▁have ▁been ▁proposed ▁that ▁use ▁a ▁fixed ▁number ▁of ▁storage ▁units . ▁ ▁Hyper Log Log ▁algorithm ▁ ▁Stream ing ▁algorithms ▁ ▁To ▁handle ▁the ▁bounded ▁storage ▁constraint , ▁streaming ▁algorithms ▁use ▁a ▁random ization ▁to ▁produce ▁a ▁non - ex act ▁estimation ▁of ▁the ▁distinct ▁number ▁of ▁elements , ▁. ▁State - of - the - art ▁estim ators ▁hash ▁every ▁element ▁ ▁into ▁a ▁low - dimensional ▁data ▁sketch ▁using ▁a ▁hash ▁function , ▁. ▁ ▁The ▁different ▁techniques ▁can ▁be ▁class ified ▁according ▁to ▁the ▁data ▁sketch es ▁they ▁store . ▁ ▁Min / max ▁sketch es ▁ ▁Min / max ▁sketch es ▁ ▁store ▁only ▁the ▁minimum / maxim um ▁hash ed ▁values . ▁Ex amples ▁of ▁known ▁min / max ▁sketch ▁estim ators : ▁Ch assa ing ▁et ▁al . ▁ ▁presents ▁max ▁sketch ▁which ▁is ▁the ▁minimum - var iance ▁un bi ased ▁estim ator ▁for ▁the ▁problem . ▁The ▁continuous ▁max ▁sketch es ▁estim ator ▁ ▁is ▁the ▁maximum ▁lik elihood ▁estim ator . ▁The ▁estim ator ▁of ▁choice ▁in ▁practice ▁is ▁the ▁Hyper Log Log ▁algorithm . ▁ ▁The ▁intu ition ▁behind ▁such ▁estim ators ▁is ▁that ▁each ▁sketch ▁car ries ▁information ▁about ▁the ▁desired ▁quantity . ▁For ▁example , ▁when ▁every ▁element ▁ ▁is ▁associated ▁with ▁a ▁uniform ▁R V , ▁, ▁the ▁expected ▁minimum ▁value ▁of ▁ ▁is ▁. ▁ ▁The ▁hash ▁function ▁guarante es ▁that ▁ ▁is ▁identical ▁for ▁all ▁the ▁appearances ▁of ▁. ▁Thus , ▁the ▁existence ▁of ▁duplicates ▁does ▁not