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A road network embedding technique for k-nearest neighbor search in moving object databases / M.R. Kolahdouzan in Geoinformatica, vol 7 n° 3 (September - November 2003)  

Public [article]  inGeoinformatica > vol 7 n° 3 (September - November 2003) . - pp 255 - 273 Titre : A road network embedding technique for k-nearest neighbor search in moving object databases Type de document : Article/Communication Auteurs : M.R. Kolahdouzan, Auteur ; C. Shahabi, Auteur ; M. Sharifzadeh, Auteur Année de publication : 2003 Conférence : ACM GIS 2002, 10th ACM International Symposium on Advances in Geographic Information Systems 08/11/2002 09/11/2002 McLean Virginie - Etats-Unis Selected papers Article en page(s) : pp 255 - 273 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données localisées [Termes IGN] classification barycentrique [Termes IGN] distance euclidienne [Termes IGN] espace euclidien [Termes IGN] mise à jour de base de données [Termes IGN] réseau routier [Termes IGN] système d'information géographique [Termes IGN] topologie Résumé : (Auteur) A very important class of queries in GIS applications is the class of Knearest neighbor queries. Most of the current studies on the Knearest neighbor queries utilize spatial index structures and hence are based on the Euclidean distances between the points. In realworld road networks, however, the shortest distance between two points depends on the actual path connecting the points and cannot be computed accurately using one of the Minkowski metrics. Thus, the Euclidean distance may not properly approximate the real distance. In this paper, we apply an embedding technique to transform a road network to a high dimensional space in order to utilize computationally simple Minkowski metrics for distance measurement. Subsequently, we extend our approach to dynamically transform new points into the embedding space. Finally, we propose an efficient technique that can find the actual shortest path between two points in the original road network using only the embedding space. Our empirical experiments indicate that the Chessboard distance metric (L,) in the embedding space preserves the ordering of the distances between a point and its neighbors more precisely as compared to the Euclidean distance in the original road network. Numéro de notice : A2003-202 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1023/A:1025153016110 En ligne : https://doi.org/10.1023/A:1025153016110 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22498 [article]

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