US 12,169,492 B1
Systems and processes for adaptive query execution optimization
Gregory Brian Meyer, San Antonio, TX (US); and Steven Moffa, San Antonio, TX (US)
Assigned to United Services Automobile Association (USAA), San Antonio, TX (US)
Filed by United Services Automobile Association (USAA), San Antonio, TX (US)
Filed on Nov. 28, 2022, as Appl. No. 18/070,123.
Claims priority of provisional application 63/284,471, filed on Nov. 30, 2021.
Int. Cl. G06F 16/2453 (2019.01); G06F 16/2455 (2019.01); G06F 16/28 (2019.01)
CPC G06F 16/24542 (2019.01) [G06F 16/2455 (2019.01); G06F 16/287 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, via a processor, a plurality of queries from a client device;
receiving, via the processor, execution statistics for each of the plurality of queries;
determining, via the processor, one or more topics based on one or more characteristics of the plurality of queries;
grouping, via the processor, the plurality of queries into the one or more topics;
calculating, via the processor, execution statistics for each topic of the one or more topics, based upon the execution statistics of queries of a corresponding topic;
applying, via the processor, the plurality of queries and the execution statistics associated with each of the plurality of queries to a machine learning model to identify potentially relevant characteristics of the plurality of queries that result in a threshold level of divergent execution between two or more of the plurality of queries;
providing, to a downstream system, an indication of the execution statistics and the potentially relevant characteristics for each topic of the one or more topics;
performing, based on the execution statistics, an efficiency simulation;
identifying, based on the efficiency simulation, an optimized scheduling for the plurality of queries; and
providing an electronic indication of the identified optimized scheduling of the plurality of queries, wherein the electronic indication comprises an electronic instruction to render a graphical user interface (GUI), the GUI comprising a scheduling recommendation, a recommended time of query execution, one or more recommended topics, and one or more recommended cloud computing resources available for purchase from a cloud service provider by which to execute the queries.