Run-Length Compressed Indexes Are Superior for Highly Repetitive Sequence Collections

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dc.contributor.author Siren, Jouni fi
dc.contributor.author Välimäki, Niko fi
dc.contributor.author Mäkinen, Veli fi
dc.contributor.author Navarro, Gonzalo fi
dc.date.accessioned 2008-12-04T08:03:31Z fi
dc.date.accessioned 2009-05-28T10:35:19Z
dc.date.available 2008-12-04T08:03:31Z fi
dc.date.available 2009-05-28T10:35:19Z
dc.date.issued 2008 fi
dc.identifier.citation In Proc. 15th Symposium on String Processing and Information Retrieval, LNCS 5280, pp. 164–175, 2008. fi
dc.identifier.uri http://hdl.handle.net/10138/1200 fi
dc.description.abstract A repetitive sequence collection is one where portions of a base sequence of length n are repeated many times with small variations, forming a collection of total length N. Examples of such collections are version control data and genome sequences of individuals, where the differences can be expressed by lists of basic edit operations. This paper is devoted to studying ways to store massive sets of highly repetitive sequence collections in space-efficient manner so that retrieval of the content as well as queries on the content of the sequences can be provided time-efficiently. We show that the state-of-the-art entropy-bound full-text self-indexes do not yet provide satisfactory space bounds for this specific task. We engineer some new structures that use run-length encoding and give empirical evidence that these structures are superior to the current structures. fi
dc.publisher Springer-Verlag fi
dc.title Run-Length Compressed Indexes Are Superior for Highly Repetitive Sequence Collections fi
dc.type Preprint fi
dc.identifier.laitoskoodi 523 fi
dc.creator.corporateName Tietojenkäsittelytieteen laitos fi
dc.creator.corporateName Department of Computer Science en
dc.creator.corporateName Datavetenskap, Institutionen för sv

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