▁Z DD ▁may ▁not ▁be ▁the ▁best ▁to ▁complete ▁simple ▁searches , ▁yet ▁it ▁is ▁efficient ▁in ▁retr ieving ▁data ▁that ▁is ▁only ▁partially ▁specified , ▁or ▁data ▁that ▁is ▁only ▁supposed ▁to ▁match ▁a ▁key ▁approximately . ▁Complex ▁queries ▁can ▁be ▁handled ▁with ▁ease . ▁Moreover , ▁Z DD s ▁do ▁not ▁involve ▁as ▁many ▁variables . ▁In ▁fact , ▁by ▁using ▁a ▁Z DD , ▁one ▁can ▁represent ▁those ▁five ▁letter ▁words ▁as ▁a ▁sparse ▁function ▁that ▁has ▁ 2 6 × 5 ▁= ▁ 1 3 0 ▁variables , ▁where ▁variable ▁ ▁for ▁example ▁determ ines ▁whether ▁the ▁second ▁letter ▁is ▁“ a ”. ▁To ▁represent ▁the ▁word ▁“ cra zy ”, ▁one ▁can ▁make ▁F ▁true ▁when ▁ ▁and ▁all ▁other ▁variables ▁are ▁ 0 . ▁Thus , ▁F ▁can ▁be ▁considered ▁as ▁a ▁family ▁consisting ▁of ▁the ▁ 5 7 5 7 ▁subsets ▁, ▁etc . ▁With ▁these ▁ 1 3 0 ▁variables ▁the ▁Z DD ▁size ▁Z ( F ) ▁is ▁in ▁fact ▁ 5 0 2 0 ▁instead ▁of ▁ 6 2 3 3 . ▁According ▁to ▁Kn uth , ▁the ▁equivalent ▁size ▁of ▁B ( F ) ▁using ▁a ▁B DD ▁is ▁ 4 6 , 1 8 9 — sign ific antly ▁larger ▁than ▁Z ( F ). ▁In ▁spite ▁of ▁having ▁similar ▁theories ▁and ▁algorithms , ▁Z DD s ▁out perform ▁B DD s ▁for ▁this ▁problem ▁with ▁quite ▁a ▁large ▁margin . ▁Con sequently , ▁Z DD s ▁allow ▁us ▁to ▁perform