Version 3.1.0 of the streaming percentiles library has been released.
This change allows you to put streaming analytics classes into STL containers, such as:
1 2 3 4 5 6 7 8 9 10 11 12 13 #include <vector>#include <stmpct/gk.
This is part 8/8 of my Calculating Percentiles on Streaming Data series.
Versions 1.x and 2.x of the C++ library required all measurements to use the type double, and usage of the algorithms looked something like this:
1 2 3 4 5 6 7 8 #include <stmpct/gk.
By default, Emscripten creates a module which can be used from both Node.JS and the browser, but it has the following issues:
This is part 7/8 of my Calculating Percentiles on Streaming Data series.
In 2005, Graham Cormode, Flip Korn, S. Muthukrishnan, and Divesh Srivastava published a paper called Effective Computation of Biased Quantiles over Data Streams [CKMS05]. This paper took the Greenwald-Khanna algorithm [GK01] and made the following notable changes:
Generalized the algorithm to work with arbitrary targeted quantiles, where a targeted quantile is a combination of a quantile $\phi$ and a maximum allowable error $\epsilon$.