Granted Patent B2

US 12,625,868 · App. 18/619,786 · Granted May 12, 2026

Inventors: Andrei Lougovtsov (Boston, MA); Jason Arnold (Chicago, IL); Kevin Garner (League City, TX)

Assignee: Ocient Holdings LLC

G06F16/24542G06F11/3419G06F16/24532G06F16/2456

Abstract

Claims (119)

1 . A method for execution within a database system by one or more computing devices, the method comprising:

determining a first query for execution that indicates creation of a first machine learning model;

executing the first query to generate the first machine learning model from a training set of rows based on accessing and processing the training set of rows via a plurality of operators;

updating system configuration data tracking a plurality of objects of a database system to further track the first machine learning model as a corresponding first object tracked via the system configuration data;

determining a second query for execution that indicates applying of the first machine learning model; and

generating query output for the second query via execution of the second query based on applying the first machine learning model to a set of rows in accordance with at least one property of the corresponding first object based on accessing the system configuration data.

2 . The method of claim 1 , wherein the system configuration data indicates a plurality of object groups that collectively includes the plurality of objects, wherein the first corresponding object is mapped to a first object group of the plurality of object groups, and wherein the at least one property of the corresponding first object is based on at least one object group property of the first object group.

3 . The method of claim 1 , wherein the corresponding first object mapped is mapped to a first corresponding set of permissions, and wherein the first corresponding set of permissions is enforced to restrict usage of the first machine learning model based on the corresponding first object being mapped to the first corresponding set of permissions, and wherein the at least one property of the corresponding first object indicates the first corresponding set of permissions.

4 . The method of claim 1 , wherein the system configuration data indicates state data for a corresponding database system based on indicating ones of the plurality of objects that are available for access in query executions at a corresponding time, further comprising:

determining to execute the second query based on determining the first corresponding object is available for access in query executions at the corresponding time, wherein the at least one property of the first corresponding object includes the first corresponding object being available for access in query executions at the corresponding time.

5 . The method of claim 1 , wherein the first machine learning model is configured in accordance with a first selected model type of a plurality of machine learning function types,

wherein the plurality of machine learning function types includes at least two of:

a simple linear regression type;

a multiple linear regression type;

a polynomial regression type;

a linear combination regression type;

a K means type;

a K Nearest Neighbors type;

a logistic regression type;

a naive bayes type;

a nonlinear regression type;

a feedforward network type;

a principal component analysis type;

a support vector machine type;

a decision tree type;

a linear discriminant analysis type;

a Gaussian mixture model type; or

a Sammon mapping type;

wherein the first selected model type corresponds to one of:

the simple linear regression type;

the multiple linear regression type;

the polynomial regression type;

the linear combination regression type;

the K means type;

the K Nearest Neighbors type;

the logistic regression type;

the naive bayes type;

the nonlinear regression type;

the feedforward network type;

the principal component analysis type;

the support vector machine type;

the decision tree type;

the linear discriminant analysis type;

the Gaussian mixture model type; or

the Sammon mapping type.

6 . A database system comprising:

at least one processor, and

at least one memory that stores operations instructions that, when executed by the at least one processor, causes the database system to perform operations that include:

determining a first query for execution that indicates creation of a first machine learning model;

executing the first query to generate the first machine learning model from a training set of rows based on accessing and processing the training set of rows via a plurality of operators;

updating system configuration data tracking a plurality of objects of a database system to further track the first machine learning model as a corresponding first object tracked via the system configuration data;

determining a second query for execution that indicates applying of the first machine learning model; and

generating query output for the second query via execution of the second query based on applying the first machine learning model to a set of rows in accordance with at least one property of the corresponding first object based on accessing the system configuration data.

7 . The database system of claim 6 , wherein the system configuration data indicates a plurality of object groups that collectively includes the plurality of objects, wherein the first corresponding object is mapped to a first object group of the plurality of object groups, and wherein the at least one property of the corresponding first object is based on at least one object group property of the first object group.

8 . The database system of claim 6 , wherein the corresponding first object mapped is mapped to a first corresponding set of permissions, and wherein the first corresponding set of permissions is enforced to restrict usage of the first machine learning model based on the corresponding first object being mapped to the first corresponding set of permissions, and wherein the at least one property of the corresponding first object indicates the first corresponding set of permissions.

9 . The databases system of claim 8 , wherein the database system is further operable to:

determine to execute the second query based on determining a corresponding second query expression adheres to the first corresponding set of permissions based on the corresponding first object being mapped to the first corresponding set of permissions, wherein the query output is generated for the second query based on determining to execute the second query;

determine a third query for execution that indicates applying of the first machine learning model; and

determine to not execute the third query based on determining a corresponding third query expression does not adhere to the first corresponding set of permissions based on the corresponding first object being mapped to the first corresponding set of permissions, wherein corresponding query output is not generated for the third query based on determining to not execute the third query.

10 . The database system of claim 8 , wherein the first corresponding set of permissions indicates, for each of a set of one or more authorized user entities, whether each of a set of operations performed upon the corresponding first object, wherein the set of operations includes at least one of: executing the corresponding first object in executing a corresponding query, reading the corresponding first object in executing a corresponding query, modifying the corresponding first object in executing a corresponding query, or deleting the corresponding first object in executing a corresponding query.

