"String-searching algorithms, sometimes called string-matching algorithms, are an important class of string algorithms that try to find a place where one or several strings (also called patterns) are found within a larger string or text."
This subfield focuses on the development of algorithms for processing strings, such as searching for patterns or computing distances between strings.
Character manipulation: This involves working with individual characters in a string. Knowing how to insert, delete, and modify characters is crucial for string algorithms.
Prefix and suffix: The prefix and suffix of a string refer to the characters that come before or after a given substring. These components can be used to optimize string searching and matching algorithms.
Pattern matching algorithms: Common pattern matching algorithms include Naive algorithm, Rabin-Karp algorithm, Knuth-Morris-Pratt algorithm, and Boyer-Moore algorithm.
Regular expressions: Regular expressions are a way of defining patterns within strings. They are commonly used in text processing and searching applications.
Substring search algorithms: Substring search algorithms are used to locate the occurrence of a substring within a given string. Common examples include brute-force searching, Boyer-Moore, Knuth-Morris-Pratt algorithms.
String compression: String compression is a technique for reducing the amount of space required to store long strings. Common compression algorithms are Run length encoding, Huffman coding, and Lempel Ziv Welch algorithm.
String manipulation: String manipulation techniques include reordering characters in a string, concatenating two or more strings, and parsing string data.
Trie: Trie is a tree-based data structure used to store and search strings. Tries can be used for prefix matching, auto-completion, and other string-related applications.
Data Storage and Retrieval: An in-depth understanding of the storage and retrieval mechanisms of strings is essential to optimize string algorithms.
Text indexing algorithms: Indexing algorithms like suffix tree and suffix array can be used to speed up substring searching and pattern matching by maintaining index for all substrings.
String editing algorithms: Many times one needs to edit one string into another string by applying certain operations like adding, deleting, rearranging, etc. to minimize the edit operations required is an area of research in String Algorithm.
Bioinformatics: Use of string algorithms is required in the analysis and comparison of genetic data.
String matching algorithms: These algorithms are used for searching and finding a particular pattern within a given string. Some popular string matching algorithms are the Rabin-Karp algorithm, Knuth-Morris-Pratt algorithm, and Boyer-Moore algorithm.
String compression algorithms: These algorithms are used for reducing the size of a string by replacing repetitive sequences with shorter codes. Examples of string compression algorithms include Huffman coding, Lempel-Ziv-Welch algorithm, and Run Length Encoding.
String sorting algorithms: These algorithms are used for sorting a list of strings based on a certain criteria, such as lexicographically or based on length. Popular string sorting algorithms include radix sort, quick sort, and merge sort.
String manipulation algorithms: These algorithms are used for modifying or transforming a string in specific ways, such as reversing a string, replacing characters, or converting case. Examples of string manipulation algorithms include the reverse string algorithm, the replace character algorithm, and the convert case algorithm.
Longest Common Substring (LCS) algorithms: These algorithms are used for finding the longest common substring between two or more strings. Some popular LCS algorithms include dynamic programming-based approach, suffix tree-based approach, and brute-force approach.
Regular Expression algorithms: These algorithms are used for matching a pattern described by a regular expression against a given string. Some popular regular expression algorithms include Thompson’s NFA algorithm, Glushkov’s DFA algorithm, and McNaughton-Yamada-Thompson algorithm.
Trie-based algorithms: These algorithms are used for efficiently storing and searching a set of strings. Trie-based algorithms include Trie, Trie with compressed elements, and Aho-Corasick algorithm.
Hashing algorithms: These algorithms are used for generating hash values for a given string, which can be used for comparing strings or searching for a specific string in a hash table. Examples of hashing algorithms include SHA-1, MD5, and MurmurHash.
Edit Distance algorithms: These algorithms are used for calculating the minimum number of insertions, deletions, or substitutions required to transform one string into another. Some popular Edit Distance algorithms include Levenshtein distance, Damerau-Levenshtein distance, and Hamming distance.
Pattern Recognition algorithms: These algorithms are used for recognizing patterns in a given string, such as detecting regular expressions or specific phrases. Examples of pattern recognition algorithms include the Aho-Corasick algorithm and the Z algorithm.
"A basic example of string searching is when the pattern and the searched text are arrays of elements of an alphabet (finite set) Σ. Σ may be a human language alphabet, for example, the letters A through Z, and other applications may use a binary alphabet (Σ = {0,1}) or a DNA alphabet (Σ = {A,C,G,T}) in bioinformatics."
"In practice, the method of feasible string-search algorithm may be affected by the string encoding."
"If a variable-width encoding is in use, then it may be slower to find the Nth character, perhaps requiring time proportional to N. This may significantly slow some search algorithms."
"One of many possible solutions is to search for the sequence of code units instead."
"Doing so may produce false matches unless the encoding is specifically designed to avoid it."
"They try to find a place where one or several strings (also called patterns) are found within a larger string or text."
"String-searching algorithms, sometimes called string-matching algorithms..."
"Arrays of elements of an alphabet (finite set) Σ."
"Yes, some applications use a binary alphabet (Σ = {0,1}) or a DNA alphabet (Σ = {A,C,G,T}) in bioinformatics."
"The method of feasible string-search algorithm may be affected by the string encoding."
"A variable-width encoding is in use."
"It may be slower to find the Nth character, perhaps requiring time proportional to N."
"Doing so may produce false matches unless the encoding is specifically designed to avoid it."
"Finding the Nth character."
"To improve search performance."
"Yes, this may significantly slow some search algorithms."
"Arrays of elements of an alphabet (finite set) Σ."
"Applications may use a binary alphabet (Σ = {0,1}) or a DNA alphabet (Σ = {A,C,G,T}) in bioinformatics."
"They are an important class of string algorithms in computer science."