Patents by Inventor Anthony Escalona

Anthony Escalona 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 generating an effective test data set for testing big data applications
    Patent number: 10467220
    Abstract: A system for generating an effective test data set for testing big data applications includes a data collector, a data analyzer, an input domain modeler, a self-adaptive input domain modeler, and a test data set generator. The data collector collects a high volume of data from an original data set and initial constraints, the data analyzer analyzes the data and the initial constraints to generate analytical results, the input domain modeler automatically generates an input domain model based on the analytical results, the self-adaptive input domain modeler generates a self-adaptive input domain model by combining the input domain model and analytical results, and the test data set generator generates an initial test data set based on the self-adaptive input domain model. A method for generating an effective test data set for testing big data applications is also described.
    Type: Grant
    Filed: January 29, 2016
    Date of Patent: November 5, 2019
    Assignee: Medidata Solutions, Inc.
    Inventors: Nan Li, Anthony Escalona
  • SYSTEM AND METHOD FOR GENERATING AN EFFECTIVE TEST DATA SET FOR TESTING BIG DATA APPLICATIONS
    Publication number: 20160246838
    Abstract: A system for generating an effective test data set for testing big data applications includes a data collector, a data analyzer, an input domain modeler, a self-adaptive input domain modeler, and a test data set generator. The data collector collects a high volume of data from an original data set and initial constraints, the data analyzer analyzes the data and the initial constraints to generate analytical results, the input domain modeler automatically generates an input domain model based on the analytical results, the self-adaptive input domain modeler generates a self-adaptive input domain model by combining the input domain model and analytical results, and the test data set generator generates an initial test data set based on the self-adaptive input domain model. A method for generating an effective test data set for testing big data applications is also described.
    Type: Application
    Filed: January 29, 2016
    Publication date: August 25, 2016
    Inventors: Nan Li, Anthony Escalona