Abstract: An example method of message routing includes: receiving, by one or more processors, a request to send a message to a specified user of a plurality of users of a communication services platform; providing a user profile of the specified user to a communication channel selection model, wherein the user profile characterizes actions of one or more predefined action types that were performed by the specified user in response to receiving previous communications; identifying, based on the output of the communication channel selection model, a preferred communication channel for communicating with the specified user; determining, based on the preferred communication channel, a communication strategy for the specified user; and causing, pursuant to the communication strategy, a message to be sent to the specified user.

Abstract: An example method of message routing includes: receiving a request to send a message to a specified user of a plurality of users of a communication services platform; providing a user profile of the specified user to a send time optimization model, wherein the user profile characterizes actions of one or more predefined action types that were performed by the specified user in response to receiving previous communications; identifying, based on the output of the send time optimization model, a preferred time range for communicating with the specified user; and causing, within the preferred time range, a message to be sent to the specified user.

Abstract: Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. One method includes an operation for training a machine-learning program to generate a frequency model that determines a frequency for sending communications to users. The training utilizes training data defined by features related to user information and responses of users to previous communications to the users. The method further includes determining, by the frequency model and based on information about a first user, a first frequency for the first user. The first frequency identifies the number of communications to transmit to the first user per period of time. Further, the method includes operations for receiving a communication request to send one or more communications to the first user and determining send times for the one or more communications to the first user based on the first frequency. The communications are sent at the determined send times.

Abstract: Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. One method includes an operation for training a machine-learning program to generate a frequency model that determines a frequency for sending communications to users. The training utilizes training data defined by features related to user information and responses of users to previous communications to the users. The method further includes determining, by the frequency model and based on information about a first user, a first frequency for the first user. The first frequency identifies the number of communications to transmit to the first user per period of time. Further, the method includes operations for receiving a communication request to send one or more communications to the first user and determining send times for the one or more communications to the first user based on the first frequency. The communications are sent at the determined send times.

Abstract: Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. Send Time Optimization (STO) uses machine learning (ML) to recommend a personalized send time based on a recipient's past engagement patterns. The purpose of the ML model is to learn patterns in the data automatically and use the patterns to make personalized predictions for each recipient. The send time recommended by the model is the time at which the model believes the recipient will be most likely to engage with the message, such as clicking or opening, and use of the send time mode is expected to increase engagement from recipients. Additional customizations include communication-frequency optimization, communication-channel selection, and engagement-scoring model.

Abstract: Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. Send Time Optimization (STO) uses machine learning (ML) to recommend a personalized send time based on a recipient's past engagement patterns. The purpose of the ML model is to learn patterns in the data automatically and use the patterns to make personalized predictions for each recipient. The send time recommended by the model is the time at which the model believes the recipient will be most likely to engage with the message, such as clicking or opening, and use of the send time mode is expected to increase engagement from recipients. Additional customizations include communication-frequency optimization, communication-channel selection, and engagement-scoring model.

Abstract: Methods, systems, and computer programs are presented for the determination of optimal communication scheduling. One method includes an operation for training a machine-learning program to generate a frequency model that determines a frequency for sending communications to users. The training utilizes training data defined by features related to user information and responses of users to previous communications to the users. The method further includes determining, by the frequency model and based on information about a first user, a first frequency for the first user. The first frequency identifies the number of communications to transmit to the first user per period of time. Further, the method includes operations for receiving a communication request to send one or more communications to the first user and determining send times for the one or more communications to the first user based on the first frequency. The communications are sent at the determined send times.

Abstract: A system and method for allowing application programs that are external to the relational database to access the sensitive data in the database in a seamless fashion are described. The application programs are allowed to use existing query statements without having to modify such statements for accessing encrypted data in the relational database.

Abstract: A system and method for a mechanism is provided for automatically selecting a new encryption key for re-encrypting data in a target database. New initialization vectors may be specified for re-encrypting each column of data selected for re-encryption. Further, a new initialization vector may be specified for one or more rows of data of a database table in the target database that is selected for re-encryption.

Abstract: A system and method for providing a mechanism for automating the conversion of the relational database to a secure relational database with little or no impact on the resources of the relational database during the conversion.

Abstract: A system and method for allowing application programs that are external to the relational database to access the sensitive data in the database in a seamless fashion are described. The application programs are allowed to use existing query statements without having to modify such statements for accessing encrypted data in the relational database.

Abstract: A system and method for a mechanism is provided for automatically selecting a new encryption key for re-encrypting data in a target database. New initialization vectors may be specified for re-encrypting each column of data selected for re-encryption. Further, a new initialization vector may be specified for one or more rows of data of a database table in the target database that is selected for re-encryption.