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dc.date.accessioned 2013-11-22T13:38:27Z
dc.date.available 2013-11-22T13:38:27Z
dc.date.issued 2013-10
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/31295
dc.description.abstract The Dynamic Spatial Approximation Tree (DSAT) is a data structure specially designed for searching in metric spaces. It has been shown that it compares favorably against alternative data structures in spaces of high dimension or queries with low selectivity. The DSAT supports insertion and deletions of elements. However, it has been noted that eliminations degrade the structure over time. In [8] is proposed a method to handle deletions over the DSAT, which shown to be superior to the former in the sense that it permits controlling the expected deletion cost as a proportion of the insertion cost. In this paper we propose and study a new deletion method, based on the deletions strategies presented in [8], which has demonstrated to be better. The outcome is a fully dynamic data structure that can be managed through insertions and deletions over arbitrarily long periods of time without any reorganization. en
dc.language es es
dc.subject multimedia databases en
dc.subject DATABASE MANAGEMENT es
dc.subject Data mining es
dc.subject metric spaces en
dc.subject similarity search en
dc.title New deletion method for dynamic spatial approximation trees en
dc.type Objeto de conferencia es
sedici.creator.person Kasián, Fernando es
sedici.creator.person Ludueña, Verónica es
sedici.creator.person Reyes, Nora Susana es
sedici.creator.person Roggero, Patricia es
sedici.description.note X Workshop bases de datos y minería de datos es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.relation.event XVIII Congreso Argentino de Ciencias de la Computación es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)