# US 2017/0286487 A1 · Database statistical histogram forecasting > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2017/0286487 A1 - Document ID: US20170286487A1 - Status: Published application - Kind: A1 - Application number: 15/629,077 - Filed: 2017-06-21 - Published: 2017-10-05 - Earliest claimed: 2015-01-16 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Felipe G. Bortoletto; Reinaldo T. Katahira; Craig M. Trim - Web page: https://patentgrep.com/patent/US20170286487A1 ## CPC - G06F 16/24545: {Selectivity estimation or determination} - G06F 16/2462: {Approximate or statistical queries} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method and system for forecasting a histogram in a database system is provided. The method includes determining that database table statistics and historical statistical histograms associated with specified subject matter have been previously retrieved. The database table statistics and historical statistical histograms are retrieved and determined to be frequency based histograms. Historical target values associated with the historical statistical histograms are identified and new target values associated with the historical target values are identified. A value identifying a number of occurrences for identified target values comprising the new target values and the historical target values is forecast and database table histograms comprising the identified target values are stored. ## Claims ### Claim 1 A histogram forecasting method comprising: determining, by a computer processor of a database system, that a plurality of database table statistics and historical statistical histograms associated with specified subject matter have been previously retrieved, wherein said historical statistical histograms comprise frequency based histograms; retrieving, by said computer processor from a historical statistical histogram database, said plurality of database table statistics and said historical statistical histograms associated with said specified subject matter; identifying, by said computer processor, historical target values associated with said historical statistical histograms; forecasting, by said computer processor, new target values associated with said historical target values; forecasting, by said computer processor, a value identifying a number of occurrences for identified target values comprising said new target values and said historical target values; storing, by said computer processor within said database system, database table histograms comprising said identified target values; and executing, by said computer processor based on said database table histograms comprising said identified target values, a database partition scan providing accurate feedback to a data mining algorithm being executed for allocating optimized storage space of said database system with respect to partitions of said database system. ### Claim 2 (depends on 1) The method of claim 1, further comprising: retrieving, by said computer processor reading a full set of said historical statistical histograms, said historical target values. ### Claim 3 (depends on 1) The method of claim 1, further comprising: predicting, by said computer processor, a delta trend comprising a rate of said new target values crossing a lower boundary and an upper boundary of a minimum and a maximum target value range of said historical statistical histograms. ### Claim 4 (depends on 1) The method of claim 1, wherein each historical target value of said historical target values reveals a single dimension. ### Claim 5 (depends on 1) The method of claim 1, wherein each identified value of said identified target values comprises a specified dimension comprising an associated trend identified by said computer processor executing a support vector machine (SVM) algorithm. ### Claim 6 (depends on 1) The method of claim 1, further comprising: providing at least one support service for at least one of creating, integrating, hosting, maintaining, and deploying computer-readable code in the computing system, said code being executed by the computer processor to implement: said determining that said plurality of database table statistics and historical statistical histograms associated with said specified subject matter have been previously retrieved, said retrieving, said determining that said historical statistical histograms comprise frequency based histograms, said identifying, said forecasting said new target values, said forecasting said value, and said storing. ### Claim 7 A database system comprising a memory and a computer processor coupled to said memory, said memory comprising instructions that when executed by the computer processor implement a histogram forecasting method comprising: determining, by said computer processor, that a plurality of database table statistics and historical statistical histograms associated with specified subject matter have been previously retrieved, wherein said historical statistical histograms comprise frequency based histograms; retrieving, by said computer processor from a historical statistical histogram database, said plurality of database table statistics and said historical statistical histograms associated with said specified subject matter; determining, by said computer processor, that said historical statistical histograms comprise frequency based histograms; identifying, by said computer processor, historical target values associated with said historical statistical histograms; forecasting, by said computer processor, new target values associated with said historical target values; forecasting, by said computer processor, a value identifying a number of occurrences for identified target values comprising said new target values and said historical target values; and storing, by said computer processor within said database system, database table histograms comprising said identified target values; and executing, by said computer processor based on said database table histograms comprising said identified target values, a database partition scan providing accurate feedback to a data mining algorithm being executed for allocating optimized storage space of said database system with respect to partitions of said database system. ### Claim 8 (depends on 7) The database system of claim 7, wherein said method further comprises: retrieving, by said computer processor reading a full set of said historical statistical histograms, said historical target values. ### Claim 9 (depends on 7) The database system of claim 7, wherein said method further comprises: predicting, by said computer processor, a delta trend comprising a rate of said new target values crossing a lower boundary and an upper boundary of a minimum and a maximum target value range of said historical statistical histograms. ### Claim 10 (depends on 7) The database system of claim 7, wherein each historical target value of said historical target values reveals a single dimension. ### Claim 11 (depends on 7) The database system of claim 7, wherein each identified value of said identified target values comprises a specified dimension comprising an associated trend identified by said computer processor executing a support vector machine (SVM) algorithm. ### Claim 12 A computer program product, comprising a computer readable hardware storage device storing a computer readable program code, said computer readable program code comprising an algorithm that when executed by a computer processor of a database system server implements a histogram forecasting method, said method comprising: determining, by said computer processor, that a plurality of database table statistics and historical statistical histograms associated with specified subject matter have been previously retrieved; retrieving, by said computer processor from a historical statistical histogram database, said plurality of database table statistics and said historical statistical histograms associated with said specified subject matter; determining, by said computer processor, that said historical statistical histograms comprise frequency based histograms; identifying, by said computer processor, historical target values associated with said historical statistical histograms; forecasting, by said computer processor, new target values associated with said historical target values; forecasting, by said computer processor, a value identifying a number of occurrences for identified target values comprising said new target values and said historical target values; and storing, by said computer processor within said database system, database table histograms comprising said identified target values; and executing, by said computer processor based on said database table histograms comprising said identified target values, a database partition scan providing accurate feedback to a data mining algorithm being executed for allocating optimized storage space of said database system with respect to partitions of said database system. ### Claim 13 (depends on 12) The computer program product of claim 12, wherein said method further comprises: retrieving, by said computer processor reading a full set of said historical statistical histograms, said historical target values. ### Claim 14 (depends on 12) The computer program product of claim 12, wherein said method further comprises: predicting, by said computer processor, a delta trend comprising a rate of said new target values crossing a lower boundary and an upper boundary of a minimum and a maximum target value range of said historical statistical histograms. ### Claim 15 (depends on 12) The computer program product of claim 12, wherein each historical target value of said historical target values reveals a single dimension. ### Claim 16 (depends on 12) The computer program product of claim 12, wherein each identified value of said identified target values comprises a specified dimension comprising an associated trend identified by said computer processor executing a support vector machine (SVM) algorithm.