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dc.contributor.authorSmyth, Bill
dc.contributor.authorWang, S.
dc.date.accessioned2017-01-30T15:24:46Z
dc.date.available2017-01-30T15:24:46Z
dc.date.created2010-03-31T20:02:37Z
dc.date.issued2009
dc.identifier.citationSmyth, W.F. and Wang, Shu. 2009. An adaptive hybrid pattern-matching algorithm on indeterminate strings. International Journal of Foundations of Computer Science. 20 (6): pp. 985-1004.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/46034
dc.identifier.doi10.1142/S0129054109007005
dc.description.abstract

We describe a hybrid pattern-matching algorithm that works on both regular and indeterminate strings. This algorithm is inspired by the recently proposed hybrid algorithm FJS and its indeterminate successor. However, as discussed in this paper, because of the special properties of indeterminate strings, it is not straightforward to directly migrate FJS to an indeterminate version. Our new algorithm combines two fast pattern-matching algorithms, ShiftAnd and BMS (the Sunday variant of the Boyer-Moore algorithm), and is highly adaptive to the nature of the text being processed. It avoids using the border array, therefore avoids some of the cases that are awkward for indeterminate strings. Although not always the fastest in individual test cases, our new algorithm is superior in overall performance to its two component algorithms — perhaps a general advantage of hybrid algorithms.

dc.publisherWorld Scientific
dc.subjectadaptive
dc.subjectIndeterminate Strings
dc.subjecthybrid
dc.titleAn adaptive hybrid pattern-matching algorithm on indeterminate strings
dc.typeJournal Article
dcterms.source.volume20
dcterms.source.number6
dcterms.source.startPage985
dcterms.source.endPage1004
dcterms.source.issn01290541
dcterms.source.titleInternational Journal of Foundations of Computer Science
curtin.note

Electronic version of an article published as International Journal of Foundations of Computer Science, 20, 6, 2009, 985-1004. http://dx.doi.org/10.1142/S0129054109007005 © copyright World Scientific Publishing Company

curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusOpen access
curtin.facultyCurtin Business School
curtin.facultyThe Digital Ecosystems and Business Intelligence Institute (DEBII)


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