Abstract: A method, apparatus, and computer program product are disclosed for improved machine learning using a statistical model. In the context of an apparatus, some example embodiments include a processor configured to cause retrieval of information regarding a plurality of consumers, and modeling circuitry configured to train a statistical model of the plurality of consumers based on the retrieved information, and predict, using the statistical model, an incremental booking value associated with the promotion for each consumer of the plurality of consumers. The processor is further configured to select a subset of the plurality of consumers for receiving impressions of the promotion. Some example embodiments may further include communications circuitry configured to transmit an impression of the promotion to each consumer in the subset of the plurality of consumers.

Abstract: The present disclosure relates to methods, systems, and apparatuses for identifying related records in a database.

Abstract: A method, apparatus, and computer program product are disclosed for improved machine learning using a statistical model. In the context of an apparatus, some example embodiments include a processor configured to cause retrieval of information regarding a plurality of consumers, and modeling circuitry configured to train a statistical model of the plurality of consumers based on the retrieved information, and predict, using the statistical model, an incremental booking value associated with the promotion for each consumer of the plurality of consumers. The processor is further configured to select a subset of the plurality of consumers for receiving impressions of the promotion. Some example embodiments may further include communications circuitry configured to transmit an impression of the promotion to each consumer in the subset of the plurality of consumers.

Abstract: Various systems, mediums, and methods for storing and retrieving data include a non-transitory memory and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations. The operations include obtaining base data associated with a first entity, generating predictive data based on the base data using a predictive model, and providing the predictive data to a second entity. The predictive model includes a plurality of model parameters learned according to a supervised learning process. The base data includes access-restricted data associated with the first entity, and the predictive data does not include the access-restricted data.

Abstract: Technologies are described for updating a live system with static changes. According to some examples, changes such as installing and/or removing features may be applied to a cloud based service in live environment without disrupting the service. New software may be patched and installed on an upgraded machine (i.e., server). New features, diagnostics, and language packs may be added. Existing features and certificates may be removed. To accomplish these, new software to be installed and existing software to be removed may be detected. Removal and additions may be performed without preparation and activation of replacement farms.

Abstract: Aspects of the present disclosure provide systems and methods for directly transferring tenant data hosted on a source domain to a target domain, wherein the source and target domains are associated with different server farms. Additionally, where the source domain is managed by a source management layer and the target domain is managed by target management layer, which source and target management layers are not in a trust relationship. Aspects describe establishing a secure, direct communication bus between the source and target management layers in order to accomplish a plurality of steps involved in transferring the tenant, wherein tenant data transferred thereon is encrypted. In example aspects, the direct communication bus terminates upon completion of the tenant data transfer.

Abstract: Various systems and methods for domain authentication are described herein. In an example, the method may include detecting a domain from a request of a tenant for access to a farm. The method may also include identifying a presence of a site ID from a database of the farm based on the domain. The method may also include sending an authentication request to a default site or a custom site, the authentication request managed through a site manager based on the identified presence of the site ID in the database of the farm. The method may also include routing traffic from the tenant to the farm in response to satisfaction of the authentication request.

Abstract: Technologies are described for updating a live system with static changes. According to some examples, changes such as installing and/or removing features may be applied to a cloud based service in live environment without disrupting the service. New software may be patched and installed on an upgraded machine (i.e., server). New features, diagnostics, and language packs may be added. Existing features and certificates may be removed. To accomplish these, new software to be installed and existing software to be removed may be detected. Removal and additions may be performed without preparation and activation of replacement farms.