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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.
This would result in a final four-column table listing the names of the residents of the cities stored in the graph. Pattern-based queries are able to express joins, by combining multiple patterns which use the same binding variable to express a natural join using the MATCH clause:
The AIML pattern syntax is a very simple pattern language, substantially less complex than regular expressions and as such less than level 3 in the Chomsky hierarchy. To compensate for the simple pattern matching capabilities, AIML interpreters can provide preprocessing functions to expand abbreviations, remove misspellings, etc.
The Rete algorithm is widely used to implement matching functionality within pattern-matching engines that exploit a match-resolve-act cycle to support forward chaining and inferencing. It provides a means for many–many matching, an important feature when many or all possible solutions in a search network must be found.
In computer science, pattern matching is the act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition , the match usually has to be exact: "either it will or will not be a match."
wildmat is a pattern matching library developed by Rich Salz. Based on the wildcard syntax already used in the Bourne shell, wildmat provides a uniform mechanism for matching patterns across applications with simpler syntax than that typically offered by regular expressions. Patterns are implicitly anchored at the beginning and end of each ...
In the case of attributed graphs, even if the numbers of vertices and edges are the same, the matching still may be only inexact. [1] Two categories of search methods are the ones based on identification of possible and impossible pairings of vertices between the two graphs and methods that formulate graph matching as an optimization problem. [3]
Pattern matching programming languages (2 C, 30 P) R. Regular expressions (1 C, 12 P) S. String matching algorithms (1 C, 16 P) Pages in category "Pattern matching"