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<title>XED - Grupo02</title>
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<description>Subcomunidade do Grupo02 de XED</description>
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<dc:date>2026-05-22T19:05:01Z</dc:date>
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<title>Multilevel modeling of Geographic Information Systems based on International Standards</title>
<link>http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1427</link>
<description>Multilevel modeling of Geographic Information Systems based on International Standards
Pedreira, Óscar
</description>
<dc:date>2021-01-01T00:00:00Z</dc:date>
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<title>Space-Efficient Representations of Raster Time Series</title>
<link>http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1426</link>
<description>Space-Efficient Representations of Raster Time Series
Silva-Coira, Fernando; R. Paramá, José; de Bernardo, Guillermo; Seco, Diego
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<dc:date>2019-01-01T00:00:00Z</dc:date>
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<title>Compresión de textos en Bases de Datos Digitales</title>
<link>http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1419</link>
<description>Compresión de textos en Bases de Datos Digitales
R.Brisaboa, Nieves; Iglesias, E.L.; Navarro, G.; Parmá, J.R.
Este trabajo presenta una revisi´on de los m´etodos de&#13;
compresi´on de textos, que permiten la busqueda ´ directa de palabras y&#13;
frases dentro del texto sin necesidad de descomprimirlo.&#13;
Se presentan las t´ecnicas de compresi´on basadas en Huffman y dos&#13;
t´ecnicas m´as recientes: el m´etodo Denso con Post-Etiquetado y el&#13;
m´etodo (s,c)-Denso. Adem´as se muestra como estos nuevos m´etodos&#13;
son directamente comparables, en tasa de compresi´on, con las t´ecnicas&#13;
basadas en Huffman y c´omo proporcionan una compresi´on m´as simple y&#13;
r´apida, manteniendo sus caracter´ısticas m´as interesantes. De este modo&#13;
estas nuevas t´ecnicas son extremadamente adecuadas para la compresi´on&#13;
de textos sobre los que haya que realizar operaciones de Text Retrieval,&#13;
pues facilita la indexaci´on y preprocesado de los mismos sin necesidad&#13;
de descomprimirlos.
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<dc:date>2003-01-01T00:00:00Z</dc:date>
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<title>Storing and Clustering Large Spatial Datasets Using Big Data Technologies</title>
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<description>Storing and Clustering Large Spatial Datasets Using Big Data Technologies
Rodríguez Luaces, Miguel
In this paper we present the architecture of a system to store,&#13;
query and visualize on the web large datasets of geographic information.&#13;
The architecture includes a component to simulate a large number of&#13;
drivers that report their position on a regular basis, an ingestion component that is generic and can acommodate three different storage technologies, a query component that aggregates the results in order to reduce&#13;
the query time and the data transfered, and a web-based map viewer. In&#13;
addition, we define an evaluation methodology to be used to benchmark&#13;
and compare different alternatives for some components of the system,&#13;
and we validate the architecture with experiments using a dataset of 40&#13;
million locations of drivers.
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<dc:date>2018-01-01T00:00:00Z</dc:date>
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