set S of nearest neighbors in P of p(v). A search is then performed in &#x398; of a set of S&#x2032; of points that project onto the points in S.

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Broken Arrow, OK, United States of America

Daniel Scott