11 . The database system of claim 8 , wherein the database system is further operable to:

receive a permission-setting instruction indicating the first corresponding set of permissions and further indicating the corresponding first object;

set the first corresponding set of permissions for the corresponding first object in the system configuration data based on processing the permission-setting instruction.

12 . The database system of claim 6 , wherein the system configuration data indicates state data for a corresponding database system based on indicating ones of the plurality of objects that are available for access in query executions at a corresponding time, further comprising:

determining to execute the second query based on determining the first corresponding object is available for access in query executions at the corresponding time, wherein the at least one property of the first corresponding object includes the first corresponding object being available for access in query executions at the corresponding time.

13 . The database system of claim 6 , wherein the first machine learning model is configured in accordance with a first selected model type of a plurality of machine learning function types,

wherein the plurality of machine learning function types includes at least two of:

a simple linear regression type;

a multiple linear regression type;

a polynomial regression type;

a linear combination regression type;

a K means type;

a K Nearest Neighbors type;

a logistic regression type;

a naive bayes type;

a nonlinear regression type;

a feedforward network type;

a principal component analysis type;

a support vector machine type;

a decision tree type;

a linear discriminant analysis type;

a Gaussian mixture model type; or

a Sammon mapping type;

wherein the first selected model type corresponds to one of:

the simple linear regression type;

the multiple linear regression type;

the polynomial regression type;

the linear combination regression type;

the K means type;

the K Nearest Neighbors type;

the logistic regression type;

the naive bayes type;

the nonlinear regression type;

the feedforward network type;

the principal component analysis type;

the support vector machine type;

the decision tree type;

the linear discriminant analysis type;

the Gaussian mixture model type; or

the Sammon mapping type.

14 . A non-transitory computer readable storage medium comprises:

at least one memory section that stores operational instructions that, when executed by at least one processing module, causes the at least one processing module to perform operations that include:

determining a first query for execution that indicates creation of a first machine learning model;

executing the first query to generate the first machine learning model from a training set of rows based on accessing and processing the training set of rows via a plurality of operators;

updating system configuration data tracking a plurality of objects of a database system to further track the first machine learning model as a corresponding first object tracked via the system configuration data;

determining a second query for execution that indicates applying of the first machine learning model; and

generating query output for the second query via execution of the second query based on applying the first machine learning model to a set of rows in accordance with at least one property of the corresponding first object based on accessing the system configuration data.

15 . The non-transitory computer readable storage medium of claim 14 , wherein the system configuration data indicates a plurality of object groups that collectively includes the plurality of objects, wherein the first corresponding object is mapped to a first object group of the plurality of object groups, and wherein the at least one property of the corresponding first object is based on at least one object group property of the first object group.

16 . The non-transitory computer readable storage medium of claim 14 , wherein the corresponding first object mapped is mapped to a first corresponding set of permissions, and wherein the first corresponding set of permissions is enforced to restrict usage of the first machine learning model based on the corresponding first object being mapped to the first corresponding set of permissions, and wherein the at least one property of the corresponding first object indicates the first corresponding set of permissions.

17 . The non-transitory computer readable storage medium of claim 16 , wherein the at least one memory section further stores operational instructions that, when executed by the at least one processing module, causes the at least one processing module to:

determine to execute the second query based on determining a corresponding second query expression adheres to the first corresponding set of permissions based on the corresponding first object being mapped to the first corresponding set of permissions, wherein the query output is generated for the second query based on determining to execute the second query;

determine a third query for execution that indicates applying of the first machine learning model; and

determine to not execute the third query based on determining a corresponding third query expression does not adhere to the first corresponding set of permissions based on the corresponding first object being mapped to the first corresponding set of permissions, wherein corresponding query output is not generated for the third query based on determining to not execute the third query.

18 . The non-transitory computer readable storage medium of claim 16 , wherein the first corresponding set of permissions indicates, for each of a set of one or more authorized user entities, whether each of a set of operations performed upon the corresponding first object, wherein the set of operations includes at least one of: executing the corresponding first object in executing a corresponding query, reading the corresponding first object in executing a corresponding query, modifying the corresponding first object in executing a corresponding query, or deleting the corresponding first object in executing a corresponding query.

19 . The non-transitory computer readable storage medium of claim 16 , wherein the at least one memory section further stores operational instructions that, when executed by the at least one processing module, causes the at least one processing module to:

receive a permission-setting instruction indicating the first corresponding set of permissions and further indicating the corresponding first object;

set the first corresponding set of permissions for the corresponding first object in the system configuration data based on processing the permission-setting instruction.

20 . The non-transitory computer readable storage medium of claim 14 , wherein the system configuration data indicates state data for a corresponding database system based on indicating ones of the plurality of objects that are available for access in query executions at a corresponding time, further comprising:

determining to execute the second query based on determining the first corresponding object is available for access in query executions at the corresponding time, wherein the at least one property of the first corresponding object includes the first corresponding object being available for access in query executions at the corresponding time.

Assignments (1)

Continuity (2)

References Cited (35)