Combining methods for searches in nested metric spaces

Autores
Gercek, Hugo; Reyes, Nora Susana; Deco, Claudia; Bender, Cristina; Salvetti, Mariano
Año de publicación
2011
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Most search methods in metric spaces assume that the topology of the object collection is reasonably regular. However, there exist nested metric spaces, where objects in the collection can be grouped into clusters or subspaces, in such a way that different dimensions or variables explain the differences between objects inside each subspace. This paper proposes a two levels index to solve search problems in spaces with this topology. The idea is to have a first level with a list of clusters, which are identified and sorted using Sparse Spatial Selection (SSS) and Lists of Clusters techniques, and a second level having an index for each dense cluster, based on pivot selection, using SSS. It is also proposed for future work to adjust the second level indexes through dynamic pivots selection to adapt the pivots according to the searches performed in the database.
Presentado en el VIII Workshop Bases de Datos y Minería de Datos (WBDDM)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
metric spaces; pivots selection; similarity search
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:10915/18751

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spelling Combining methods for searches in nested metric spacesGercek, HugoReyes, Nora SusanaDeco, ClaudiaBender, CristinaSalvetti, MarianoCiencias Informáticasmetric spaces; pivots selection; similarity searchMost search methods in metric spaces assume that the topology of the object collection is reasonably regular. However, there exist nested metric spaces, where objects in the collection can be grouped into clusters or subspaces, in such a way that different dimensions or variables explain the differences between objects inside each subspace. This paper proposes a two levels index to solve search problems in spaces with this topology. The idea is to have a first level with a list of clusters, which are identified and sorted using Sparse Spatial Selection (SSS) and Lists of Clusters techniques, and a second level having an index for each dense cluster, based on pivot selection, using SSS. It is also proposed for future work to adjust the second level indexes through dynamic pivots selection to adapt the pivots according to the searches performed in the database.Presentado en el VIII Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI)2011-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf949-958http://sedici.unlp.edu.ar/handle/10915/18751enginfo: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:UNLP2025-09-29T10:53:37Zoai:sedici.unlp.edu.ar:10915/18751Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:53:37.432SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Combining methods for searches in nested metric spaces
title Combining methods for searches in nested metric spaces
spellingShingle Combining methods for searches in nested metric spaces
Gercek, Hugo
Ciencias Informáticas
metric spaces; pivots selection; similarity search
title_short Combining methods for searches in nested metric spaces
title_full Combining methods for searches in nested metric spaces
title_fullStr Combining methods for searches in nested metric spaces
title_full_unstemmed Combining methods for searches in nested metric spaces
title_sort Combining methods for searches in nested metric spaces
dc.creator.none.fl_str_mv Gercek, Hugo
Reyes, Nora Susana
Deco, Claudia
Bender, Cristina
Salvetti, Mariano
author Gercek, Hugo
author_facet Gercek, Hugo
Reyes, Nora Susana
Deco, Claudia
Bender, Cristina
Salvetti, Mariano
author_role author
author2 Reyes, Nora Susana
Deco, Claudia
Bender, Cristina
Salvetti, Mariano
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
metric spaces; pivots selection; similarity search
topic Ciencias Informáticas
metric spaces; pivots selection; similarity search
dc.description.none.fl_txt_mv Most search methods in metric spaces assume that the topology of the object collection is reasonably regular. However, there exist nested metric spaces, where objects in the collection can be grouped into clusters or subspaces, in such a way that different dimensions or variables explain the differences between objects inside each subspace. This paper proposes a two levels index to solve search problems in spaces with this topology. The idea is to have a first level with a list of clusters, which are identified and sorted using Sparse Spatial Selection (SSS) and Lists of Clusters techniques, and a second level having an index for each dense cluster, based on pivot selection, using SSS. It is also proposed for future work to adjust the second level indexes through dynamic pivots selection to adapt the pivots according to the searches performed in the database.
Presentado en el VIII Workshop Bases de Datos y Minería de Datos (WBDDM)
Red de Universidades con Carreras en Informática (RedUNCI)
description Most search methods in metric spaces assume that the topology of the object collection is reasonably regular. However, there exist nested metric spaces, where objects in the collection can be grouped into clusters or subspaces, in such a way that different dimensions or variables explain the differences between objects inside each subspace. This paper proposes a two levels index to solve search problems in spaces with this topology. The idea is to have a first level with a list of clusters, which are identified and sorted using Sparse Spatial Selection (SSS) and Lists of Clusters techniques, and a second level having an index for each dense cluster, based on pivot selection, using SSS. It is also proposed for future work to adjust the second level indexes through dynamic pivots selection to adapt the pivots according to the searches performed in the database.
publishDate 2011
dc.date.none.fl_str_mv 2011-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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http://purl.org/coar/resource_type/c_5794
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dc.language.none.fl_str_mv eng
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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)
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949-958
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