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String Matching Algorithms
A string-searching algorithm, sometimes called string-matching algorithm, is an algorithm that searches a body of string (computer science), text for portions that match by pattern. A basic example of string searching is when the pattern and the searched text are Array data structure, arrays of elements of an Alphabet (computer science), 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 = ) or a ''DNA alphabet'' (Σ = ) in bioinformatics. In practice, the method of feasible string-search algorithm may be affected by the string encoding. In particular, if a variable-width encoding is in use, then it may be slower to find the ''N''th 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, but doing so may produce false matches unless t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithm
In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algorithms are used as specifications for performing calculations and data processing. More advanced algorithms can use Conditional (computer programming), conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning). In contrast, a Heuristic (computer science), heuristic is an approach to solving problems without well-defined correct or optimal results.David A. Grossman, Ophir Frieder, ''Information Retrieval: Algorithms and Heuristics'', 2nd edition, 2004, For example, although social media recommender systems are commonly called "algorithms", they actually rely on heuristics as there is no truly "correct" recommendation. As an e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Knuth–Morris–Pratt Algorithm
In computer science, the Knuth–Morris–Pratt algorithm (or KMP algorithm) is a string-searching algorithm that searches for occurrences of a "word" W within a main "text string" S by employing the observation that when a mismatch occurs, the word itself embodies sufficient information to determine where the next match could begin, thus bypassing re-examination of previously matched characters. The algorithm was conceived by James H. Morris and independently discovered by Donald Knuth "a few weeks later" from automata theory. Morris and Vaughan Pratt published a technical report in 1970. The three also published the algorithm jointly in 1977. Independently, in 1969, Matiyasevich discovered a similar algorithm, coded by a two-dimensional Turing machine, while studying a string-pattern-matching recognition problem over a binary alphabet. This was the first linear-time algorithm for string matching. Background A string-matching algorithm wants to find the starting index m in st ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
C String Handling
The C programming language has a set of functions implementing operations on strings (character strings and byte strings) in its standard library. Various operations, such as copying, concatenation, tokenization and searching are supported. For character strings, the standard library uses the convention that strings are null-terminated: a string of characters is represented as an array of elements, the last of which is a " character" with numeric value 0. The only support for strings in the programming language proper is that the compiler translates quoted string constants into null-terminated strings. Definitions A string is defined as a contiguous sequence of code units terminated by the first zero code unit (often called the ''NUL'' code unit). This means a string cannot contain the zero code unit, as the first one seen marks the end of the string. The ''length'' of a string is the number of code units before the zero code unit. The memory occupied by a string is alwa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Suffix Automaton
In computer science, a suffix automaton is an efficient data structure for representing the substring index of a given string which allows the storage, processing, and retrieval of compressed information about all its substrings. The suffix automaton of a string S is the smallest directed acyclic graph with a dedicated initial vertex and a set of "final" vertices, such that paths from the initial vertex to final vertices represent the suffixes of the string. In terms of automata theory, a suffix automaton is the Minimal automaton, minimal partial deterministic finite automaton that recognizes the set of Substring, suffixes of a given String (computer science), string S=s_1 s_2 \dots s_n. The state graph of a suffix automaton is called a directed acyclic word graph (DAWG), a term that is also sometimes used for any deterministic acyclic finite state automaton. Suffix automata were introduced in 1983 by a group of scientists from the University of Denver and the University of Colo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Two-way String-matching Algorithm
In computer science, the two-way string-matching algorithm is a string-searching algorithm, discovered by Maxime Crochemore and Dominique Perrin in 1991. It takes a pattern of size ''m'', called a “needle”, preprocesses it in linear time O(''m''), producing information that can then be used to search for the needle in any “haystack” string, taking only linear time O(''n'') with ''n'' being the haystack's length. The two-way algorithm can be viewed as a combination of the forward-going Knuth–Morris–Pratt algorithm (KMP) and the backward-running Boyer–Moore string-search algorithm (BM). Like those two, the 2-way algorithm preprocesses the pattern to find partially repeating periods and computes “shifts” based on them, indicating what offset to “jump” to in the haystack when a given character is encountered. Unlike BM and KMP, it uses only O(log ''m'') additional space to store information about those partial repeats: the search pattern is split into two par ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Rabin–Karp Algorithm
In computer science, the Rabin–Karp algorithm or Karp–Rabin algorithm is a string-searching algorithm created by that uses hashing to find an exact match of a pattern string in a text. It uses a rolling hash to quickly filter out positions of the text that cannot match the pattern, and then checks for a match at the remaining positions. Generalizations of the same idea can be used to find more than one match of a single pattern, or to find matches for more than one pattern. To find a single match of a single pattern, the expected time of the algorithm is linear in the combined length of the pattern and text, although its worst-case time complexity is the product of the two lengths. To find multiple matches, the expected time is linear in the input lengths, plus the combined length of all the matches, which could be greater than linear. In contrast, the Aho–Corasick algorithm can find all matches of multiple patterns in worst-case time and space linear in the input leng ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Approximate String Matching
