# ckms

Streaming Percentiles 3.1.0 Released
Analytics c++ ckms gk javascript percentiles streaming
Published: 2019-03-29
Version 3.1.0 of the streaming percentiles library has been released. The streaming percentiles library is a cross-platform, multi-language (C++ and JavaScript) implementation of a number of online (single-pass) percentile algorithms. This version of the streaming percentiles library adds support for copy construction, assignment, move construction, and move assignment on all analytics classes. 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. Read more...
Calculating Percentiles on Streaming Data Part 8: Parameterizing Algorithms on Measurement Type
Calculating Percentiles on Streaming Data c++ ckms gk javascript percentiles streaming
Published: 2018-12-21
This is part 8/8 of my Calculating Percentiles on Streaming Data series. As mentioned in part 6 of this series, I have published a C++ and JavaScript library which implements a number of streaming percentile algorithms on GitHub at https://github.com/sengelha/streaming-percentiles. 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. Read more...
Calculating Percentiles on Streaming Data Part 7: Cormode-Korn-Muthukrishnan-Srivastava
Calculating Percentiles on Streaming Data c++ ckms gk javascript percentiles streaming
Published: 2018-03-29
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 $\epsilon$. Read more...