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<title>Conferencias</title>
<link href="http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1390" rel="alternate"/>
<subtitle>Conferencias relacionadas con Ingeniería del Software</subtitle>
<id>http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1390</id>
<updated>2026-05-22T19:05:09Z</updated>
<dc:date>2026-05-22T19:05:09Z</dc:date>
<entry>
<title>NAVIGATIONAL RULE DERIVATION: AN ALGORITHM TO DETERMINE THE EFFECT OF TRAFFIC SIGNS ON ROAD NETWORKS</title>
<link href="http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1404" rel="alternate"/>
<author>
<name>Galaktionov, Daniil</name>
</author>
<author>
<name>R. Luaces, Miguel</name>
</author>
<author>
<name>S. Places, Ángeles</name>
</author>
<id>http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1404</id>
<updated>2025-10-20T11:12:18Z</updated>
<published>2016-01-01T00:00:00Z</published>
<summary type="text">NAVIGATIONAL RULE DERIVATION: AN ALGORITHM TO DETERMINE THE EFFECT OF TRAFFIC SIGNS ON ROAD NETWORKS
Galaktionov, Daniil; R. Luaces, Miguel; S. Places, Ángeles
In this paper we present an algorithm to build a road network map enriched with traffic rules such as&#13;
   one-way streets and forbidden turns, based on the interpretation of already detected and classified&#13;
   traffic signs. Such algorithm helps to automatize the elaboration of maps for commercial navigation&#13;
   systems.&#13;
   Our solution is based on simulating navigation along the road network, determining at each point of&#13;
   interest the visibility of the signs and their effect on the roads. We test our approach in a small urban&#13;
   network and discuss various ways to generalize it to support more complex environments.&#13;
   Keywords: GIS, Traffic signs, Transport networks, Graph navigation
</summary>
<dc:date>2016-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Storing and Clustering Large Spatial Datasets Using Big Data Technologies</title>
<link href="http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1399" rel="alternate"/>
<author>
<name>Cortiñas, Alejandro</name>
</author>
<author>
<name>R. Luaces, Miguel</name>
</author>
<author>
<name>Varela-Rodeiro, Tirso</name>
</author>
<id>http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1399</id>
<updated>2025-10-20T11:13:21Z</updated>
<published>2018-01-01T00:00:00Z</published>
<summary type="text">Storing and Clustering Large Spatial Datasets Using Big Data Technologies
Cortiñas, Alejandro; R. Luaces, Miguel; Varela-Rodeiro, Tirso
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.
</summary>
<dc:date>2018-01-01T00:00:00Z</dc:date>
</entry>
</feed>
