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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/50446
Ver los metadatos del registro completo
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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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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 |
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eng |
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eng |
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info:eu-repo/semantics/altIdentifier/isbn/978-987-3806-05-6 info:eu-repo/semantics/reference/hdl/10915/50028 |
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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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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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