javascript

Streaming Percentiles 3.1.0 Released
Analytics c++ ckms gk javascript percentiles streaming
Published: 2019-03-29
Streaming Percentiles 3.1.0 Released
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
Calculating Percentiles on Streaming Data Part 8: Parameterizing Algorithms on Measurement Type
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...
Creating UMD Module from Emscripten using CMake
Emscripten cmake emscripten javascript require.js umd
Published: 2018-04-14
Creating UMD Module from Emscripten using CMake
By default, Emscripten creates a module which can be used from both Node.JS and the browser, but it has the following issues: The module pollutes the global namespace The module is created with the name Module (in my case, I require streamingPercentiles) The module cannot be loaded by some module loaders such as require.js While the above issues can (mostly) be corrected by using –s MODULARIZE=1, it changes the semantics of the resulting JavaScript file, as the module now returns a function rather than an object. 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
Calculating Percentiles on Streaming Data Part 7: Cormode-Korn-Muthukrishnan-Srivastava
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...