Matching a set of strings with variable length don't cares
Theoretical Computer Science
Efficient string matching: an aid to bibliographic search
Communications of the ACM
NR-grep: a fast and flexible pattern-matching tool
Software—Practice & Experience
Efficient pattern-matching with don't cares
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Flexible pattern matching in strings: practical on-line search algorithms for texts and biological sequences
Dictionary matching and indexing with errors and don't cares
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Simple deterministic wildcard matching
Information Processing Letters
BIBM '07 Proceedings of the 2007 IEEE International Conference on Bioinformatics and Biomedicine
A faster algorithm for matching a set of patterns with variable length don't cares
Information Processing Letters
Regular expression matching with multi-strings and intervals
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
String matching with variable length gaps
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Finding patterns with variable length gaps or don’t cares
COCOON'06 Proceedings of the 12th annual international conference on Computing and Combinatorics
String matching with variable length gaps
Theoretical Computer Science
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The string-matching problem with wildcards is considered in the context of online matching of multiple patterns. Our patterns are strings of characters in the input alphabet and of variable-length gaps, where the width of a gap may vary between two integer bounds or from an integer lower bound to infinity. Our algorithm is based on locating "keywords" of the patterns in the input text, that is, maximal substrings of the patterns that contain only input characters. Matches of prefixes of patterns are collected from the keyword matches, and when a prefix constituting a complete pattern is found, a match is reported. In collecting these partial matches we avoid locating those keyword occurrences that cannot participate in any prefix of a pattern found thus far. Our experiments show that our algorithm scales up well, when the number of patterns increases.