Abstract: Techniques for evaluating a user experience experiment designed to use one user experience variant selected from a number of user experience variants as a global-best user experience variant to be used across users relative to a machine model trained to use user data to identify a user-preferred user experience variant. Disclosed systems and methods provide techniques for optimizing user response. In one embodiment, a global-best user experience variant is evaluated by comparing an aggregate user response determined for the global-best user experience variant to an aggregate user response determined using user response predictions determined using the trained machine model, and using the outcome of the comparison to make a recommendation as to which one of the global-best user experience variant and the trained machine model to adopt for providing a user experience to users.

Abstract: Techniques for evaluating a user experience experiment designed to use one user experience variant selected from a number of user experience variants as a global-best user experience variant to be used across users relative to a machine model trained to use user data to identify a user-preferred user experience variant. Disclosed systems and methods provide techniques for optimizing user response. In one embodiment, a global-best user experience variant is evaluated by comparing an aggregate user response determined for the global-best user experience variant to an aggregate user response determined using user response predictions determined using the trained machine model, and using the outcome of the comparison to make a recommendation as to which one of the global-best user experience variant and the trained machine model to adopt for providing a user experience to users.

Abstract: The present teaching relates to streaming data querying and providing query results. In one example, a query is received for events happening after receipt of the query. A plurality of data records are obtained, where each of the data records corresponds to an event happening after receipt of the query. For each of the plurality of data records, it is determined whether the data record matches the query. Based on the determining, one or more query results are determined and provided as a response to the query.

Abstract: Techniques for evaluating a user experience experiment designed to use one user experience variant selected from a number of user experience variants as a global-best user experience variant to be used across users relative to a machine model trained to use user data to identify a user-preferred user experience variant. Disclosed systems and methods provide techniques for optimizing user response. In one embodiment, a global-best user experience variant is evaluated by comparing an aggregate user response determined for the global-best user experience variant to an aggregate user response determined using user response predictions determined using the trained machine model, and using the outcome of the comparison to make a recommendation as to which one of the global-best user experience variant and the trained machine model to adopt for providing a user experience to users.

Abstract: The present teaching generally relates to detecting data bucket discrepancies associated with online experiments. In a non-limiting embodiment, a monitoring layer may be generated within an online experimentation platform that includes at least a first layer, and where a first online experiment is associated with the first layer, the monitoring layer includes a monitoring layer data bucket, and the first layer includes at least a first data bucket. First data representing user activity associated with a first plurality of identifiers may be obtained, the user activity being associated with the first layer. Second data representing at least one user engagement parameter may be generated, and a first discrepancy between the first and second data may be determined. The first discrepancy indicating a first amount of identifiers that include a first metadata tag associated with the first layer and lack a second metadata tag associated with the monitoring layer.

Abstract: Users may interact with a multitude of content on content sources. Content sources may desire to identify sessions that the user created. As provided herein, a user interacting with a content source may be identified. Source data and session data regarding the user interaction may be extracted. A data pipeline may be generated based upon the source data and the session data. The data pipeline may be sessionized into a single data feed based upon a finished current events interval (e.g., sessions that are activated and terminated during a first interval), a finished last events interval (e.g., sessions that are activated during the first interval and terminated during a second interval), a second finished current events interval (e.g., sessions that are activated and terminated during the second interval), and/or a second finished last events interval (e.g., sessions that are activated during the second interval and terminated during a third interval).

Abstract: The present teaching generally relates to detecting data bucket discrepancies associated with online experiments. In a non-limiting embodiment, a monitoring layer may be generated within an online experimentation platform that includes at least a first layer, and where a first online experiment is associated with the first layer, the monitoring layer includes a monitoring layer data bucket, and the first layer includes at least a first data bucket. First data representing user activity associated with a first plurality of identifiers may be obtained, the user activity being associated with the first layer. Second data representing at least one user engagement parameter may be generated, and a first discrepancy between the first and second data may be determined. The first discrepancy indicating a first amount of identifiers that include a first metadata tag associated with the first layer and lack a second metadata tag associated with the monitoring layer.

Abstract: The present teaching relates to streaming data querying and providing query results. In one example, a query is received for events happening after receipt of the query. A plurality of data records are obtained, where each of the data records corresponds to an event happening after receipt of the query. For each of the plurality of data records, it is determined whether the data record matches the query. Based on the determining, one or more query results are determined and provided as a response to the query.

Abstract: Users may interact with a multitude of content on content sources. Content sources may desire to identify sessions that the user created. As provided herein, a user interacting with a content source may be identified. Source data and session data regarding the user interaction may be extracted. A data pipeline may be generated based upon the source data and the session data. The data pipeline may be sessionized into a single data feed based upon a finished current events interval (e.g., sessions that are activated and terminated during a first interval), a finished last events interval (e.g., sessions that are activated during the first interval and terminated during a second interval), a second finished current events interval (e.g., sessions that are activated and terminated during the second interval), and/or a second finished last events interval (e.g., sessions that are activated during the second interval and terminated during a third interval).

Abstract: In one embodiment, data generated via a map process and/or reduce process may be obtained. A request message may be sent to a server, where the request message indicates a request for a location in storage at which the data is to be stored. Upon receiving the location from the server, the data may be copied to the location in the storage. A commit message may be sent to the server, where the commit message indicates that the data has been copied to the location. In addition, the data may be deleted.

Abstract: In one embodiment, data generated via a map process and/or reduce process may be obtained. A request message may be sent to a server, where the request message indicates a request for a location in storage at which the data is to be stored. Upon receiving the location from the server, the data may be copied to the location in the storage. A commit message may be sent to the server, where the commit message indicates that the data has been copied to the location. In addition, the data may be deleted.