Abstract: An embodiment of the present invention is directed to classifying attributes into respective PI/PG categories based on metadata. An embodiment of the present invention may classify each attribute into PII/Non-PII and then into various Protection group codes that define access, roles permissions, privileges and/or other action. An embodiment of the present invention may leverage various statistical techniques, natural language processing (NLP) methods and different combinations of algorithms customized to improve prediction accuracies of a classifier model.

Abstract: Systems and methods according to exemplary embodiments provide a process and automation framework enabling Acceptance Test Driven Development (ATDD) automation for Extract, Transform, and Load (ETL) and Big Data testing. Exemplary embodiments include a user interface for executing end to end tests as part of an ATDD process during ETL. The user interface may act as a shopping cart where the user only has to pick and choose the flavor of tests he/she desires to run (e.g., Pre-Ingestion, Post Ingestion, Data Reconciliation, etc.), and the feature files associated with the tests are dynamically generated.

Abstract: An embodiment of the present invention is directed to reducing complexities in machine learning application development by providing a drag-and-drop user interface for an entire machine learning process. The innovative system significantly reduces development time and efforts. An embodiment of the present invention is directed to applying optimized common components that follow industry wide best practices thereby improving the time to market as well as the overall code quality. The embodiments of the present invention provide adaptability and extendibility to support various platforms. According to an embodiment of the present invention, a generic platform agnostic code generator may be extended to support various use cases, applications, platforms and environments.

Abstract: Systems and methods according to exemplary embodiments provide a process and automation framework enabling Acceptance Test Driven Development (ATDD) automation for Extract, Transform, and Load (ETL) and Big Data testing. Exemplary embodiments include a user interface for executing end to end tests as part of an ATDD process during ETL. The user interface may act as a shopping cart where the user only has to pick and choose the flavor of tests he/she desires to run (e.g., Pre-Ingestion, Post Ingestion, Data Reconciliation, etc.), and the feature files associated with the tests are dynamically generated.

Abstract: An embodiment of the present invention is directed to classifying attributes into respective PI/PG categories based on metadata. An embodiment of the present invention may classify each attribute into PII/Non-PII and then into various Protection group codes that define access, roles permissions, privileges and/or other action. An embodiment of the present invention may leverage various statistical techniques, natural language processing (NLP) methods and different combinations of algorithms customized to improve prediction accuracies of a classifier model.

Abstract: An embodiment of the present invention is directed to reducing complexities in machine learning application development by providing a drag-and-drop user interface for an entire machine learning process. The innovative system significantly reduces development time and efforts. An embodiment of the present invention is directed to applying optimized common components that follow industry wide best practices thereby improving the time to market as well as the overall code quality. The embodiments of the present invention provide adaptability and extendibility to support various platforms. According to an embodiment of the present invention, a generic platform agnostic code generator may be extended to support various use cases, applications, platforms and environments.