What are the three common data generation technologies and their application areas?

Use decision trees, deep learning and iterative proportional fitting to generate data. The method is selected according to the requirements and purpose.

Three common data generation techniques

1. Generation by distribution

For situations where there is no real data but the data analyst understands the distribution of the data set, the analyst can generate various Random samples from distributions such as normal, exponential, chi-square, lognormal, and uniform. This allows different types of data to be simulated for analysis and prediction.

In this technique, the utility of synthetic data depends on how well the analyst understands the specific data environment.

2. Fit real data to known distribution

If you have real data, you can generate synthetic data by fitting the known distribution. Monte Carlo methods can be used to generate data if the parameters of the distribution and the fit to the real data are known.

Although the Monte Carlo method can find the best fit, it may not be practical enough.

Consider using machine learning models such as decision trees to fit non-classical distributions, including multimodal distributions and distributions with no known common characteristics.

Using machine learning to fit distributions can produce highly correlated synthetic data, but overfitting is a risk.

For situations where only part of the real data exists, hybrid synthetic data generation can also be used. In this case, the analyst generates part of the data set based on a theoretical distribution and other parts based on real data.

3. Use deep learning

Deep generative models such as variational autoencoders (VAE) and generative adversarial networks (GAN) can generate synthetic data.

Variational Autoencoder (VAE) is an unsupervised method in which the encoder compresses the original data set into a more compact structure and transmits the data to the decoder. The decoder then produces an output, which is a representation of the original data set. The system is trained by optimizing the correlation between input and output data.

Generative Adversarial Network (GAN), in the GAN model, two networks, the generator and the discriminator, iteratively train the model. The generator takes a random sample of data and generates a synthetic data set. The discriminator compares the synthetically generated data with the real data set based on previously set conditions.

Phase of testing synthetic data

After data synthesis, the utility of the synthetic data is evaluated by comparing the synthetic data with real data. The utility evaluation process has two stages:

Generic comparison: Compares parameters such as distributions and correlation coefficients measured from two data sets.

Workload-aware utility evaluation: Compare output accuracy for specific use cases by analyzing synthetic data.

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