Abstract: An example method includes receiving a request for a transaction; associating the request with a user; receiving, from the user, identification information; retrieving, from a database, first stored authentication information associated with the user; retrieving, from a distributed ledger, second stored authentication information associated with the user; and determining that the received authentication information matches the first stored authentication details and the second stored authentication details and, in response, outputting, by the computing system, a proof of authentication to enable the transaction.

Abstract: Methods and systems are presented for providing a data control framework that enables storing, sharing, and transferring of data in a secure manner. Data files stored in data repositories are scanned. Content associated with different section of each data file is analyzed, and each section is tagged with a sensitivity level based on the content and a subject matter derived for the data file. Each data file is also assigned to a clearance classification based on an expected viewer of the data file. When sections from a first data file is being transferred to a second data file, a data control mechanism is triggered. If a particular section from the first data file is incompatible with the second data file, the data control mechanism may prevent the particular section from being transferred to the second data file, while allowing the remaining sections being transferred to the second data file.

Abstract: Techniques are disclosed relating to automatically generating and analyzing database queries. In various embodiments, a database inquiry assistance system maintains a first machine learning model trained using query history data for a database and a second machine learning model using analysis history for the database. In an embodiment, the system receives from a user system a request for an inquiry into data stored in the database and identifies a sequence of queries for responding to the request, where identifying the sequence of queries includes applying the second machine learning model to the request. The system generates corresponding database query code for implementing one or more of the queries in the sequence of queries, where generating the corresponding database query code includes applying the first machine learning model to descriptors of one or more of the queries, and sends a plan identifying the sequence of queries to the user system.

Abstract: There are provided systems and methods for configurable storage resource usage through a multi-phase data processing pipeline for data storage efficiency. A service provider may provide online digital computing services, such as electronic transaction processing, account, and the like through online platforms and digital content that may be loaded and served on webpages and through applications of the service provider. The service provider may process and serve data, which may be stored in various data storages and databases of the service provider. The service provider may configure and optimize storage resource usage by processing data through a multi-phased processing pipeline that analyzes the data for reductions in data storage size, duplications, and/or storage times to convert data into a more efficient structure. This may include converting the data to smaller data pieces, deduplicating the same or similar data, or limiting storage in resource intensive data stores.

Abstract: There are provided systems and methods for configurable storage resource usage through a multi-phase data processing pipeline for data storage efficiency. A service provider may provide online digital computing services, such as electronic transaction processing, account, and the like through online platforms and digital content that may be loaded and served on webpages and through applications of the service provider. The service provider may process and serve data, which may be stored in various data storages and databases of the service provider. The service provider may configure and optimize storage resource usage by processing data through a multi-phased processing pipeline that analyzes the data for reductions in data storage size, duplications, and/or storage times to convert data into a more efficient structure. This may include converting the data to smaller data pieces, deduplicating the same or similar data, or limiting storage in resource intensive data stores.

Abstract: Methods and systems are presented for providing a data control framework that enables storing, sharing, and transferring of data in a secure manner. Data files stored in data repositories are scanned. Content associated with different section of each data file is analyzed, and each section is tagged with a sensitivity level based on the content and a subject matter derived for the data file. Each data file is also assigned to a clearance classification based on an expected viewer of the data file. When sections from a first data file is being transferred to a second data file, a data control mechanism is triggered. If a particular section from the first data file is incompatible with the second data file, the data control mechanism may prevent the particular section from being transferred to the second data file, while allowing the remaining sections being transferred to the second data file.

Abstract: Methods and systems are presented for providing a data control framework that enables storing, sharing, and transferring of data in a secure manner. Data files stored in data repositories are scanned. Content associated with different section of each data file is analyzed, and each section is tagged with a sensitivity level based on the content and a subject matter derived for the data file. Each data file is also assigned to a clearance classification based on an expected viewer of the data file. When sections from a first data file is being transferred to a second data file, a data control mechanism is triggered. If a particular section from the first data file is incompatible with the second data file, the data control mechanism may prevent the particular section from being transferred to the second data file, while allowing the remaining sections being transferred to the second data file.

Abstract: An example method includes receiving a selection of an item; retrieving a user interaction history and a user profile; determining a pattern of user behavior based on the user interaction history and on the user profile; receiving a set of active promotions for the third party; receiving a set of donation-receiving organizations; receiving, from an investment provider, a set of investments; determining an amount of savings based on a comparison of the selected item to the set of active promotions; determining at least one donation-receiving organization and at least one investment based on the pattern of user behavior; and presenting, on a GUI, an interactive element that enables a user to complete a transaction involving the selected item and direct the amount of savings to the at least one determined donation-receiving organization or to the at least one determined investment.

Abstract: An example method includes receiving a request for a transaction; associating the request with a user; receiving, from the user, identification information; retrieving, from a database, first stored authentication information associated with the user; retrieving, from a distributed ledger, second stored authentication information associated with the user; and determining that the received authentication information matches the first stored authentication details and the second stored authentication details and, in response, outputting, by the computing system, a proof of authentication to enable the transaction.

Abstract: Techniques are disclosed relating to automatically generating and analyzing database queries. In various embodiments, a database inquiry assistance system maintains a first machine learning model trained using query history data for a database and a second machine learning model using analysis history for the database. In an embodiment, the system receives from a user system a request for an inquiry into data stored in the database and identifies a sequence of queries for responding to the request, where identifying the sequence of queries includes applying the second machine learning model to the request. The system generates corresponding database query code for implementing one or more of the queries in the sequence of queries, where generating the corresponding database query code includes applying the first machine learning model to descriptors of one or more of the queries, and sends a plan identifying the sequence of queries to the user system.