Capital One Services, LLC

Guganathan Sellamuthu

Narender Pashikant

Maqbool A Khatri

Brian Burgess

Benjamin Cabell Glancy

Disclosed embodiments pertain to automatic failover for non-relational databases. A machine learning model can be generated and trained with historical failover data. The historical failover data can be related to previous primary node failures or previous region failures of a plurality of non-relational databases. A non-relational database may be monitored for real-time or near-real-time data. The data can be input into the machine learning model to predict the likelihood that a primary region is in a failure state. A new primary node can be designated automatically when the likelihood satisfies a predetermined threshold. Data services are thus automatically transferred from a primary node in the failing region to a new primary node in a non-failing region.

Automatic failover of a non-relational database

A system method of managing a software repository. The method including receiving a dataset comprising a set of computer instructions, retrieving feature-related data from the dataset, determining, by a machine learning model executing on the computing device, a function of the computer instructions based on the feature-related data, and generating a synopsis of the function of the computer instructions. The method can include transmitting the synopsis to a user interface and receiving feedback from a user indicative of whether the synopsis accurately describes the function of the computer instructions. If the feedback indicates that the synopsis accurately describes the function of the computer instructions, the method can include storing the synopsis in a memory of the computing device. If the feedback indicates that the synopsis inaccurately describes the function of the computer instructions, the method can include generating a revised synopsis of the function of the computer instructions.

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Glen Allen, VA, United States of America

Guganathan Sellamuthu