US-12625849-B2 - Systems and methods for generating a data definition language statement

Abstract

Systems and methods for generating a data definition language (DDL) statement for a database management product. The method includes receiving at an interface a partial DDL text input; providing the partial DDL text input to a machine learning model trained on a corpus of DDL statements and configured to analyze the received partial DDL text input and generate a complete DDL statement based on the analysis of the received partial DDL text input and the corpus of DDL statements.

Inventors

• Tom Ulveman Jensen
• Revanth Akella
• Lester Manalastas
• Rekha Gadikota

Assignees

• ROCKET SOFTWARE, INC.

Dates

Publication Date

20260512

Application Date

20241230

Claims (20)

1. 1 . A method for generating a data definition language (DDL) statement for a database management product, the method comprising: receiving at an interface a partial DDL text input; providing the partial DDL text input to a machine learning model trained on a corpus of DDL statements and trained by: receiving a plurality of sample DDL statements, identifying an object type associated with each of the sample DDL statements, creating sequences of tokens representing the objects, wherein the length of the sequences is based on the identified object type, and providing the sequences of tokens to the machine learning model for training, wherein the trained machine learning model then analyzes the received partial DDL text input, and generates a complete DDL statement based on the analysis of the received partial DDL text input and the corpus of DDL statements.
2. 2 . The method of claim 1 further comprising: receiving the corpus of DDL statements, and training the machine learning model on the corpus of DDL statements.
3. 3 . The method of claim 2 wherein training the machine learning model on the corpus of DDL statements includes: separating a sample DDL statement of the corpus into a plurality of object types, creating sequences of tokens representing the objects, and providing the sequences to the machine learning model.
4. 4 . The method of claim 3 wherein the machine learning model includes a neural network.
5. 5 . The method of claim 3 wherein the one or more processors generate the complete DDL statement by predicting at least one text item to follow the partial DDL text input.
6. 6 . The method of claim 1 further comprising: receiving confirmation of the generated complete DDL statement, and using the generated complete DDL statement to modify the database management product.
7. 7 . The method of claim 1 wherein the machine learning model is implemented for a mainframe environment.
8. 8 . The method of claim 6 wherein the machine learning model is implemented for z/OS Interactive System Productivity Facility (ISPF) applications.
9. 9 . A system for generating a data definition language (DDL) statement for a database management product, the system comprising: an interface for at least receiving a partial DDL text input from a user; a database for storing a corpus of DDL statements; and one or more processors executing instructions stored on memory and configured to: execute a machine learning model to analyze the corpus of DDL statements, wherein the machine learning model is trained by: receiving a plurality of sample DDL statements, identifying an object type associated with each of the sample DDL statements, creating sequences of tokens representing the objects, wherein the length of the sequences is based on the identified object type and the machine learning model is trained on the sequences of tokens, and generate a complete DDL statement based on the analysis of the received partial DDL text input and the corpus of DDL statements, wherein the interface is further configured to present the generated complete DDL statement to the user.
10. 10 . The system of claim 9 wherein the one or more processors are further configured to: receive the corpus of DDL statements, and train the machine learning model on the corpus of DDL statements.
11. 11 . The system of claim 10 wherein the one or more processors train the machine learning model on the corpus of DDL statements by: separating a sample DDL statement of the corpus into a plurality of object types, creating sequences of tokens representing the objects, and providing the sequences to the machine learning model.
12. 12 . The system of claim 11 wherein the machine learning model includes a neural network.
13. 13 . The system of 11 wherein the one or more processors generate the complete DDL statement by predicting at least one text item to follow the partial DDL text input.
14. 14 . The system of claim 9 wherein the interface is further configured to receive confirmation of the generated complete DDL statement, and the one or more processors are further configured to use the generated complete DDL statement to modify the database management product.
15. 15 . The system of claim 9 wherein the machine learning model is implemented for a mainframe environment.
16. 16 . The system of claim 15 wherein the machine learning model is implemented for z/OS Interactive System Productivity Facility (ISPF) applications.
17. 17 . A computer program product for generating a data definition language (DDL) statement for a database management product, the computer program product comprising computer executable code embodied in one or more non-transitory computer readable media that, when executing on one or more processors, performs the steps of: receiving at an interface a partial DDL text input; providing the partial DDL text input to a machine learning model trained on a corpus of DDL statements and trained by: receiving a plurality of sample DDL statements, identifying an object type associated with each of the sample DDL statements, creating sequences of tokens representing the objects, wherein the length of the sequences is based on the identified object type, and providing the sequences of tokens to the machine learning model for training, wherein the trained machine learning model then: analyzes the received partial DDL text input, and generates a complete DDL statement based on the analysis of the received partial DDL text input and the corpus of DDL statements.
18. 18 . The computer program product of claim 7 wherein the computer program product further comprises computer executable code that, when executing on the one or more processors, performs the steps of: receiving the corpus of DDL statements, and training the machine learning model on the corpus of DDL statements.
19. 19 . The computer program product of claim 18 wherein training the machine learning model on the plurality of sample DDL statements includes: separating a sample DDL statement of the corpus into a plurality of object types, creating sequences of tokens representing the objects, and providing the sequences to the machine learning model.
20. 20 . The computer program product of claim 17 wherein the machine learning model includes a neural network.

