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008 220601s2012 sz | s |||| 0|eng d
020 _a9783031018855
024 7 _a10.1007/978-3-031-01885-5
_2doi
040 _aES-MaUEC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQA76.9.T48
_b2012 EB
100 1 _aBarsky, Marina
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9687072
245 1 0 _aFull-Text (Substring) Indexes in External Memory
_cby Marina Barsky, Alex Thomo, Ulrike Stege
250 _a1st edition 2012
264 1 _aCham
_bSpringer International Publishing
_c2012
300 _a1 recurso en línea (XV, 76 páginas)
336 _atexto
_btxt
_2rdacontent
337 _aelectrónico
_bc
_2rdamedia
338 _arecurso electrónico
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
490 0 _aSynthesis Lectures on Data Management
_x2153-5426
505 0 _aStructures for Indexing Substrings -- External Construction of Suffix Trees -- Scaling Up: When the Input Exceeds the Main Memory -- Queries for Disk-based Indexes -- Conclusions and Open Problems.
520 _aNowadays, textual databases are among the most rapidly growing collections of data. Some of these collections contain a new type of data that differs from classical numerical or textual data. These are long sequences of symbols, not divided into well-separated small tokens (words). The most prominent among such collections are databases of biological sequences, which are experiencing today an unprecedented growth rate. Starting in 2008, the "1000 Genomes Project" has been launched with the ultimate goal of collecting sequences of additional 1,500 Human genomes, 500 each of European, African, and East Asian origin. This will produce an extensive catalog of Human genetic variations. The size of just the raw sequences in this catalog would be about 5 terabytes. Querying strings without well-separated tokens poses a different set of challenges, typically addressed by building full-text indexes, which provide effective structures to index all the substrings of the given strings. Since full-text indexes occupy more space than the raw data, it is often necessary to use disk space for their construction. However, until recently, the construction of full-text indexes in secondary storage was considered impractical due to excessive I/O costs. Despite this, algorithms developed in the last decade demonstrated that efficient external construction of full-text indexes is indeed possible. This book is about large-scale construction and usage of full-text indexes. We focus mainly on suffix trees, and show efficient algorithms that can convert suffix trees to other kinds of full-text indexes and vice versa. There are four parts in this book. They are a mix of string searching theory with the reality of external memory constraints. The first part introduces general concepts of full-text indexes and shows the relationships between them. The second part presents the first series of external-memory construction algorithms that can handle the construction of full-text indexes for moderately large strings in the order of few gigabytes. The third part presents algorithms that scale for very large strings. The final part examines queries that can be facilitated by disk-resident full-text indexes. Table of Contents: Structures for Indexing Substrings / External Construction of Suffix Trees / Scaling Up: When the Input Exceeds the Main Memory / Queries for Disk-based Indexes / Conclusions and Open Problems.
988 _aSynthesis Collection of Technology_2012
650 7 _2embne
_9141188
_aProceso de textos
650 7 _2embne
_9670359
_aDispositivos de almacenamiento de datos
650 7 _2embne
_9151819
_aAlgoritmos computacionales
700 1 _aThomo, Alex-Imir,
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9687073
_d1971-
700 1 _aStege, Ulrike
_eautor
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9687074
776 0 8 _iPrinted edition:
_z9783031007576
776 0 8 _iPrinted edition:
_z9783031030130
856 4 0 _uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01885-5
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b02/2023
_dz
_esc
_zSI