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Handling high-level queries in location-based services for user groups / P. Partsinevelos in Geoinformatica, vol 10 n° 2 (June - August 2006)  

Public [article]  inGeoinformatica > vol 10 n° 2 (June - August 2006) . - pp 213 - 234 Titre : Handling high-level queries in location-based services for user groups Type de document : Article/Communication Auteurs : P. Partsinevelos, Auteur ; Nectaria Tryfona, Auteur Année de publication : 2006 Article en page(s) : pp 213 - 234 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes IGN] requête spatiale [Termes IGN] service fondé sur la position [Termes IGN] utilisateur Résumé : (Auteur) In this paper, we deal with the high level management of data involved in Location-Based Services in order to accommodate corresponding queries. A series of techniques are employed on major components of an LBS, namely mobile users, application environment and selected services. First, a grouping technique generalizes the spatio-temporal data describing the mobile users' movement. The environment is represented under a combined spatial and content hierarchy according to the application at hand. Finally, in the service component, relational operators are formed to support relative spatio-temporal queries, while lifeline data types are constructed to extract and compare behavioral trend patterns among the formed user groups. We show how all proposed techniques communicate under a common database schema. Answers to characteristic queries demonstrate the applicability of this work. Copyright Springer Numéro de notice : A2006-217 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1007/s10707-006-7580-7 En ligne : https://doi.org/10.1007/s10707-006-7580-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27944 [article]

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Reconstructing spatiotemporal trajectories from sparse data / P. Partsinevelos in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 1 (December 2005 - March 2006)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 60 n° 1 (December 2005 - March 2006) . - pp 3 - 16 Titre : Reconstructing spatiotemporal trajectories from sparse data Type de document : Article/Communication Auteurs : P. Partsinevelos, Auteur ; Peggy Agouris, Auteur ; A. Stefanidis, Auteur Année de publication : 2005 Article en page(s) : pp 3 - 16 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse de groupement [Termes IGN] carte de Kohonen [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal [Termes IGN] données spatiotemporelles [Termes IGN] modélisation spatio-temporelle [Termes IGN] objet mobile [Termes IGN] reconstruction d'itinéraire ou de trajectoire [Termes IGN] segmentation [Termes IGN] seuillage Résumé : (Auteur) In motion imagery-based tracking applications, it is common to extract locations of moving objects without any knowledge about the identity of the objects they correspond to. The identification of individual spatiotemporal trajectories from such data sets is far from trivial when these trajectories intersect in space, time, or attributes. In this paper, we present a novel approach for the reconstruction of entangled spatiotemporal trajectories of moving objects captured in motion imagery data sets. We have developed AGENT (Attribute-aided Classification of Entangled Trajectories), a novel framework that comprises classification, clustering, and neural net processes to progressively reconstruct elongated trajectories using as input spatiotemporal coordinates of image patches and corresponding attribute values. AGENT proceeds by first forming brief fragments and then linking them and adding points to them. An initial classification allows us to form brief segments corresponding to distinct objects. These segments are then linked together through clustering to form longer trajectories. Back-propagation neural network classification and geometric/self-organizing map (SOM) analysis refine these trajectories by removing misclassified and redistributing unassigned points. Thus, AGENT integrates some established classification and clustering tools to devise a novel approach that can address the tracking challenges of busy environments. Furthermore, AGENT allows us use spatiotemporal (ST) thresholds to cluster trajectories according to their spatial and temporal extent. In the paper, we present in detail our framework and experimental results that support the application potential of our approach. Numéro de notice : A2006-218 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2005.10.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2005.10.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27945 [article]

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