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Unveiling movement uncertainty for robust trajectory similarity analysis / Andre Salvaro Furtado in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 1-2 (January - February 2018) . - pp 140 - 168 Titre : Unveiling movement uncertainty for robust trajectory similarity analysis Type de document : Article/Communication Auteurs : Andre Salvaro Furtado, Auteur ; Luis Otavio Alvares, Auteur ; Nikos Pelekis, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 140 - 168 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] distance [Termes IGN] données localisées [Termes IGN] incertitude des données [Termes IGN] mesure de similitude multidimensionnelle [Termes IGN] méthode robuste [Termes IGN] trace GPS [Termes IGN] trajet (mobilité) Résumé : (Auteur) Trajectory data analysis and mining require distance and similarity measures, and the quality of their results is directly related to those measures. Several similarity measures originally proposed for time-series were adapted to work with trajectory data, but these approaches were developed for well-behaved data that usually do not have the uncertainty and heterogeneity introduced by the sampling process to obtain trajectories. More recently, similarity measures were proposed specifically for trajectory data, but they rely on simplistic movement uncertainty representations, such as linear interpolation. In this article, we propose a new distance function, and a new similarity measure that uses an elliptical representation of trajectories, being more robust to the movement uncertainty caused by the sampling rate and the heterogeneity of this kind of data. Experiments using real data show that our proposal is more accurate and robust than related work. Numéro de notice : A2018-023 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1372763 En ligne : https://doi.org/10.1080/13658816.2017.1372763 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89175 [article]

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079-2018011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

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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Code-barres	Cote	Support	Localisation	Section	Disponibilité
057-07021	RAB	Revue	Centre de documentation	En réserve L003	Disponible