Search results
Results from the WOW.Com Content Network
The count–min sketch was invented in 2003 by Graham Cormode and S. Muthu Muthukrishnan [1] and described by them in a 2005 paper. [2] Count–min sketch is an alternative to count sketch and AMS sketch and can be considered an implementation of a counting Bloom filter (Fan et al., 1998 [3]) or multistage-filter. [1]
The HyperLogLog has three main operations: add to add a new element to the set, count to obtain the cardinality of the set and merge to obtain the union of two sets. Some derived operations can be computed using the inclusion–exclusion principle like the cardinality of the intersection or the cardinality of the difference between two HyperLogLogs combining the merge and count operations.
Bloom filter, Cuckoo filter, Count–min sketch, and Top-K – RedisBloom [55] implements a set of probabilistic data structures for Redis; Others [56] Former implementations include: Graph – RedisGraph [57] implements a queryable property graph RedisGraph has been discontinued, [58] and continued in the form of a fork called FalkorDB. [59]
Min/max sketches [2] [3] store only the minimum/maximum hashed values. Examples of known min/max sketch estimators: Chassaing et al. [4] presents max sketch which is the minimum-variance unbiased estimator for the problem. The continuous max sketches estimator [5] is the maximum likelihood estimator.
Count sketch is a type of dimensionality reduction that is particularly efficient in statistics, machine learning and algorithms. [1] [2] It was invented by Moses Charikar, Kevin Chen and Martin Farach-Colton [3] in an effort to speed up the AMS Sketch by Alon, Matias and Szegedy for approximating the frequency moments of streams [4] (these calculations require counting of the number of ...
Count–min sketch; Distributed hash table; Double hashing; Dynamic perfect hash table; Hash array mapped trie; Hash list; Hash table; Hash tree; Hash trie; Koorde; Prefix hash tree; Rolling hash; MinHash; Ctrie
function lookupByPositionIndex(i) node ← head i ← i + 1 # don't count the head as a step for level from top to bottom do while i ≥ node.width[level] do # if next step is not too far i ← i - node.width[level] # subtract the current width node ← node.next[level] # traverse forward at the current level repeat repeat return node.value end ...
He gave the example of a hyphenation algorithm for a dictionary of 500,000 words, ... Count–min sketch – Probabilistic data structure in computer science;