Calculating Percentiles on Streaming Data Part 3: Visualizing Greenwald-Khanna

This is part 3 of my series on calculating percentiles on streaming data.

In an effort to better understand the Greenwald-Khanna [GK01] algorithm, I created a series of visualizations of the cumulative distribution functions of a randomly-generated, normally-distributed data set with $latex \mu$ = 0 and $latex \sigma$ = 1, as the number of random numbers $latex n$ increases from 1 to 1,000.

The way to read these visualizations is to find the percentile you are looking for on the y-axis, then trace horizontally to find the vertical line on the chart which intersects this location, then read the value from the x-axis.

Exact: Exact Percentiles

Greenwald-Khanna $latex \epsilon$ = 0.1: GK 0.1

Greenwald-Khanna $latex \epsilon$ = 0.05: GK 0.05

Greenwald-Khanna $latex \epsilon$ = 0.01: GK 0.01

From these visualizations, it is quite intuitive and clear how the “resolution” of Greenwald-Khanna increases as $latex \epsilon$ decreases, and how the compress operation keeps the number of elements in the summary data set $latex \mathsf{S}$ relatively small as $latex n$ increases.

References

  • [GK01] M. Greenwald and S. Khanna. Space-efficient online computation of quantile summaries. In Proceedings of ACM SIGMOD, pages 58–66, 2001.