Systems and methods for synthetic database query generation

1. A system for training models for outputting synthetic database query results, the system comprising:

at least one memory unit for storing instructions; and

at least one processor configured to execute the instructions to perform operations comprising:

receiving a first query input entered by a user at a user interface; determining a type of the first query input;

generating a synthetic dataset using a dataset generator comprising a trained generative adversarial network, the synthetic dataset:

differing by at least a predetermined amount from a reference dataset according to a similarity metric; and

comprising synthetic data portions generated by:

determining a class of sensitive data portions in the first query input;

selecting a subclass of sensitive data portions within the class based on a distribution model;

generating synthetic data portions using a subclass-specific model trained to generate synthetic values for the selected subclass and not for other subclasses within the class; and

replacing the sensitive data portions with the synthetic data portions;

based on the determined first query input type, providing the first query input and the synthetic dataset to a plurality of training models;

training the plurality of training models based on the first query input and the synthetic dataset;

receiving a second query input;

determining a type of the second query input; and

routing the second query input to a selected training model of the plurality of training models based on the determined second query input type and an output format of the selected training model.

2. The system of claim 1 , wherein the operations further comprise generating an expected database result from the first query input.

3. The system of claim 1 , wherein the operations further comprise evaluating, by a model optimizer, performance criteria of the plurality of training models.

4. The system of claim 3 , wherein the performance criteria includes the similarity metric.

5. The system of claim 1 , wherein the operations further comprise:

determining, based on the first query input and a database language interpreter, an output data format;

returning, based on a generation model and the output data format, a synthetic result of the first query input;

extracting information from the generation model; and

displaying the extracted information on the user interface.

6. The system of claim 1 , wherein the reference dataset includes data represented in least one of a structured data format, a semi-structured data format, or an unstructured data format.

7. The system of claim 1 , wherein the first query input comprises customer financial information.

8. The system of claim 7 , wherein determining the type of the second query input includes using a database language interpreter to interpret a language associated with the second query input.

9. The system of claim 7 , wherein the operations further comprise predicting the subclass of sensitive data portions.

10. The system of claim 9 , wherein the subclass of sensitive data portions is predicted based on at least one additional subclass.

11. A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations, the operations comprising:

receiving a first query input entered by a user at a user interface;

determining a type of the first query input;

generating a synthetic dataset using a dataset generator comprising a trained generative adversarial network, the synthetic dataset:

differing by at least a predetermined amount from a reference dataset according to a similarity metric; and

comprising synthetic data portions generated by:

determining a class of sensitive data portions in the first query input;

selecting a subclass of sensitive data portions within the class based on a distribution model:

generating synthetic data portions to replace the sensitive data portions using a subclass-specific model trained to generate synthetic values for the selected subclass and not for other subclasses within the class;

based on the determined first query input type, providing the first query input and the synthetic dataset to a plurality of training models;

training the plurality of training models based on the first query input and the synthetic dataset;

receiving a second query input;

determining a type of the second query input; and

routing the second query input to a selected training model of the plurality of training models based on the determined second query input type and an output format of the selected training model.

12. A computer-implemented method for training models for outputting synthetic database query results, the method comprising:

receiving a first query input entered by a user at a user interface;

determining a type of the first query input;

generating a synthetic dataset using a dataset generator comprising a trained generative adversarial network, the synthetic dataset:

differing by at least a predetermined amount from a reference dataset according to a similarity metric; and

comprising synthetic data portions generated by:

determining a class of sensitive data portions in the first query input; and

selecting a subclass of sensitive data portions within the class based on a distribution model;

generating synthetic data portions to replace the sensitive data portions using a subclass-specific model trained to generate synthetic values for the selected subclass and not for other subclasses within the class;

based on the determined first query input type, providing the first query input and the synthetic dataset to a plurality of training models;

training the plurality of training models based on the first query input and the synthetic dataset;

receiving a second query input;

determining a type of the second query input; and

routing the second query input to a selected training model of the plurality of training models based on the determined second query input type and an output format of the selected training model.

13. The computer-implemented method of claim 12 , wherein the method further comprises generating an expected database result from the first query input.

14. The computer-implemented method of claim 12 , wherein the method further comprises evaluating, by a model optimizer, performance criteria of the plurality of training models.

15. The computer-implemented method of claim 14 , wherein the performance criteria includes the similarity metric.

16. The computer-implemented method of claim 12 , wherein the method further comprises:

determining, based on the first query input and a database language interpreter, an output data format;

returning, based on a generation model and the output data format, a synthetic result of the first query input;

extracting information from the generation model; and

displaying the extracted information on the user interface.

17. The computer-implemented method of claim 12 , wherein the reference dataset includes data represented in at least one of a structured data format or a semi-structured data format.

18. The computer-implemented method of claim 2 , herein the first query input comprises customer financial information.

19. The computer-implemented method of claim 12 , wherein determining the type of the second query input includes using a database language interpreter to interpret a language associated with the second query input.

20. The computer-implemented method of claim 12 , wherein the method further comprises predicting the subclass of sensitive data portions.

21. The computer-implemented method of claim 20 , wherein the subclass of sensitive data portions is predicted based on at least one additional subclass.