Abstract: Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user's performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output.

Abstract: Systems and methods for data ingestion in real time are described herein. In an embodiment, a server computer receives a message comprising one or more client events from a storage device which publishes the message in response to storing the one or more client events. The server computer stores the one or more client events as raw event strings which are then parsed into parsed event strings. Identity resolution methods are performed on the parsed event strings. Feature groups are then identified in the parsed event strings and used to generate aggregation keys which are used to aggregate the feature groups prior to storing aggregated data in one or more aggregation tables.

Abstract: Systems and methods for data ingestion in real time are described herein. In an embodiment, a server computer receives a message comprising one or more client events from a storage device which publishes the message in response to storing the one or more client events. The server computer stores the one or more client events as raw event strings which are then parsed into parsed event strings. Identity resolution methods are performed on the parsed event strings. Feature groups are then identified in the parsed event strings and used to generate aggregation keys which are used to aggregate the feature groups prior to storing aggregated data in one or more aggregation tables.

Abstract: Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user’s performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output.

Abstract: Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user's performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output.

Abstract: Systems and methods for data ingestion in real time are described herein. In an embodiment, a server computer receives a message comprising one or more client events from a storage device which publishes the message in response to storing the one or more client events. The server computer stores the one or more client events as raw event strings which are then parsed into parsed event strings. Identity resolution methods are performed on the parsed event strings. Feature groups are then identified in the parsed event strings and used to generate aggregation keys which are used to aggregate the feature groups prior to storing aggregated data in one or more aggregation tables.

Abstract: Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user's performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output.

Abstract: A mermaid costume is disclosed. An exemplary mermaid costume may include a body portion configured to receive the feet and both legs of a user and conform substantially to the body of the user. The mermaid costume may also include a tail portion connected to the body portion. The tail portion together with the body portion provides the user with the appearance of a mermaid. An opening is formed in the body portion to enable two modes for wearing the costume.