US 12,436,929 B2
Automated mainframe database maintenance
Marcelo De Souza Vaz, Rio de Janeiro (BR); Marcos Eugenio Fernandes, Sao Paulo (BR); Thiago Bianchi, São Carlos (BR); and Adriana Melges Quintanilha Weingart, Sao Paulo (BR)
Assigned to Kyndryl, Inc., New York, NY (US)
Filed by Kyndryl, Inc., New York, NY (US)
Filed on Feb. 25, 2022, as Appl. No. 17/680,558.
Prior Publication US 2023/0273908 A1, Aug. 31, 2023
Int. Cl. G06F 16/21 (2019.01); G06F 11/07 (2006.01); G06F 16/22 (2019.01); G06F 16/23 (2019.01); G06F 16/2458 (2019.01); G06N 20/00 (2019.01)
CPC G06F 16/217 (2019.01) [G06F 11/0727 (2013.01); G06F 16/2272 (2019.01); G06F 16/2282 (2019.01); G06F 16/2308 (2019.01); G06F 16/2477 (2019.01); G06N 20/00 (2019.01)] 18 Claims
OG exemplary drawing
 
1. A method, comprising:
monitoring, by a computing device, real-time performance metrics of a mainframe database;
obtaining, by the computing device, the real-time performance metrics of the mainframe database based on the monitoring of the real-time performance metrics;
automatically generating, by the computing device, a predicted maintenance task as an output of a trained database maintenance task classification machine learning (ML) model based on an input of the real-time performance metrics;
automatically generating, by the computing device, a time to execute the predicted maintenance task as an output of a trained database maintenance triggering ML model based on an input of the predicted maintenance task and the real-time performance metrics;
automatically generating, by the computing device, maintenance task instructions for the mainframe database based on the predicted maintenance task, the time to execute the predicted maintenance task, and a maintenance profile of the mainframe database;
automatically initiating, by the computing device, the execution of the maintenance task instructions by the mainframe database;
automatically generating, by the computing device, a determination of an anomalous behavior as an output of a trained database monitor anomaly detection ML model based on determining that the real-time performance metrics are an unpredicted behavior of the mainframe database, wherein the trained database monitor anomaly detection ML model is trained with historic performance related metrics of a time series dataset by utilizing a support vector regression algorithm; and
automatically sending, by the computing device, an alert indicating the anomalous behavior to a remote client device via a network connection.