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In computer science, an algorithm for matching wildcards (also known as globbing) is useful in comparing text strings that may contain wildcard syntax. [1] Common uses of these algorithms include command-line interfaces, e.g. the Bourne shell [2] or Microsoft Windows command-line [3] or text editor or file manager, as well as the interfaces for some search engines [4] and databases. [5]
strings Text to be searched for. [drive:][path]filename Specifies a file or files to search. Flags: /B Matches pattern if at the beginning of a line. /E Matches pattern if at the end of a line. /L Uses search strings literally. /R Uses search strings as regular expressions. /S Searches for matching files in the current directory and all ...
The usual context of wildcard characters is in globbing similar names in a list of files, whereas regexes are usually employed in applications that pattern-match text strings in general. For example, the regex ^ [ \t] +| [ \t] +$ matches excess whitespace at the beginning or end of a line.
Python has a glob module in the standard library which performs wildcard pattern matching on filenames, [28] and an fnmatch module with functions for matching strings or filtering lists based on these same wildcard patterns. [17] Guido van Rossum, author of the Python programming language, wrote and contributed a glob routine to BSD Unix in ...
agrep (approximate grep) is an open-source approximate string matching program, developed by Udi Manber and Sun Wu between 1988 and 1991, [26] for use with the Unix operating system. It was later ported to OS/2, DOS, and Windows. agrep (approximate grep) matches even when the text only approximately fits the search pattern. [27]
In computer science, the Krauss wildcard-matching algorithm is a pattern matching algorithm. Based on the wildcard syntax in common use, e.g. in the Microsoft Windows command-line interface, the algorithm provides a non-recursive mechanism for matching patterns in software applications, based on syntax simpler than that typically offered by regular expressions.
In this example, we will consider a dictionary consisting of the following words: {a, ab, bab, bc, bca, c, caa}. The graph below is the Aho–Corasick data structure constructed from the specified dictionary, with each row in the table representing a node in the trie, with the column path indicating the (unique) sequence of characters from the root to the node.
P denotes the string to be searched for, called the pattern. Its length is m. S[i] denotes the character at index i of string S, counting from 1. S[i..j] denotes the substring of string S starting at index i and ending at j, inclusive. A prefix of S is a substring S[1..i] for some i in range [1, l], where l is the length of S.