Spatial selection of sparse pivots for similarity search in metric spaces

Autores
Rodríguez Brisaboa, Nieves; Fariña, Antonio; Pedreira, Óscar; Reyes, Nora Susana
Año de publicación
2007
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Similarity search is a fundamental operation for applications that deal with unstructured data sources. In this paper we propose a new pivot-based method for similarity search, called Sparse Spatial Selection (SSS). The main characteristic of this method is that it guarantees a good pivot selection more efficiently than other methods previously proposed. In addition, SSS adapts itself to the dimensionality of the metric space we are working with, without being necessary to specify in advance the number of pivots to use. Furthermore, SSS is dynamic, that is, it is capable to support object insertions in the database efficiently, it can work with both continuous and discrete distance functions, and it is suitable for secondary memory storage. In this work we provide experimental results that confirm the advantages of the method with several vector and metric spaces. We also show that the efficiency of our proposal is similar to that of other existing ones over vector spaces, although it is better over general metric spaces.
Facultad de Informática
Materia
Ciencias Informáticas
base de datos
Database Applications
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:10915/9521

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network_name_str SEDICI (UNLP)
spelling Spatial selection of sparse pivots for similarity search in metric spacesRodríguez Brisaboa, NievesFariña, AntonioPedreira, ÓscarReyes, Nora SusanaCiencias Informáticasbase de datosDatabase ApplicationsSimilarity search is a fundamental operation for applications that deal with unstructured data sources. In this paper we propose a new pivot-based method for similarity search, called Sparse Spatial Selection (SSS). The main characteristic of this method is that it guarantees a good pivot selection more efficiently than other methods previously proposed. In addition, SSS adapts itself to the dimensionality of the metric space we are working with, without being necessary to specify in advance the number of pivots to use. Furthermore, SSS is dynamic, that is, it is capable to support object insertions in the database efficiently, it can work with both continuous and discrete distance functions, and it is suitable for secondary memory storage. In this work we provide experimental results that confirm the advantages of the method with several vector and metric spaces. We also show that the efficiency of our proposal is similar to that of other existing ones over vector spaces, although it is better over general metric spaces.Facultad de Informática2007-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf8-13http://sedici.unlp.edu.ar/handle/10915/9521enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-2.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:UNLP2025-09-29T10:50:44Zoai:sedici.unlp.edu.ar:10915/9521Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:44.276SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Spatial selection of sparse pivots for similarity search in metric spaces
title Spatial selection of sparse pivots for similarity search in metric spaces
spellingShingle Spatial selection of sparse pivots for similarity search in metric spaces
Rodríguez Brisaboa, Nieves
Ciencias Informáticas
base de datos
Database Applications
title_short Spatial selection of sparse pivots for similarity search in metric spaces
title_full Spatial selection of sparse pivots for similarity search in metric spaces
title_fullStr Spatial selection of sparse pivots for similarity search in metric spaces
title_full_unstemmed Spatial selection of sparse pivots for similarity search in metric spaces
title_sort Spatial selection of sparse pivots for similarity search in metric spaces
dc.creator.none.fl_str_mv Rodríguez Brisaboa, Nieves
Fariña, Antonio
Pedreira, Óscar
Reyes, Nora Susana
author Rodríguez Brisaboa, Nieves
author_facet Rodríguez Brisaboa, Nieves
Fariña, Antonio
Pedreira, Óscar
Reyes, Nora Susana
author_role author
author2 Fariña, Antonio
Pedreira, Óscar
Reyes, Nora Susana
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
base de datos
Database Applications
topic Ciencias Informáticas
base de datos
Database Applications
dc.description.none.fl_txt_mv Similarity search is a fundamental operation for applications that deal with unstructured data sources. In this paper we propose a new pivot-based method for similarity search, called Sparse Spatial Selection (SSS). The main characteristic of this method is that it guarantees a good pivot selection more efficiently than other methods previously proposed. In addition, SSS adapts itself to the dimensionality of the metric space we are working with, without being necessary to specify in advance the number of pivots to use. Furthermore, SSS is dynamic, that is, it is capable to support object insertions in the database efficiently, it can work with both continuous and discrete distance functions, and it is suitable for secondary memory storage. In this work we provide experimental results that confirm the advantages of the method with several vector and metric spaces. We also show that the efficiency of our proposal is similar to that of other existing ones over vector spaces, although it is better over general metric spaces.
Facultad de Informática
description Similarity search is a fundamental operation for applications that deal with unstructured data sources. In this paper we propose a new pivot-based method for similarity search, called Sparse Spatial Selection (SSS). The main characteristic of this method is that it guarantees a good pivot selection more efficiently than other methods previously proposed. In addition, SSS adapts itself to the dimensionality of the metric space we are working with, without being necessary to specify in advance the number of pivots to use. Furthermore, SSS is dynamic, that is, it is capable to support object insertions in the database efficiently, it can work with both continuous and discrete distance functions, and it is suitable for secondary memory storage. In this work we provide experimental results that confirm the advantages of the method with several vector and metric spaces. We also show that the efficiency of our proposal is similar to that of other existing ones over vector spaces, although it is better over general metric spaces.
publishDate 2007
dc.date.none.fl_str_mv 2007-04
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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
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