APPARATUS FOR SYNTHETIC DATA GENERATION
In an embodiment, an apparatus for synthetic data generation is presented. The apparatus includes a processor and a memory communicatively connected to the processor. The memory contains instructions configured to the processor to receive data. The processor is configured to input the data into a generative framework. The generative framework includes a first category of synthetic data generation and a second category of synthetic data generation. The generative framework is configured to input data an output synthetic data through at least a category of synthetic data generation. The processor is configured to generate, based on the generative framework, synthetic data from the received data.
1 . An apparatus for synthetic data generation, comprising:
a processor; and
a memory communicatively connected to the processor, the memory containing instructions configuring the processor to:
receive data;
input the data into a generative framework, the generative framework comprising:
a plurality of categories of synthetic data generation, wherein the plurality of categories of synthetic data generation includes at least:
a first category of synthetic data generation; and
a second category of synthetic data generation, wherein the generative framework is configured to input data and output synthetic data through at least a category of synthetic data generation; and
generate, based on the generative framework, synthetic data from the received data.
2 . The apparatus of claim 1 , wherein the processor is further configured to validate an aspect of the synthetic data.
3 . The apparatus of claim 2 , wherein the aspect includes evaluation of one of veracity, variety, volume, or a combination thereof, of the synthetic data.
4 . The apparatus of claim 1 , wherein the first category of synthetic data generating methods includes a plurality of generative artificial intelligence architectures.
5 . The apparatus of claim 1 , wherein the first category of synthetic data generating methods includes a hierarchical modeling algorithm (HMA).
6 . The apparatus of claim 5 , wherein the processor is further configured to extract combination data of the data and feed the extracted combination data to the HMA as metadata.
7 . The apparatus of claim 1 , wherein the second category of synthetic data generation methods includes a distribution-based generation.
8 . The apparatus of claim 1 , wherein the synthetic data generated from the generative framework maintains referential integrity of the data.
9 . The apparatus of claim 1 , wherein the generative framework is further configured to generate a free text variable through a large language model (LLM).
10 . The apparatus of claim 1 , wherein the processor is further configured to:
receive user input, the user input including a selection of a category of synthetic data generating methods of the generative framework; and
generate the synthetic data through category of synthetic data generating methods selected from the user input.
11 . A method of synthetic data generation using a computing device, comprising:
receiving data;
inputting the data into a generative framework, the generative framework comprising:
a plurality of categories of synthetic data generation, the plurality of categories of synthetic data generation includes at least:
a first category of synthetic data generation; and
a second category of synthetic data generation, wherein the generative framework is configured to input data and output synthetic data through at least a category of synthetic data generation; and
generating, based on the generative framework, synthetic data from the received data.
12 . The method of claim 11 , further comprising validating, by the processor, an aspect of the synthetic data.
13 . The method of claim 12 , wherein the aspect includes evaluation of one of veracity, variety, volume, or a combination thereof, of the synthetic data.
14 . The method of claim 11 , wherein the first category of synthetic data generation includes a plurality of generative artificial intelligence architectures.
15 . The method of claim 11 , wherein the first category of synthetic data generation includes a hierarchical modeling algorithm (HMA).
16 . The method of claim 15 , further comprising extracting, by the processor, combination data of the data and feeding the extracted combination data to the HMA as metadata.
17 . The method of claim 11 , wherein the second category of synthetic data generation includes a distribution-based generation.
18 . The method of claim 11 , wherein the synthetic data generated from the generative framework maintains referential integrity of the data.
19 . The method of claim 11 , further comprising generating a free text variable through a large language model (LLM) through the generative framework.
20 . The method of claim 11 , further comprising:
receiving user input, the user input including a selection of a category of synthetic data generation of the generative framework; and
generating the synthetic data through the category of synthetic data generation selected from the user input.