Abstract: A system is provided for distributed and authenticated provisioning of encrypted electronic data. In particular, the system may comprise an intelligent monitoring engine that may continuously monitor and log actions and/or requests executed by an endpoint device to identify one or more data request patterns associated with the endpoint device. Based on identifying the data request patterns, the system may transfer encrypted electronic data to the endpoint device for storage, where the encrypted electronic data may be configured with a time to live on the endpoint device. The system may further comprise an unauthorized activity monitor that may monitor the actions taken by the endpoint device to detect potentially unauthorized activity involving the endpoint device. If no unauthorized activity is detected, the system may permit the endpoint device to decrypt the encrypted electronic data to be presented to a user.

Abstract: A system includes a database, a memory, and a processor. The database stores a set of transactions, each of which includes a set of fields. The processor receives a query from a user through a channel. The query is directed to a transaction stored in the database. The transaction includes a set of fields. The processor applies a machine learning policy adapted to determine a view, i.e., a subset of the set of fields that the user is likely to access using the channel. The processor further accesses the database and retrieves the view. The processor then sends the view to the channel.

Abstract: A system includes a first database, a second database, a first processor, a second processor, a third processor, and a fourth processor. The first database includes a first segment assigned to the first processor and a first transaction type and a second segment assigned to the second processor and a second transaction type. Each of the first and second processors receives a request, assigned to its associated transaction type, from the third hardware processor, to store data in the first database. The processor then writes the data into the segment of first database assigned to the processor/transaction type. The processor further stores a command to write the data into the second database in a queue. The fourth processor then executes the commands stored in the queue, thereby writing the data into the second database.

Abstract: A system includes a first database, a second database, a first processor, a second processor, a third processor, and a fourth processor. The first database includes a first segment assigned to the first processor and a first transaction type and a second segment assigned to the second processor and a second transaction type. Each of the first and second processors receives a request, assigned to its associated transaction type, from the third hardware processor, to store data in the first database. The processor then writes the data into the segment of first database assigned to the processor/transaction type. The processor further stores a command to write the data into the second database in a queue. The fourth processor then executes the commands stored in the queue, thereby writing the data into the second database.

Abstract: A system includes a database, a memory, and a processor. The database stores a set of transactions, each of which includes a set of fields. The processor receives a query from a user through a channel. The query is directed to a transaction stored in the database. The transaction includes a set of fields. The processor applies a machine learning policy adapted to determine a view, i.e., a subset of the set of fields that the user is likely to access using the channel. The processor further accesses the database and retrieves the view. The processor then sends the view to the channel.