Sas Institute Inc.

Dominique Joseph Latour

Georges H Guirguis

Robert S Ray

Taiyeong Lee

David Rawlins Duling

A computing device creates a state of an analytic model. An analytic engine of an analytic model of an analytic model type is initialized. The analytic model is trained using a dataset and the analytic engine. A request to save a state of the analytic model is received. In response to receipt of the third indicator, the state of the trained analytic model is serialized. The state includes a reentry point name of a function of the analytic model type called to instantiate the trained analytic model. The serialized state is written to an output file. The written state is read from the output file. The state of the trained analytic model is restored using the read state. An analytic result is computed based on data in a second dataset different from the named dataset using the restored state of the trained analytic model. The computed analytic result is output.

Streamlined analytic model training and scoring system

A computing device restores a state of an analytic model. A first indicator of a dataset is received. A second indicator is received of an analytic model state file in which a state of an analytic model is stored. A reentry point name of a function to restore the analytic model is extracted from the state stored in the analytic model state file indicated by the received second indicator. The function is called using the extracted reentry point name to restore the state of the analytic model. The dataset indicated by the received first indicator is scored using the restored state of the analytic model. Scoring results from the scored dataset are written to an output file.

Streamlined system to restore an analytic model state for training and scoring

Computer-implemented systems and methods are provided for creating a cluster structure from a data set containing input variables. Global clusters are created within a first stage, by computing a similarity matrix from the data set. A global cluster structure and sub-cluster structure are created within a second stage, where the global cluster structure and the sub-cluster structure are created using a latent variable clustering technique and the cluster structure output is generated by combining the created global cluster structure and the created sub-cluster structure.

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Cary, NC, United States of America

Dominique Joseph Latour