American Express Travel Related Services Company, Inc.

Himanshu Prabhakar

Ravi Arasan

Kunal Chandrashekhar Joshi

Sandeep Bose

Matthew Kent Meyer

Jeremy David Seideman

Gurusamy Ramasamy

Carmen Patricia Argüello

Ravi K Kaushik

Marimuthu Ponnambalam

Atul R Dalmia

Pratiksha M Jogani

Kandarp Srivastava

Vineet Agrwal

Roopesh R Varier

Nicholas Andrew Ondo

Katheryn Zhao

Suprabhat Gurrala

Rishi Anand

Jack Etheredge

Alireza Aliamiri

At least some embodiments are directed to a large-scale data standardization system. The system receives a set of documents with records formatted according to a third-party data schema. The system utilizes a first machine learning model to select a document from the set of documents. The system utilizes a machine learning model to select data classification labels formatted according to the third-party data schema. The classification labels are associated with a set of records. The system utilizes a second machine learning model to generate a canonical data structure constructed according to a standardized data schema based on the classification labels and the records associated with the classification labels.

System and methods for automated standardization of heterogeneous data using machine learning

The process includes receiving a data set comprising a plurality of rows and a plurality of columns, and applying a first rule based decisioning to the data set to generate a first layer of metadata that comprises at least one of a key, a type indicator, a categorical indicator, and/or a continuous indicator. The first layer of metadata may be descriptive of the data set. The processor may further apply a second rule based decisioning to the first layer to generate a second layer that includes at least one of the key, the type indicator, the categorical indicator, or the continuous indicator. The second layer may be descriptive of the first layer. The process may also include generating an output file from at least one of the first layer or the second layer.

Rule based decisioning on metadata layers

A system may receive a request to derive an output variable from a source variable. The request may include proposed logic to derive the output variable from the source variable. The system may then compare the proposed logic to existing logic to determine the proposed logic is new. In response to the proposed logic being new, the system may generate transformation code configured to execute the proposed logic. The system may further schedule the transformation code for execution at a predetermined time, and then execute the transformation code to generate data for the output variable.

Data transformations with metadata

A system may register a use case with the use case including an application. An application identifier may be assigned to the application. The system may generate a transformation associated with the use case. The transformation may include logic to derive an output variable from a source variable. The system may also execute the transformation to derive output data for the output variable from source data of the source variable. The system may further lookup an access permission for the application using the application identifier in response to an access request.

System and method for creating, tracking, and maintaining big data use cases

System and method transforming source data into output data in big data environments

System and method for recursive metadata layers on big data sets

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Bayonne, NJ, United States of America

Himanshu Prabhakar