HyperLogLog and other probabilistic data structures for mining in data streams
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Updated
Dec 3, 2014 - Python
HyperLogLog and other probabilistic data structures for mining in data streams
go/golang version of hyperloglog, ported from popular java version java-hll. hyperloglog is an Cardinality estimate algorithm with low memory and low bias
F# HyperLogLog implementation based on C# Microsoft HyperLogLog
Implementation of HyperLogLog algorithms for distinct count estimate
Extension to Clearspring impl. of HLL++, which allows merging directly from a stream
A small tool for comparing HLL/HLL++ implementations
Implementations for FM Sketch, Hyperloglog and Virtual Hyperloglog algorithms tested on real Intenet traffic from CAIDA
Hyper Log Log analytical data processor for LieYing
Spark with probabilistic algortighmts - Bloom filter, HLL, QTree and Count-min sketch
Probabilistic data structures for OCaml
A streaming data pipeline to perform basic analytics with scalability in mind
Implementation of HyperLogLog algorithm to count number of unique elements in data stream.
Implements the Hyper Log Log approximate count-distinct algorithm.
A header-only bit vector library for C . This can be used for implementing dynamic bit-vectors for building Bloom-Filters and Hyper-Logs .
Redis compatible HyperLogLog implementation in Elixir
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