List of Clustered Permutations in Secondary Memory

Autores
Roggero, Patricia; Reyes, Nora Susana; Figueroa, Karina; Paredes, Rodrigo
Año de publicación
2015
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Similarity search is a difficult problem and various indexing schemas have been defined to process similarity queries efficiently in many applications, including multimedia databases and other repositories handling complex objects. Metric indices support efficient similarity searches, but most of them are designed for main memory. Thus, they can handle only small datasets, suffering serious performance degradations when the objects reside on disk.Most real-life database applications require indices able to work on secondary memory. Among a plethora of indices, the List of Clustered Permutations (LCP) has shown to be competitive in main memory, since groups the permutations and establishes a criterion to discard whole clusters according the permutation of their centers. We introduce a secondary-memory variant of the LCP, which maintains the low number of distance evaluations when comparing the permutations themselves, and also needs a low number of I/O operations at construction and searching.
XII Workshop Bases de Datos y Minería de Datos (WBDDM)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Metrics
Secondary storage
permutation-based algorithm
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/50446

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network_name_str SEDICI (UNLP)
spelling List of Clustered Permutations in Secondary MemoryRoggero, PatriciaReyes, Nora SusanaFigueroa, KarinaParedes, RodrigoCiencias InformáticasMetricsSecondary storagepermutation-based algorithmSimilarity search is a difficult problem and various indexing schemas have been defined to process similarity queries efficiently in many applications, including multimedia databases and other repositories handling complex objects. Metric indices support efficient similarity searches, but most of them are designed for main memory. Thus, they can handle only small datasets, suffering serious performance degradations when the objects reside on disk.Most real-life database applications require indices able to work on secondary memory. Among a plethora of indices, the List of Clustered Permutations (LCP) has shown to be competitive in main memory, since groups the permutations and establishes a criterion to discard whole clusters according the permutation of their centers. We introduce a secondary-memory variant of the LCP, which maintains the low number of distance evaluations when comparing the permutations themselves, and also needs a low number of I/O operations at construction and searching.XII Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI)2015-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/50446enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-3806-05-6info:eu-repo/semantics/reference/hdl/10915/50028info: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:UNLP2025-09-10T12:07:16Zoai:sedici.unlp.edu.ar:10915/50446Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-10 12:07:17.192SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv List of Clustered Permutations in Secondary Memory
title List of Clustered Permutations in Secondary Memory
spellingShingle List of Clustered Permutations in Secondary Memory
Roggero, Patricia
Ciencias Informáticas
Metrics
Secondary storage
permutation-based algorithm
title_short List of Clustered Permutations in Secondary Memory
title_full List of Clustered Permutations in Secondary Memory
title_fullStr List of Clustered Permutations in Secondary Memory
title_full_unstemmed List of Clustered Permutations in Secondary Memory
title_sort List of Clustered Permutations in Secondary Memory
dc.creator.none.fl_str_mv Roggero, Patricia
Reyes, Nora Susana
Figueroa, Karina
Paredes, Rodrigo
author Roggero, Patricia
author_facet Roggero, Patricia
Reyes, Nora Susana
Figueroa, Karina
Paredes, Rodrigo
author_role author
author2 Reyes, Nora Susana
Figueroa, Karina
Paredes, Rodrigo
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Metrics
Secondary storage
permutation-based algorithm
topic Ciencias Informáticas
Metrics
Secondary storage
permutation-based algorithm
dc.description.none.fl_txt_mv Similarity search is a difficult problem and various indexing schemas have been defined to process similarity queries efficiently in many applications, including multimedia databases and other repositories handling complex objects. Metric indices support efficient similarity searches, but most of them are designed for main memory. Thus, they can handle only small datasets, suffering serious performance degradations when the objects reside on disk.Most real-life database applications require indices able to work on secondary memory. Among a plethora of indices, the List of Clustered Permutations (LCP) has shown to be competitive in main memory, since groups the permutations and establishes a criterion to discard whole clusters according the permutation of their centers. We introduce a secondary-memory variant of the LCP, which maintains the low number of distance evaluations when comparing the permutations themselves, and also needs a low number of I/O operations at construction and searching.
XII Workshop Bases de Datos y Minería de Datos (WBDDM)
Red de Universidades con Carreras en Informática (RedUNCI)
description Similarity search is a difficult problem and various indexing schemas have been defined to process similarity queries efficiently in many applications, including multimedia databases and other repositories handling complex objects. Metric indices support efficient similarity searches, but most of them are designed for main memory. Thus, they can handle only small datasets, suffering serious performance degradations when the objects reside on disk.Most real-life database applications require indices able to work on secondary memory. Among a plethora of indices, the List of Clustered Permutations (LCP) has shown to be competitive in main memory, since groups the permutations and establishes a criterion to discard whole clusters according the permutation of their centers. We introduce a secondary-memory variant of the LCP, which maintains the low number of distance evaluations when comparing the permutations themselves, and also needs a low number of I/O operations at construction and searching.
publishDate 2015
dc.date.none.fl_str_mv 2015-10
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dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-3806-05-6
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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
rights_invalid_str_mv 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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