7.00.02 - Background - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Aster Analytics
Release Number
September 2017
Content Type
Programming Reference
User Guide
Publication ID
English (United States)
Last Update
Fourier Transform converts a signal from its time/space domain representation to a frequency domain representation (consisting of sinusoidal functions). A frequency domain representation of a signal enables much easier computation in (for example):
  • Applying high/low pass filters to image data in image processing
  • Detecting outliers in sensor data, caused by displacement (due to machine vibration, for example)

Frequency domain representations are widely used in engineering, science, and mathematics applications.

For more information about Fourier Transform, see https://en.wikipedia.org/wiki/Fourier_transform.

Fast Fourier Transform (FFT), developed by Cooley and Tukey in 1965, is an algorithm that computes the DFT of a signal. FFT significantly reduces the complexity of the Fourier Transform algorithm by exploiting the symmetry and periodicity of a Fourier Transform and using a divide-and-conquer strategy.

The divide-and conquer-strategy that the Aster Analytics FFT function uses is Radix-2, Radix-4, or Radix-8, for a signal whose length is a power of 2, 4, or 8, respectively.