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: The present invention relates to a new pharmaceutical composition from natural materials belonging in traditional Chinese medicine and to its preparation method. Said new pharmaceutical composition comprises Astragalus root, Pleurotus fungi, Winnged Euonymus Twig and Mulberry Leaf. At first, astragalus root, Pleurotus fungi, Winnged Euonymus Twig and Mulberry Leaf are extracted by water or alcohol to obtain extractive. The obtained extractive can be got by extracting the above four materials together, or by combining the extractive from the three materials together (stragalus root, Winnged Euonymus Twig and Mulberry Leaf) among above four materials with the extractive from Pleurotus fungi singly. Then the mixed extractive is centrifugalized and separated. If need be, the gained centrifugate can be made into oral formulations in various dose form with excipient by routine method. Said pharmaceutical composition has the effect of regulating immunity on the body.

Abstract: The present invention relates to a new pharmaceutical composition from natural materials belonging in traditional Chinese medicine and to its preparation method. Said new pharmaceutical composition comprises Astragalus root, Pleurotus fungi, Winnged Euonymus Twig and Mulberry Leaf. At first, astragalus root, Pleurotus fungi, Winnged Euonymus Twig and Mulberry Leaf are extracted by water or alcohol to obtain extractive. The obtained extractive can be got by extracting the above four materials together, or by combining the extractive from the three materials together (Astragalus root, Winnged Euonymus Twig and Mulberry Leaf) among above four materials with the extractive from Pleurotus fungi singly. Then the mixed extractive is centrifugalized and separated. If need be, the gained centrifugate can be made into oral formulations in various dose form with excipient by routine method. Said pharmaceutical composition has the effect of regulating immunity on the body.

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.