Capital One Services, LLC

Maqbool A Khatri

Matthew Louis Nowak

Narender Pashikant

David Walter Peters

Guganathan Sellamuthu

Kelvin Wong

James C McGRORY

Rees Draminski

Justin Cheng

Sean O'neill

Alan Christopher Weaver

In some implementations, a data quality system may receive an incoming data set for data quality analysis. The data quality system may obtain data quality validation information from one or more data repositories that form a data quality knowledge base. The data quality system may use one or more machine learning models to evaluate whether the incoming data set has one or more anomalies based on the data quality validation information. The data quality system may generate data quality results for the incoming data set based on an output from the one or more machine learning models. The data quality system may update the data quality knowledge base in accordance with the data quality results for the incoming data set.

Artificial intelligence system for data quality resolution

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

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Glen Allen, VA, United States of America

Maqbool A Khatri