Description

CROSS REFERENCE TO RELATED APPLICATIONS The present application claims the benefit of and priority to U.S. provisional application No. 63/669,829, filed on Jul. 11, 2024, the content of which is hereby incorporated by reference as if set forth in its entirety herein. TECHNICAL FIELD Embodiments described herein generally relate to systems and methods for managing network devices and, more particularly but not exclusively, to systems and methods for managing database management products. BACKGROUND Organizations, individuals, and other entities rely on the Data Definition Language (DDL) syntax to create or otherwise modify database products or objects thereof. Modifying these objects generally involves using Structured Query Language (SQL) statements. These SQL statements may include instructions to add data to an object associated with a database, modify data of an object, remove data from an object, or the like. Implementing desired object instruction(s) may involve statements that are hundreds or even thousands of lines of code. For example, an entity such as a bank may have data records or each customer. Each of these records may link multiple accounts associated with the user (e.g., an account associated with their brokerage, their retirement account, etc.). Updating or changing one of these accounts may involve DDL statements that comprise hundreds or thousands of lines of code. Manually generating these DDL statements can be tedious, time-consuming, and susceptible to errors. A need exists, therefore, for systems and methods that overcome the disadvantages associated with existing techniques. SUMMARY This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description section. This summary is not intended to identify or exclude key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. According to one aspect, embodiments relate to a method for generating a data definition language (DDL) statement for a database management product. The method includes receiving at an interface a partial DDL text input; providing the partial DDL text input to a machine learning model trained on a corpus of DDL statements and configured to analyze the received partial DDL text input and generate a complete DDL statement based on the analysis of the received partial DDL text input and the corpus of DDL statements. In some embodiments, the method further includes receiving the corpus of DDL statements and training the machine learning model on the corpus of DDL statements. In some embodiments, training the machine learning model on the corpus of DDL statements includes separating a sample DDL statement of the corpus into a plurality of object types, creating sequences of tokens representing the objects, and providing the sequences to the machine learning model. In some embodiments, the machine learning model includes a neural network. In some embodiments, one or more processors generate the complete DDL statement by predicting at least one text item to follow the partial DDL text input. In some embodiments, the method further includes receiving confirmation of the generated complete DDL statement, and using the generated complete DDL statement to modify the database management product. In some embodiments, the machine learning model is implemented for a mainframe environment. In some embodiments, the machine learning model is implemented for z/OS Interactive System Productivity Facility (ISPF) applications. According to another aspect, embodiments relate to a system for generating a data definition language (DDL) statement for a database management product. The system includes an interface for at least receiving a partial DDL text input from a user; a database for storing a corpus of DDL statements; and one or more processors executing instructions stored on memory and configured to execute a machine learning model to analyze the corpus of DDL statements, and generate a complete DDL statement based on the analysis of the received partial DDL text input and the corpus of DDL statements, wherein the interface is further configured to present the generated complete DDL statement to the user. In some embodiments, the one or more processors are further configured to receive the corpus of DDL statements and train the machine learning model on the corpus of DDL statements. In some embodiments, the one or more processors train the machine learning model on the corpus of DDL statements by separating a sample DDL statement of the corpus into a plurality of object types, creating sequences of tokens representing the objects, and providing the sequences to the machine learning model. In some embodiments, the machine learning model includes a neural network. In some embodiments, the one or more processors generate the complete DDL statement by predicting at le