Spatial selection of sparse pivots for similarity search in metric spaces

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...

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Autores principales: Rodríguez Brisaboa, Nieves, Fariña, Antonio, Pedreira, Óscar, Reyes, Nora Susana
Formato: Articulo
Lenguaje:Inglés
Publicado: 2007
Materias:
Ciencias Informáticas
base de datos
Database Applications
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/9521
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-2.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-9521
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
base de datos
Database Applications
spellingShingle Ciencias Informáticas
base de datos
Database Applications
Rodríguez Brisaboa, Nieves
Fariña, Antonio
Pedreira, Óscar
Reyes, Nora Susana
Spatial selection of sparse pivots for similarity search in metric spaces
topic_facet Ciencias Informáticas
base de datos
Database Applications
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.
format Articulo
Articulo
author Rodríguez Brisaboa, Nieves
Fariña, Antonio
Pedreira, Óscar
Reyes, Nora Susana
author_facet Rodríguez Brisaboa, Nieves
Fariña, Antonio
Pedreira, Óscar
Reyes, Nora Susana
author_sort Rodríguez Brisaboa, Nieves
title Spatial selection of sparse pivots for similarity search in metric spaces
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
publishDate 2007
url http://sedici.unlp.edu.ar/handle/10915/9521
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-2.pdf
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AT farinaantonio spatialselectionofsparsepivotsforsimilaritysearchinmetricspaces
AT pedreiraoscar spatialselectionofsparsepivotsforsimilaritysearchinmetricspaces
AT reyesnorasusana spatialselectionofsparsepivotsforsimilaritysearchinmetricspaces
bdutipo_str Repositorios
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