The trie seems like a very good idea for your constraints. A “thinking outside the box” alternative: If you can afford some probability of answering “present” for a string that is absent EDIT: if you can afford false positives, use a Bloom filter as suggested by WizardOfOdds in the comments. For k=1, a Bloom filter … Read more
You could read up on Java Trie or look at trie.
How about the Materialized Path design? CREATE TABLE trie ( path VARCHAR(<maxdepth>) PRIMARY KEY, …other attributes of a tree node… ); To store a word like “stackoverflow”: INSERT INTO trie (path) VALUES (‘s’), (‘st’), (‘sta’), (‘stac’), (‘stack’), (‘stacko’), (‘stackov’), (‘stackove’), (‘stackover’), (‘stackover’), (‘stackoverf’), (‘stackoverflo’), (‘stackoverflow’); The materialized path in the tree is the prefixed sequence … Read more
From what I can tell you don’t need to improve the efficiency of Levenshtein Distance, you need to store your strings in a structure that stops you needing to run distance computations so many times i.e by pruning the search space. Since Levenshtein distance is a metric, you can use any of the metric spaces … Read more
This is my own code, pulled from my answer to How to find a word from arrays of characters? : public class Trie { public struct Letter { public const string Chars = “ABCDEFGHIJKLMNOPQRSTUVWXYZ”; public static implicit operator Letter(char c) { return new Letter() { Index = Chars.IndexOf(c) }; } public int Index; public char … Read more
The complexity of creating a trie is O(W*L), where W is the number of words, and L is an average length of the word: you need to perform L lookups on the average for each of the W words in the set. Same goes for looking up words later: you perform L steps for each … Read more
There is no trie data structure in the core Java libraries. This may be because tries are usually designed to store character strings, while Java data structures are more general, usually holding any Object (defining equality and a hash operation), though they are sometimes limited to Comparable objects (defining an order). There’s no common abstraction … Read more
The trie was the first data structure of this kind discovered. The suffix tree is an improvement over the trie (it has suffix links which allow linear error search, the suffix tree trims unnecessary branches of the trie therefore it does not require as much space). The suffix array is a stripped down data structure … Read more
if you are looking for an ANSI C implementation you can “steal” it from FreeBSD. The file you are looking for is called radix.c. It’s used for managing routing data in kernel.
A tree is a general structure of recursive nodes. There are many types of trees. Popular ones are binary tree and balanced tree. A Trie is a kind of tree, known by many names including prefix tree, digital search tree, and retrieval tree (hence the name ‘trie’). Each kind of tree has a different purpose, … Read more