In computer science, approximate string matching (often colloquially referred to as fuzzy string searching) is the technique of finding strings that match a pattern approximately (rather than exactly). The problem of approximate string matching is typically divided into two sub-problems: finding approximate substring matches inside a given string and finding dictionary strings that match the pattern approximately. Overview The closeness of a match is measured in terms of the number of primitive operations necessary to convert the string into an exact match. This number is called the edit distance between the string and the pattern. The usual primitive operations are: * insertion: ''cot'' → ''coat'' * deletion: ''coat'' → ''cot'' * substitution: ''coat'' → ''cost'' These three operations may be generalized as forms of substitution by adding a NULL character (here symbolized by *) wherever a character has been deleted or inserted: * insertion: ''co*t'' → ''coat'' * del ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Trigram Search
Trigram search is a method of searching for text when the exact syntax or spelling of the target object is not precisely known or when queries may be regular expressions. It finds objects which match the maximum number of three consecutive character strings (i.e. trigrams) in the entered search terms, which are generally near matches. Two strings with many shared trigrams can be expected to be very similar. Trigrams also allow for efficiently creating search engine indexes for searches that are regular expressions or match the text inexactly. Indexes can significantly accelerate searches. A threshold for number of trigram matches can be specified as a cutoff point, after a result is unmatched. Using trigrams for accelerating searches is a technique used in some systems for code searching, in situations in which queries that are regular expressions may be useful, in search engines such as Elasticsearch, as well as in databases such as PostgreSQL. Examples Consider the string "al ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Depth-first Search
Depth-first search (DFS) is an algorithm for traversing or searching tree or graph data structures. The algorithm starts at the root node (selecting some arbitrary node as the root node in the case of a graph) and explores as far as possible along each branch before backtracking. Extra memory, usually a stack, is needed to keep track of the nodes discovered so far along a specified branch which helps in backtracking of the graph. A version of depth-first search was investigated in the 19th century by French mathematician Charles Pierre Trémaux as a strategy for solving mazes. Properties The time and space analysis of DFS differs according to its application area. In theoretical computer science, DFS is typically used to traverse an entire graph, and takes time where , V, is the number of vertices and , E, the number of edges. This is linear in the size of the graph. In these applications it also uses space O(, V, ) in the worst case to store the stack of vertices on t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Suffix Array
In computer science, a suffix array is a sorted array of all suffixes of a string. It is a data structure used in, among others, full-text indices, data-compression algorithms, and the field of bibliometrics. Suffix arrays were introduced by as a simple, space efficient alternative to suffix trees. They had independently been discovered by Gaston Gonnet in 1987 under the name ''PAT array'' . gave the first in-place \mathcal(n) time suffix array construction algorithm that is optimal both in time and space, where ''in-place'' means that the algorithm only needs \mathcal(1) additional space beyond the input string and the output suffix array. Enhanced suffix arrays (ESAs) are suffix arrays with additional tables that reproduce the full functionality of suffix trees preserving the same time and memory complexity. A sorted array of only some (rather than all) suffixes of a string is called a sparse suffix array. Definition Let S=S ..S /math> be an n-string and let S ,j/math ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Suffix Tree
In computer science, a suffix tree (also called PAT tree or, in an earlier form, position tree) is a compressed trie containing all the suffixes of the given text as their keys and positions in the text as their values. Suffix trees allow particularly fast implementations of many important string operations. The construction of such a tree for the string S takes time and space linear in the length of S. Once constructed, several operations can be performed quickly, such as locating a substring in S, locating a substring if a certain number of mistakes are allowed, and locating matches for a regular expression pattern. Suffix trees also provided one of the first linear-time solutions for the longest common substring problem. These speedups come at a cost: storing a string's suffix tree typically requires significantly more space than storing the string itself. History The concept was first introduced by . Rather than the suffix S ..n/math>, Weiner stored in his trie the ''prefix ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Substring Index
In computer science, a substring index is a data structure which gives substring search in a text or text collection in sublinear time. Once constructed from a document or set of documents, a substring index can be used to locate all occurrences of a pattern in time linear or near-linear in the pattern size, with no dependence or only logarithmic dependence on the document size. The phrase full-text index is often used for substring indexes. But this is ambiguous, as it is also used for regular word indexes such as inverted files and document retrieval. See full text search. General considerations These data structures typically treat their text and pattern as strings over a fixed alphabet, and search for locations where the pattern occurs as a substring of the text. The symbols of the alphabet may be characters (for instance in Unicode) but in practical applications for text retrieval it may be preferable to treat the ( stemmed) words of a document as the symbols of its alphabe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
