Optimizing the spatial approximation tree from the root

Autores
Gómez, Alejandro J.; Ludueña, Verónica; Reyes, Nora Susana
Año de publicación
2008
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Many computational applications need to look for information in a database. Nowadays, the predominance of nonconventional databases makes the similarity search (i.e., searching elements of the database that are "similar" to a given query) becomes a preponderant concept. The Spatial Approximation Tree has been shown that it compares favorably against alternative data structures for similarity searching in metric spaces of medium to high dimensionality ("difficult" spaces) or queries with low selectivity. However, for the construction process the tree root has been randomly selected and the tree ,in its shape and performance, is completely determined by this selection. Therefore, we are interested in improve mainly the searches in this data structure trying to select the tree root so to reflect some of the own characteristics of the metric space to be indexed. We regard that selecting the root in this way it allows a better adaption of the data structure to the intrinsic dimensionality of the metric space considered, so also it achieves more efficient similarity searches.
Facultad de Informática
Materia
Ciencias Informáticas
Base de Datos
Metrics
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9632
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/9632
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Optimizing the spatial approximation tree from the rootGómez, Alejandro J.Ludueña, VerónicaReyes, Nora SusanaCiencias InformáticasBase de DatosMetricsMany computational applications need to look for information in a database. Nowadays, the predominance of nonconventional databases makes the similarity search (i.e., searching elements of the database that are "similar" to a given query) becomes a preponderant concept. The Spatial Approximation Tree has been shown that it compares favorably against alternative data structures for similarity searching in metric spaces of medium to high dimensionality ("difficult" spaces) or queries with low selectivity. However, for the construction process the tree root has been randomly selected and the tree ,in its shape and performance, is completely determined by this selection. Therefore, we are interested in improve mainly the searches in this data structure trying to select the tree root so to reflect some of the own characteristics of the metric space to be indexed. We regard that selecting the root in this way it allows a better adaption of the data structure to the intrinsic dimensionality of the metric space considered, so also it achieves more efficient similarity searches.Facultad de Informática2008-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf111-117http://sedici.unlp.edu.ar/handle/10915/9632enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Jul08-9.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:23:35Zoai:sedici.unlp.edu.ar:10915/9632Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:23:35.485SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Optimizing the spatial approximation tree from the root
title Optimizing the spatial approximation tree from the root
spellingShingle Optimizing the spatial approximation tree from the root
Gómez, Alejandro J.
Ciencias Informáticas
Base de Datos
Metrics
title_short Optimizing the spatial approximation tree from the root
title_full Optimizing the spatial approximation tree from the root
title_fullStr Optimizing the spatial approximation tree from the root
title_full_unstemmed Optimizing the spatial approximation tree from the root
title_sort Optimizing the spatial approximation tree from the root
dc.creator.none.fl_str_mv Gómez, Alejandro J.
Ludueña, Verónica
Reyes, Nora Susana
author Gómez, Alejandro J.
author_facet Gómez, Alejandro J.
Ludueña, Verónica
Reyes, Nora Susana
author_role author
author2 Ludueña, Verónica
Reyes, Nora Susana
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Base de Datos
Metrics
topic Ciencias Informáticas
Base de Datos
Metrics
dc.description.none.fl_txt_mv Many computational applications need to look for information in a database. Nowadays, the predominance of nonconventional databases makes the similarity search (i.e., searching elements of the database that are "similar" to a given query) becomes a preponderant concept. The Spatial Approximation Tree has been shown that it compares favorably against alternative data structures for similarity searching in metric spaces of medium to high dimensionality ("difficult" spaces) or queries with low selectivity. However, for the construction process the tree root has been randomly selected and the tree ,in its shape and performance, is completely determined by this selection. Therefore, we are interested in improve mainly the searches in this data structure trying to select the tree root so to reflect some of the own characteristics of the metric space to be indexed. We regard that selecting the root in this way it allows a better adaption of the data structure to the intrinsic dimensionality of the metric space considered, so also it achieves more efficient similarity searches.
Facultad de Informática
description Many computational applications need to look for information in a database. Nowadays, the predominance of nonconventional databases makes the similarity search (i.e., searching elements of the database that are "similar" to a given query) becomes a preponderant concept. The Spatial Approximation Tree has been shown that it compares favorably against alternative data structures for similarity searching in metric spaces of medium to high dimensionality ("difficult" spaces) or queries with low selectivity. However, for the construction process the tree root has been randomly selected and the tree ,in its shape and performance, is completely determined by this selection. Therefore, we are interested in improve mainly the searches in this data structure trying to select the tree root so to reflect some of the own characteristics of the metric space to be indexed. We regard that selecting the root in this way it allows a better adaption of the data structure to the intrinsic dimensionality of the metric space considered, so also it achieves more efficient similarity searches.
publishDate 2008
dc.date.none.fl_str_mv 2008-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/9632
url http://sedici.unlp.edu.ar/handle/10915/9632
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Jul08-9.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
111-117
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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