# US 12,117,998 B2 · Machine-learning-based, adaptive updating of quantitative data in database system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,117,998 B2 - Document ID: US12117998B2 - Status: Granted - Kind: B2 - Application number: 18/454,230 - Filed: 2023-08-23 - Published: 2023-12-21 - Granted: 2024-10-15 - Earliest claimed: 2022-02-09 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Ling Qin; Fu Fei Xu; Jie Zhang; Guo Dong Wan; Yi Lei Wang; Wen Jing Shi; Yuan Cao - Web page: https://patentgrep.com/patent/US12117998B2 ## CPC - G06F 16/2453: Query optimisation - G06F 16/2272: {Management thereof} - G06F 16/25: Integrating or interfacing systems involving database management systems - G06N 3/08: Learning methods - G06N 20/00: Machine learning - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Machine-learning-based, adaptive updating of quantitative data in a database system is provided, which includes training one or more machine learning models to facilitate adaptively updating quantitative data in the database system, and ascertaining an update urgency index for updating the quantitative data for one or more data structures of the database system. The update urgency index is representative of an urgency for updating the quantitative data for the data structure(s) and is based, at least in part, on real-time query metrics. The machine learning model(s) is used to adaptively update the quantitative data, where the adaptively updating is based, at least in part, on the ascertained update urgency index. Processing of a database query is optimized in the database system using the adaptively updated quantitative data. ## Claims ### Claim 1 A computer program product for facilitating processing within a computing environment, the computer program product comprising: one or more computer-readable storage media having program instructions embodied therewith, the program instructions being readable by a processing circuit to cause the processing circuit to perform a 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 the adaptively updated quantitative data. ### Claim 2 (depends on 1) The computer program product of claim 1, wherein the real-time query metrics comprise a volume and frequency of database queries to the data structure(s) in the database system. ### Claim 3 (depends on 1) The computer program product of claim 1, wherein the real-time query metrics are based, at least in part, on collecting M top-most database queries to the data structure(s) in order of processor usage for a time interval and estimating therefor a deviation in usage from an estimated processor usage for the M top-most database queries, the estimated deviation being representative of a performance deviation of the M top-most database queries, where M≥1. ### Claim 4 (depends on 1) The computer program product of claim 1, wherein the 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) exceeding a threshold predicts poor query performance. ### Claim 5 (depends on 1) The computer program product of claim 1, wherein the predicting, by the machine learning model(s), the execution time for the one or more database queries uses regression analysis based on time series data. ### Claim 6 (depends on 1) The computer program product of claim 1, wherein the adaptively updating comprises adaptively updating the quantitative data based on a relative urgency of the ascertained update urgency index, the adaptively updating including currently proceeding with updating of the quantitative data where the ascertained update urgency index indicates updating of the quantitative data is urgent, and delaying proceeding with updating of the quantitative data where the ascertained update urgency index indicates that the updating is not urgent. ### Claim 7 (depends on 1) The computer program product of claim 1, further comprising identifying a list of data structures for which the update urgency index is to be ascertained, the one or more data structures being in the list of data structures, the list of data structures including any data structures with major data changes since a last updating of quantitative data, any data structures with no quantitative data, and any data structures with stale quantitative data. ### Claim 8 (depends on 7) The computer program product of claim 7, further comprising predictively identifying, by the machine learning model(s), a data structure in the database system with degrading query execution performance, wherein the list of data structures further includes the predictively identified data structure with degrading query execution performance. ### Claim 9 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. ### Claim 10 (depends on 9) The computer system of claim 9, wherein the real-time query metrics comprise a volume and frequency of database queries to the data structure(s) in the database system. ### Claim 11 (depends on 9) The computer system of claim 9, wherein the real-time query metrics are based, at least in part, on collecting M top-most database queries to the data structure(s) in order of processor usage for a time interval and estimating therefor a deviation in usage from an estimated processor usage for the M top-most database queries, the estimated deviation being representative of a performance deviation of the M top-most database queries, where M≥1. ### Claim 12 (depends on 9) The computer system of claim 9, wherein the 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) exceeding a threshold predicts poor query performance. ### Claim 13 (depends on 9) The computer system of claim 9, wherein the predicting, by the machine learning model(s), the execution time for the one or more database queries uses regression analysis based on time series data. ### Claim 14 (depends on 9) The computer system of claim 9, further comprising identifying a list of data structures for which the update urgency index is to be ascertained, the one or more data structures being in the list of data structures, the list of data structures including any data structures with major data changes since a last updating of quantitative data, any data structures with no quantitative data, and any data structures with stale quantitative data. ### Claim 15 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. ### Claim 16 (depends on 15) The computer-implemented method of claim 15, wherein the real-time query metrics comprise a volume and frequency of database queries to the data structure(s) in the database system. ### Claim 17 (depends on 15) The computer-implemented method of claim 15, wherein the real-time query metrics are based, at least in part, on collecting M top-most database queries to the data structure(s) in order of processor usage for a time interval and estimating therefor a deviation in usage from an estimated processor usage for the M top-most database queries, the estimated deviation being representative of a performance deviation of the M top-most database queries, where M≥1.