Longest common substring

In computer science, a longest common substring of two or more strings is a longest string that is a substring of all of them. There may be more than one longest common substring. Applications include data deduplication and plagiarism detection.

Examples

The picture shows two strings where the problem has multiple solutions. Although the substring occurrences always overlap, it is impossible to obtain a longer common substring by "uniting" them.

The strings "ABABC", "BABCA" and "ABCBA" have only one longest common substring, viz. "ABC" of length 3. Other common substrings are "A", "AB", "B", "BA", "BC" and "C".

  ABABC
    |||
   BABCA
    |||
    ABCBA

Problem definition

Given two strings, $S$ of length $m$ and $T$ of length $n$ , find a longest string which is substring of both $S$ and $T$ .

A generalization is the k-common substring problem. Given the set of strings $S=\{S_{1},\ldots ,S_{K}\}$ , where $|S_{i}|=n_{i}$ and ${\textstyle \sum n_{i}=N}$ . Find for each $2\leq k\leq K$ , a longest string which occurs as substring of at least $k$ strings.

Algorithms

One can find the lengths and starting positions of the longest common substrings of $S$ and $T$ in $\Theta$ $(n m)$ time with the help of a generalized suffix tree. A faster algorithm can be achieved in the word RAM model of computation if the size $\sigma$ of the input alphabet is in $2^{o\left({\sqrt {\log(n m)}}\right)}$ . In particular, this algorithm runs in ${\textstyle O\left((n m)\log \sigma /{\sqrt {\log(n m)}}\right)}$ time using $O\left((n m)\log \sigma /\log(n m)\right)$ space.^[1] Solving the problem by dynamic programming costs $\Theta (nm)$ . The solutions to the generalized problem take $\Theta (n_{1} \cdots n_{K})$ space and $\Theta (n_{1}\cdots n_{K})$ time with dynamic programming and take $\Theta (n_{1} \cdots n_{K})$ time with a generalized suffix tree.

Suffix tree

The longest common substrings of a set of strings can be found by building a generalized suffix tree for the strings, and then finding the deepest internal nodes which have leaf nodes from all the strings in the subtree below it. The figure on the right is the suffix tree for the strings "ABAB", "BABA" and "ABBA", padded with unique string terminators, to become "ABAB$0", "BABA$1" and "ABBA$2". The nodes representing "A", "B", "AB" and "BA" all have descendant leaves from all of the strings, numbered 0, 1 and 2.

Building the suffix tree takes $\Theta (N)$ time (if the size of the alphabet is constant). If the tree is traversed from the bottom up with a bit vector telling which strings are seen below each node, the k-common substring problem can be solved in $\Theta (NK)$ time. If the suffix tree is prepared for constant time lowest common ancestor retrieval, it can be solved in $\Theta (N)$ time.^[2]

Dynamic programming

The following pseudocode finds the set of longest common substrings between two strings with dynamic programming:

function LongestCommonSubstring(S[1..r], T[1..n])
    L := array(1..r, 1..n)
    z := 0
    ret := {}

    for i := 1..r
        for j := 1..n
            if S[i] = T[j]
                if i = 1 or j = 1
                    L[i, j] := 1
                else
                    L[i, j] := L[i − 1, j − 1]   1
                if L[i, j] > z
                    z := L[i, j]
                    ret := {S[i − z   1..i]}
                else if L[i, j] = z
                    ret := ret ∪ {S[i − z   1..i]}
            else
                L[i, j] := 0
    return ret

This algorithm runs in $O(nr)$ time. The array L stores the length of the longest common suffix of the prefixes S[1..i] and T[1..j] which end at position i and j, respectively. The variable z is used to hold the length of the longest common substring found so far. The set ret is used to hold the set of strings which are of length z. The set ret can be saved efficiently by just storing the index i, which is the last character of the longest common substring (of size z) instead of S[i-z 1..i]. Thus all the longest common substrings would be, for each i in ret, S[(ret[i]-z)..(ret[i])].

The following tricks can be used to reduce the memory usage of an implementation:

Keep only the last and current row of the DP table to save memory ( $O(\min(r,n))$ $O(\min(r,n))$ instead of $O(nr)$ $O(nr)$ )
- The last and current row can be stored on the same 1D array by traversing the inner loop backwards
Store only non-zero values in the rows. This can be done using hash-tables instead of arrays. This is useful for large alphabets.

References

^ Charalampopoulos, Panagiotis; Kociumaka, Tomasz; Pissis, Solon P.; Radoszewski, Jakub (Aug 2021). Mutzel, Petra; Pagh, Rasmus; Herman, Grzegorz (eds.). Faster Algorithms for Longest Common Substring. European Symposium on Algorithms. Leibniz International Proceedings in Informatics (LIPIcs). Vol. 204. Schloss Dagstuhl. doi:10.4230/LIPIcs.ESA.2021.30. Here: Theorem 1, p.30:2.
^ Gusfield, Dan (1999) [1997]. Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. USA: Cambridge University Press. ISBN 0-521-58519-8.

External links

[CKPR21-1] Charalampopoulos, Panagiotis; Kociumaka, Tomasz; Pissis, Solon P.; Radoszewski, Jakub (Aug 2021). Mutzel, Petra; Pagh, Rasmus; Herman, Grzegorz (eds.). Faster Algorithms for Longest Common Substring. European Symposium on Algorithms. Leibniz International Proceedings in Informatics (LIPIcs). Vol. 204. Schloss Dagstuhl. doi:10.4230/LIPIcs.ESA.2021.30. Here: Theorem 1, p.30:2.

[Gus97-2] Gusfield, Dan (1999) [1997]. Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. USA: Cambridge University Press. ISBN 0-521-58519-8.

[1]

[2]

v t e Strings
String metric	Approximate string matching Bitap algorithm Damerau–Levenshtein distance Edit distance Gestalt pattern matching Hamming distance Jaro–Winkler distance Lee distance Levenshtein automaton Levenshtein distance Wagner–Fischer algorithm
String-searching algorithm	Apostolico–Giancarlo algorithm Boyer–Moore string-search algorithm Boyer–Moore–Horspool algorithm Knuth–Morris–Pratt algorithm Rabin–Karp algorithm Raita algorithm Trigram search Two-way string-matching algorithm Zhu–Takaoka string matching algorithm
Multiple string searching	Aho–Corasick Commentz-Walter algorithm
Regular expression	Comparison of regular-expression engines Regular grammar Thompson's construction Nondeterministic finite automaton
Sequence alignment	BLAST Hirschberg's algorithm Needleman–Wunsch algorithm Smith–Waterman algorithm
Data structure	DAFSA Suffix array Suffix automaton Suffix tree Generalized suffix tree Rope Ternary search tree Trie
Other	Parsing Pattern matching Compressed pattern matching Longest common subsequence Longest common substring Sequential pattern mining Sorting String rewriting systems String operations