Using a data mirror to generate synthetic data

1 . A method, comprising:

receiving, by at least one processor operating a machine learning (ML) model, a sample data set and associated data;

determining, by the at least one processor, at least one feature of the data associated with the sample data set;

determining, by the at least one processor, at least one structural characteristic of the sample data set;

determining, by the at least one processor, and based, at least in part, on one or more of the at least one feature of the data associated with the sample data set and the at least one structural characteristic of the sample data set, that at least a portion of the data associated with the sample data set is categorical data, the categorical data having a categorical data feature;

generating, by the at least one processor, synthetic data, the synthetic data having the same categorical data feature as the categorical data;

packaging, by the at least one processor, the synthetic data into a synthetic data set, the synthetic data set having the same at least one feature and the same at least one structural characteristic as the sample data set; and

distributing, by the at least one processor, the synthetic data set to at least one remote receiver.

2 . The method of claim 1 , further comprising:

determining, by the at least one processor, and based, at least in part, on one or more of the at least one feature of the data associated with the sample data set and the at least one structural characteristic of the sample data set, that at least a second portion of the data is uncategorical data;

generating, by the at least one processor, second synthetic data corresponding to the second portion of the data by at least one of pseudorandom process and a random process; and

packaging, by the at least one processor, the second synthetic data into the synthetic data set.

3 . The method of claim 2 , wherein generating the second synthetic data comprises specifying at least one rule for the second synthetic data and wherein the at least one of the pseudorandom process and the random process generates the second synthetic data while complying with the at least one rule.

4 . The method of claim 1 , further comprising linking, by the at least one processor, at least two separate sections of the sample data set.

5 . The method of claim 4 , wherein the linking comprises determining whether the at least two separate sections have a similarity in at least one feature above a threshold similarity value.

6 . The method of claim 1 , wherein the generating comprises specifying at least one rule for the synthetic data and wherein the ML model generates the synthetic data while complying with the at least one rule.

7 . The method of claim 1 , wherein the generating comprises determining whether the synthetic data conforms with a threshold conformity requirement with respect to the sample data set.

8 . The method of claim 1 , wherein the determining that the at least the portion of the data associated with the sample data set is categorical data comprises determining that at least a portion of the categorical data is at least one of numerical data and datetime data from the at least one feature.

9 . The method of claim 8 , wherein the receiving comprises iterating through data scanned at a specified path to assemble the sample data set.

10 . A system, comprising:

at least one processor; and

at least one non-transitory memory in communication with the at least one processor and storing instructions that, when executed by the at least one processor, perform operations, comprising:

receiving a sample data set and associated data;

determining at least one feature of the data associated with the sample data set;

determining at least one structural characteristic of the sample data set;

determining based, at least in part, on one or more of the at least one feature of the data associated with the sample data set and the at least one structural characteristic of the sample data set, that at least a portion of the data is categorical data, the categorical data having a categorical data feature;

generating, by a machine learning (ML) model, synthetic data, the synthetic data having the same categorical data feature as the categorical data;

packaging the synthetic data into a synthetic data set, the synthetic data set having the same at least one feature and the same at least one structural characteristic as the sample data set; and

distributing the synthetic data set to at least one remote receiver.

11 . The system of claim 10 , wherein the operations further comprise:

determining based, at least in part, on one or more of the at least one feature of the data associated with the sample data set and the at least one structural characteristic of the sample data set, that at least a second portion of the data is uncategorical data;

generating second synthetic data corresponding to the second portion of the data by at least one of pseudorandom process and a random process; and

packaging the second synthetic data into the synthetic data set.

12 . The system of claim 11 , wherein generating the second synthetic data comprises specifying at least one rule for the second synthetic data and wherein the at least one of the pseudorandom process and the random process generates the second synthetic data while complying with the at least one rule.

13 . The system of claim 10 , wherein the operations further comprise linking at least two separate sections of the sample data set.

14 . The system of claim 13 , wherein the linking comprises determining whether the at least two separate sections have a similarity in at least one feature above a threshold similarity value.

15 . The system of claim 10 , wherein the generating comprises specifying at least one rule for the synthetic data and wherein the ML model generates the synthetic data while complying with the at least one rule.

16 . The system of claim 10 , wherein the generating comprises determining whether the synthetic data conforms with a threshold conformity requirement with respect to the sample data set.

17 . The system of claim 10 , wherein the determining that the at least the portion of the data associated with the sample data set is categorical data comprises determining that at least a portion of the categorical data is at least one of numerical data and datetime data from the at least one feature.

18 . The system of claim 17 , wherein the receiving comprises iterating through data scanned at a specified path to assemble the sample data set.

19 . A system, comprising:

at least one processor; and

a memory device communicatively coupled to the at least one processor and storing instructions that, when executed by the at least one processor, perform operations, comprising:

determining a feature associated with a sample data set;

determining a structural characteristic of the sample data set;

determining based, at least in part, on at least one of the feature of the sample data set and the structural characteristic of the sample data set, whether at least a portion of the data is categorical data

based, at least in part, on determining the at least the portion of data is categorical data:

determining a categorical data feature;

generating synthetic data, the synthetic data having the categorical data feature;

packaging the synthetic data into a synthetic data set, the synthetic data set having the feature associated with the sample data set and the structural characteristic associated the sample data set; and

distributing the synthetic data set.

20 . The system of claim 19 , wherein the operations further comprise:

determining based, at least in part, on one or more of the feature of the sample data set and the structural characteristic of the sample data set, that at least a second portion of the data is uncategorical data;

generating second synthetic data; and

packaging the second synthetic data into the synthetic data set.