Fully dynamic and memory-adaptative spatial approximation trees

Autores
Arroyuelo, Diego; Navarro, Gonzalo; Reyes, Nora Susana
Año de publicación
2003
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Hybrid dynamic spatial approximation trees are recently proposed data structures for searching in metric spaces, based on combining the concepts of spatial approximation and pivot based algorithms. These data structures are hybrid schemes, with the full features of dynamic spatial approximation trees and able of using the available memory to improve the query time. It has been shown that they compare favorably against alternative data structures in spaces of medium difficulty. In this paper we complete and improve hybrid dynamic spatial approximation trees, by presenting a new search alternative, an algorithm to remove objects from the tree, and an improved way of managing the available memory. The result is a fully dynamic and optimized data structure for similarity searching in metric spaces.
Eje: Teoría (TEOR)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
databases
data structures
metric spaces
Algorithms
base de datos
Metrics
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/22852
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/22852
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Fully dynamic and memory-adaptative spatial approximation treesArroyuelo, DiegoNavarro, GonzaloReyes, Nora SusanaCiencias Informáticasdatabasesdata structuresmetric spacesAlgorithmsbase de datosMetricsHybrid dynamic spatial approximation trees are recently proposed data structures for searching in metric spaces, based on combining the concepts of spatial approximation and pivot based algorithms. These data structures are hybrid schemes, with the full features of dynamic spatial approximation trees and able of using the available memory to improve the query time. It has been shown that they compare favorably against alternative data structures in spaces of medium difficulty. In this paper we complete and improve hybrid dynamic spatial approximation trees, by presenting a new search alternative, an algorithm to remove objects from the tree, and an improved way of managing the available memory. The result is a fully dynamic and optimized data structure for similarity searching in metric spaces.Eje: Teoría (TEOR)Red de Universidades con Carreras en Informática (RedUNCI)2003-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1502-1513http://sedici.unlp.edu.ar/handle/10915/22852enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:28:02Zoai:sedici.unlp.edu.ar:10915/22852Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:28:02.651SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Fully dynamic and memory-adaptative spatial approximation trees
title Fully dynamic and memory-adaptative spatial approximation trees
spellingShingle Fully dynamic and memory-adaptative spatial approximation trees
Arroyuelo, Diego
Ciencias Informáticas
databases
data structures
metric spaces
Algorithms
base de datos
Metrics
title_short Fully dynamic and memory-adaptative spatial approximation trees
title_full Fully dynamic and memory-adaptative spatial approximation trees
title_fullStr Fully dynamic and memory-adaptative spatial approximation trees
title_full_unstemmed Fully dynamic and memory-adaptative spatial approximation trees
title_sort Fully dynamic and memory-adaptative spatial approximation trees
dc.creator.none.fl_str_mv Arroyuelo, Diego
Navarro, Gonzalo
Reyes, Nora Susana
author Arroyuelo, Diego
author_facet Arroyuelo, Diego
Navarro, Gonzalo
Reyes, Nora Susana
author_role author
author2 Navarro, Gonzalo
Reyes, Nora Susana
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
databases
data structures
metric spaces
Algorithms
base de datos
Metrics
topic Ciencias Informáticas
databases
data structures
metric spaces
Algorithms
base de datos
Metrics
dc.description.none.fl_txt_mv Hybrid dynamic spatial approximation trees are recently proposed data structures for searching in metric spaces, based on combining the concepts of spatial approximation and pivot based algorithms. These data structures are hybrid schemes, with the full features of dynamic spatial approximation trees and able of using the available memory to improve the query time. It has been shown that they compare favorably against alternative data structures in spaces of medium difficulty. In this paper we complete and improve hybrid dynamic spatial approximation trees, by presenting a new search alternative, an algorithm to remove objects from the tree, and an improved way of managing the available memory. The result is a fully dynamic and optimized data structure for similarity searching in metric spaces.
Eje: Teoría (TEOR)
Red de Universidades con Carreras en Informática (RedUNCI)
description Hybrid dynamic spatial approximation trees are recently proposed data structures for searching in metric spaces, based on combining the concepts of spatial approximation and pivot based algorithms. These data structures are hybrid schemes, with the full features of dynamic spatial approximation trees and able of using the available memory to improve the query time. It has been shown that they compare favorably against alternative data structures in spaces of medium difficulty. In this paper we complete and improve hybrid dynamic spatial approximation trees, by presenting a new search alternative, an algorithm to remove objects from the tree, and an improved way of managing the available memory. The result is a fully dynamic and optimized data structure for similarity searching in metric spaces.
publishDate 2003
dc.date.none.fl_str_mv 2003-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/22852
url http://sedici.unlp.edu.ar/handle/10915/22852
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
1502-1513
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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score 13.13397

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