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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/9521
Ver los metadatos del registro completo
id |
SEDICI_7b4ba4cfddcd0c355024e6122ffcbce0 |
---|---|
oai_identifier_str |
oai:sedici.unlp.edu.ar:10915/9521 |
network_acronym_str |
SEDICI |
repository_id_str |
1329 |
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 |
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/10915/9521 |
url |
http://sedici.unlp.edu.ar/handle/10915/9521 |
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-Mar07-2.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 8-13 |
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 |
_version_ |
1844615757940391937 |
score |
13.070432 |