Patents by Inventor Gayathri P. AYYAPPAN

Gayathri P. AYYAPPAN has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • System and method for partitioning models in a database
    Patent number: 11176480
    Abstract: Systems, methods, and other embodiments are disclosed for partitioning models in a database. In one embodiment, a set of training data is parsed into multiple data partitions based on partition keys, where the data partitions are identified by the partition keys and are used for training data mining models. The multiple data partitions are analyzed to generate partition metrics data. Algorithm data, identifying at least one algorithm for processing the multiple data partitions, and resources data, identifying available modeling resources for processing the multiple data partitions, are read. The partition metrics data, the algorithm data, and the resources data are processed to generate an organization data structure. The organization data structure is configured to control distribution and processing of the multiple data partitions across the available modeling resources to generate a composite model object that includes a separately trained data mining model for each partition of the multiple partitions.
    Type: Grant
    Filed: August 2, 2016
    Date of Patent: November 16, 2021
    Assignee: Oracle International Corporation
    Inventors: Ari W. Mozes, Boriana L. Milenova, Marcos M. Campos, Mark A. McCracken, Gayathri P. Ayyappan
  • SYSTEM AND METHOD FOR PARTITIONING MODELS IN A DATABASE
    Publication number: 20170308809
    Abstract: Systems, methods, and other embodiments are disclosed for partitioning models in a database. In one embodiment, a set of training data is parsed into multiple data partitions based on partition keys, where the data partitions are identified by the partition keys and are used for training data mining models. The multiple data partitions are analyzed to generate partition metrics data. Algorithm data, identifying at least one algorithm for processing the multiple data partitions, and resources data, identifying available modeling resources for processing the multiple data partitions, are read. The partition metrics data, the algorithm data, and the resources data are processed to generate an organization data structure. The organization data structure is configured to control distribution and processing of the multiple data partitions across the available modeling resources to generate a composite model object that includes a separately trained data mining model for each partition of the multiple partitions.
    Type: Application
    Filed: August 2, 2016
    Publication date: October 26, 2017
    Inventors: Ari W. MOZES, Boriana L. MILENOVA, Marcos M. CAMPOS, Mark A. MCCRACKEN, Gayathri P. AYYAPPAN