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, Implémentation de types spatio-temporels et des éléments pour l'identification de Black Holes

, Illustration de l'ensemble des messages reçus durant l'étude

D. Vue-macroscopique,

, Résultats expérimentaux (28 et 29 Mars 2015)

, Cellules vides observées pour six heures et un jour

, Cellules vides observées pour des mailles de taille 0.1*

, Cellules vides observées pour des mailles de taille 0

, 2.1 Modèle de données pour l'étude des mobilités maritimes, Classification des cellules considérant une fenêtre temporelle de six heures et un mois de données historiques, p.112

. .. Modèle, 114 6.3.1 Requêtes persistantes typiques pour l'analyse du trafic maritime, p.115

.. .. Conclusion,

. .. De-couverture, 129 7.4.5 Analyse de l'influence de la taille de la fenêtre temporelle

.. .. Conclusion,

, ensuite les mécanismes d'un traitement hybride au travers d'une requête spécifique puis enfin le contexte maritime et la déclinaison de notre modèle pour le suivi du trafic maritime. Dans cette partie nous présentons et explicitons la façon dont notre approche a été mise en place, l'application de l'algorithme détection de Black Holes à des données réelles de positions de navires ainsi que l, Dans les parties précédentes différentes notions ont été abordées d'abord le modèle de gestion d'objets mobiles proposé

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. Titre, Une approche holistique combinant flux temps-réel et données archivées pour la gestion et le traitement d'objets mobiles : application au trafic maritime

, Mots clés : Objets mobiles, traitement temps-réel, bases de données spatio-temporelles