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Trie

In computer science, a trie (/ˈtraɪ/, /ˈtriː/ ⓘ), also known as a digital tree or prefix tree, is a specialized search tree data structure used to store and retrieve strings from a dictionary or set. Unlike a binary search tree, nodes in a trie do not store their associated key. Instead, each node's position within the trie determines its associated key…

History, Applications & Science

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Research this topic

Explore the main themes, entities and connections around Trie. Start with the topic map, then use the sections below for research and deeper semantic analysis.

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Delete
O(n)
Insert
O(n)
Invented
1960
Invented by
Edward Fredkin, Axel Thue, and René de la Briandais
Operation
Average
Search
O(n)

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

History, etymology, and pronunciation

Operations

Replacing other data structures

Implementation strategies

Applications

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

Trie

Nodes78
Edges77
Triples94
Avg. degree1.97
Density0.025641
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

Trie

Top relations

related to Bitwise tries · 9
Trie → Accordingly, Bitwise, CPU, CPUs, Each, GCC', Search, The, This
related to history · 7
Trie → Axel Thue, Briandais, Edward Fredkin, However, René, The, Tries
related to Implementation strategies · 7
Trie → Another, ASCII, For, Techniques, This, Tries, Using
has application · 6
Trie → DAFSA, DAFSAs, However, String, This, Tries
related to Operations · 6
Trie → As, ASCII, Each, In, The, Tries
related to Insertion · 4
Trie → Each, If, Insertion, The
related to Internet routing · 4
Trie → Compressed, FIB, Forwarding Information Base, IP
related to Bioinformatics · 3
Trie → Bioinformatics, BLAST, Tries
related to Compressed tries · 3
Trie → One, Radix, This
related to Deletion · 3
Trie → Deletion, If, The

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

key tree node string tries search used nodes set keys data prefix also space number value binary associated store null

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
TrieDeleteO(n)1.00infobox
TrieInsertO(n)1.00infobox
TrieInvented19601.00infobox
TrieInvented byEdward Fredkin, Axel Thue, and René de la Briandais1.00infobox
TrieOperationAverage1.00infobox
TrieSearchO(n)1.00infobox
TrieSpaceO(n)1.00infobox
TrieTime complexity in big O notationTime complexity in big O notationOperation Average Worst caseSearch O(n) O(n)Insert O(n) O(n)Delete O(n) O(n)Space complexitySpace O(n) O(wn)1.00infobox
TrieTypeTree1.00infobox
Trieis aordered tree data structure used in the representation of a set of strings over a finite alphabet set0.90text
autocompleteinstance ofwith the connections between nodes defined by individual characters rather than the entire key.Tries are particularly effective for tasks0.80text
spell checkinginstance ofwith the connections between nodes defined by individual characters rather than the entire key.Tries are particularly effective for tasks0.80text

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
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