Multi-pattern matching algorithm with wildcards based on Euclidean distance and hash function
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 9786, Page: 334-344
2016
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Conference Paper Description
In this paper, we present an algorithm to solve the problem of multi-pattern matching with a fixed number of wildcards. The method requires each pattern in a pattern set P to be partitioned into l length blocks, and then, the Euclidean distance is calculated between each first block b, of patterns and every possible alignment of the text t. If the Euclidean distance at position i is 0, then the block b, of pattern p matches the text t at position i. The Euclidean distance values are used as hash values to check the matches of the remaining blocks of the partially matched pattern. The complexity of our algorithm is O(k n log l + o + d) time. Where n is the length of the text, l is the length of the blocks, k is the number of patterns, o is the number of blocks that match using the hash values and d is the number of wildcard symbols in the blocks that match using the hash values. The major advantages of our algorithm are that (a) it can find the matches of long patterns efficiently, (b) if the alphabet size σ is large, the algorithm is still efficient and (c) it supports non-interrupted pattern updates.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84978849182&origin=inward; http://dx.doi.org/10.1007/978-3-319-42085-1_26; http://link.springer.com/10.1007/978-3-319-42085-1_26; http://link.springer.com/content/pdf/10.1007/978-3-319-42085-1_26; https://dx.doi.org/10.1007/978-3-319-42085-1_26; https://link.springer.com/chapter/10.1007/978-3-319-42085-1_26
Springer Science and Business Media LLC
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