Capital One Services, LLC

Aditya Relangi

Behdad Forghani

Abhijit Chitnis

Aniketh Bothra

Michael Langford

Methods and systems described herein may include processing a change data capture stream in parallel and optimizing the write operations contained in the change data capture stream such that the write operations may be written to a change table in parallel. Processing the change data capture stream may include decoding the transaction data contained in the change data capture stream in parallel. After the transaction data in the change data capture stream has been decoded, the transaction data may be analyzed to identify write operations that write to the same row. Write operations that write to the same row may be merged into a single write operation. The merging of write operations may be performed until each write operation writes to a different row. The write operations may then be written to the change table in parallel.

Parallel stream processing of change data capture

Examples of the present disclosure can comprise systems and methods for creating and modifying named entity recognition models. The system can use two or more existing named entity recognition models to output responses to natural language queries for which the models have not yet been trained. When the output from the two or more models match, the query and the resulting output can be stored as training data for a new named entity recognition model. If the output from the two or models do not match, the query and the outputs can be stored in an exceptions file for additional review. In some embodiments, the system can comprise one or more processors and a display for providing a user interface (UI).

Training systems for pseudo labeling natural language

A device may receive, from a user device, a request for a recommendation identifying one or more tasks to be performed, of a set of tasks that are part of a job for an organization and associated with managing a set of applications for a product or a service. The device may generate the recommendation by using a data model that has been trained using one or more machine learning techniques to process data identifying a set of application status metrics based on application status data for the set of applications, and events data identifying real-time events associated with the group of sites. The device may provide the recommendation for display on an interface of the user device. The device may perform one or more actions associated with assisting in performance of at least one of the one or more tasks.

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Irving, TX, United States of America

Aditya Relangi