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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/18751
Ver los metadatos del registro completo
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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 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/10915/18751 |
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http://sedici.unlp.edu.ar/handle/10915/18751 |
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 |
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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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application/pdf 949-958 |
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