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Algorithms for nearest neighbor search on moving object trajectories / E. Frentzos in Geoinformatica, vol 11 n° 2 (June - August 2007)  

Public [article]  inGeoinformatica > vol 11 n° 2 (June - August 2007) . - pp 159 - 193 Titre : Algorithms for nearest neighbor search on moving object trajectories Type de document : Article/Communication Auteurs : E. Frentzos, Auteur ; K. Gratsias, Auteur ; Nikos Pelekis, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 159 - 193 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] arbre-R [Termes IGN] base de données d'objets mobiles [Termes IGN] classification barycentrique [Termes IGN] continuité géographique [Termes IGN] distance euclidienne [Termes IGN] objet mobile [Termes IGN] objet statique [Termes IGN] spatial metrics Résumé : (Auteur) Nearest Neighbor (NN) search has been in the core of spatial and spatiotemporal database research during the last decade. The literature on NN query processing algorithms so far deals with either stationary or moving query points over static datasets or future (predicted) locations over a set of continuously moving points. With the increasing number of Mobile Location Services (MLS), the need for effective k-NN query processing over historical trajectory data has become the vehicle for data analysis, thus improving existing or even proposing new services. In this paper, we investigate mechanisms to perform NN search on R-tree-like structures storing historical information about moving object trajectories. The proposed (depth-first and best-first) algorithms vary with respect to the type of the query object (stationary or moving point) as well as the type of the query result (historical continuous or not), thus resulting in four types of NN queries. We also propose novel metrics to support our search ordering and pruning strategies. Using the implementation of the proposed algorithms on two members of the R-tree family for trajectory data (namely, the TB-tree and the 3D-R-tree), we demonstrate their scalability and efficiency through an extensive experimental study using large synthetic and real datasets. Copyright Springer Numéro de notice : A2007-236 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-006-0007-7 En ligne : https://doi.org/10.1007/s10707-006-0007-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28599 [article]

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