Abstract: Embodiments of the present invention provide a divide-and-conquer algorithm which divides expanded data into a cluster of machines. Each portion of data is used to train logistic classification models in parallel, and then combined at the end of the training phase to create a single ordinal model. The training scheme removes the need for synchronization between the parallel learning algorithms during the training period, making training on large datasets technically feasible without the use of supercomputers or computers with specific processing capabilities. Embodiments of the present invention also provide improved estimation and prediction performance of the model learned compared to the existing techniques for training models with large datasets.

Abstract: Embodiments of the present invention provide a divide-and-conquer algorithm which divides expanded data into a cluster of machines. Each portion of data is used to train logistic classification models in parallel, and then combined at the end of the training phase to create a single ordinal model. The training scheme removes the need for synchronization between the parallel learning algorithms during the training period, making training on large datasets technically feasible without the use of supercomputers or computers with specific processing capabilities. Embodiments of the present invention also provide improved estimation and prediction performance of the model learned compared to the existing techniques for training models with large datasets.

Abstract: An integrated data pipeline can take advantage of a streaming service, which can handle tasks such as automated redelivery, as well as a processing service, which can allocate workers on a task- or event-specific basis. Event data is aggregated and compressed for delivery by the streaming service. The streaming service can deliver the data asynchronously to the processing service, which can disaggregate and decompress the data to obtain the original data records. The type of event for each record can be determined to determine whether the data should be processed using online and/or offline processing. For online processing the appropriate fields are determined and data extracted to be passed to the online processing services. For offline processing the record data is concatenated sequentially into mini-batches, then compacted into larger batch files that are stored for subsequent offline processing.

Abstract: An advertising system can be configured to determine advertisements that are responsive to targeting segments included in advertisement requests without having to individually evaluate Boolean or logical targeting expressions associated with each advertisement. For example, the advertising system can be configured to determine, for advertisements, one or more terms that correspond to at least a portion of the respective targeting expressions of the advertisement. When an advertisement request is received, a determination can be made as to whether any of the segments (e.g., age group) associated with that request correspond to any of the terms that were derived from the advertisements. The advertisement system can then determine any advertisements that match the term(s) that correspond to the request.