Using parallel pivot vs. clustering-based techniques for web engines
- Autores
- Gil Costa, Graciela Verónica; Printista, Alicia Marcela
- Año de publicación
- 2007
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Web Engines are a useful tool for searching information in the Web. But a great part of this information is non-textual and for that case a metric space is used. A metric space is a set where a notion of distance (called a metric) between elements of the set is defined. In this paper we present an efficient parallelization of a pivot-based method devised for this purpose which is called the Sparse Spatial Selection (SSS) strategy and we compare it with a clustering-based method, a parallel implementation of the Spatial Approximation Tree (SAT). We show that SAT compares favourably against the pivot data structures SSS. The experimental results were obtained on a highperformance cluster and using several metric spaces, that shows load balance parallel strategies for the SAT. The implementations are built upon the BSP parallel computing model, which shows efficient performance for this application domain and allows a precise evaluation of algorithms.
VIII Workshop de Procesamiento Distribuido y Paralelo
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Informática
Clustering
Search process
Parallel algorithms
metric spaces
parallel search
distance computations - 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:10915/22974
Ver los metadatos del registro completo
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Using parallel pivot vs. clustering-based techniques for web enginesGil Costa, Graciela VerónicaPrintista, Alicia MarcelaCiencias InformáticasInformáticaClusteringSearch processParallel algorithmsmetric spacesparallel searchdistance computationsWeb Engines are a useful tool for searching information in the Web. But a great part of this information is non-textual and for that case a metric space is used. A metric space is a set where a notion of distance (called a metric) between elements of the set is defined. In this paper we present an efficient parallelization of a pivot-based method devised for this purpose which is called the Sparse Spatial Selection (SSS) strategy and we compare it with a clustering-based method, a parallel implementation of the Spatial Approximation Tree (SAT). We show that SAT compares favourably against the pivot data structures SSS. The experimental results were obtained on a highperformance cluster and using several metric spaces, that shows load balance parallel strategies for the SAT. The implementations are built upon the BSP parallel computing model, which shows efficient performance for this application domain and allows a precise evaluation of algorithms.VIII Workshop de Procesamiento Distribuido y ParaleloRed de Universidades con Carreras en Informática (RedUNCI)2007-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1310-1322http://sedici.unlp.edu.ar/handle/10915/22974enginfo: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:UNLP2025-09-29T10:55:16Zoai:sedici.unlp.edu.ar:10915/22974Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:16.96SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Using parallel pivot vs. clustering-based techniques for web engines |
title |
Using parallel pivot vs. clustering-based techniques for web engines |
spellingShingle |
Using parallel pivot vs. clustering-based techniques for web engines Gil Costa, Graciela Verónica Ciencias Informáticas Informática Clustering Search process Parallel algorithms metric spaces parallel search distance computations |
title_short |
Using parallel pivot vs. clustering-based techniques for web engines |
title_full |
Using parallel pivot vs. clustering-based techniques for web engines |
title_fullStr |
Using parallel pivot vs. clustering-based techniques for web engines |
title_full_unstemmed |
Using parallel pivot vs. clustering-based techniques for web engines |
title_sort |
Using parallel pivot vs. clustering-based techniques for web engines |
dc.creator.none.fl_str_mv |
Gil Costa, Graciela Verónica Printista, Alicia Marcela |
author |
Gil Costa, Graciela Verónica |
author_facet |
Gil Costa, Graciela Verónica Printista, Alicia Marcela |
author_role |
author |
author2 |
Printista, Alicia Marcela |
author2_role |
author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Informática Clustering Search process Parallel algorithms metric spaces parallel search distance computations |
topic |
Ciencias Informáticas Informática Clustering Search process Parallel algorithms metric spaces parallel search distance computations |
dc.description.none.fl_txt_mv |
Web Engines are a useful tool for searching information in the Web. But a great part of this information is non-textual and for that case a metric space is used. A metric space is a set where a notion of distance (called a metric) between elements of the set is defined. In this paper we present an efficient parallelization of a pivot-based method devised for this purpose which is called the Sparse Spatial Selection (SSS) strategy and we compare it with a clustering-based method, a parallel implementation of the Spatial Approximation Tree (SAT). We show that SAT compares favourably against the pivot data structures SSS. The experimental results were obtained on a highperformance cluster and using several metric spaces, that shows load balance parallel strategies for the SAT. The implementations are built upon the BSP parallel computing model, which shows efficient performance for this application domain and allows a precise evaluation of algorithms. VIII Workshop de Procesamiento Distribuido y Paralelo Red de Universidades con Carreras en Informática (RedUNCI) |
description |
Web Engines are a useful tool for searching information in the Web. But a great part of this information is non-textual and for that case a metric space is used. A metric space is a set where a notion of distance (called a metric) between elements of the set is defined. In this paper we present an efficient parallelization of a pivot-based method devised for this purpose which is called the Sparse Spatial Selection (SSS) strategy and we compare it with a clustering-based method, a parallel implementation of the Spatial Approximation Tree (SAT). We show that SAT compares favourably against the pivot data structures SSS. The experimental results were obtained on a highperformance cluster and using several metric spaces, that shows load balance parallel strategies for the SAT. The implementations are built upon the BSP parallel computing model, which shows efficient performance for this application domain and allows a precise evaluation of algorithms. |
publishDate |
2007 |
dc.date.none.fl_str_mv |
2007-10 |
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/10915/22974 |
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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) |
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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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