Machine-learning-based, adaptive updating of quantitative data in database system

A computer system for facilitating processing within a computing environment, the computer system comprising:

• a memory;
• and
• a processing circuit in communication with the memory, wherein the computer system is configured to perform a method, the method comprising:
• training one or more machine learning models to facilitate adaptively updating quantitative data in a database system;
• predicting an execution time of one or more database queries to the data structure(s);
• executing the one or more database queries to the data structure(s) and determining, based on the executing, an actual execution time of the one or more database queries to the data structure(s);
• determining real-time query metrics based, in part, on a difference between the predicted execution time of the one or more database queries to the data structure(s) and the actual execution time of the one or more database queries to the data structure(s);
• ascertaining an update urgency index for updating quantitative data for one or more data structures of the database system, the update urgency index being representative of an urgency for updating the quantitative data for the data structure(s) and being based, at least in part, on the real-time query metrics;
• adaptively updating, using the machine learning model(s), the quantitative data, the adaptively updating being based, at least in part, on the ascertained update urgency index;
• and
• optimizing processing of a database query in the database system using, at least in part, the adaptively updated quantitative data.

A computer-implemented method of facilitating processing within a computing environment, the computer-implemented method comprising:

• training one or more machine learning models to facilitate adaptively updating quantitative data in a database system;
• predicting an execution time of one or more database queries to the data structure(s);
• executing the one or more database queries to the data structure(s) and determining, based on the executing, an actual execution time of the one or more database queries to the data structure(s);
• determining real-time query metrics based, in part, on a difference between the predicted execution time of the one or more database queries to the data structure(s) and the actual execution time of the one or more database queries to the data structure(s);
• ascertaining an update urgency index for updating quantitative data for one or more data structures of the database system, the update urgency index being representative of an urgency for updating the quantitative data for the data structure(s) and being based, at least in part, on the real-time query metrics;
• adaptively updating, using the machine learning model(s), the quantitative data, the adaptively updating being based, at least in part, on the ascertained update urgency index;
• and
• optimizing processing of a database query in the database system using, at least in part, the adaptively updated quantitative data.