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est un bulletin de Geomatica / Canadian institute of geomatics = Association canadienne des sciences géomatiques (Canada) (1993 -)
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Ajouter le résultat dans votre panierExploiting location-aware social networks for efficient spatial query processing / Liang Tang in Geoinformatica, vol 21 n° 1 (January - March 2017)
[article]
Titre : Exploiting location-aware social networks for efficient spatial query processing Type de document : Article/Communication Auteurs : Liang Tang, Auteur ; Haiquan Chen, Auteur Année de publication : 2017 Article en page(s) : pp 33 - 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] point d'intérêt
[Termes IGN] requête spatiale
[Termes IGN] service fondé sur la position
[Termes IGN] téléphonie mobileRésumé : (auteur) In this paper, we introduce two watchtower-based parameter-tunable frameworks for efficient spatial processing with sparse distributions of Points of Interest (POIs) by exploiting mobile users’ check-in data collected from the location-aware social networks. In our proposed frameworks, the network traversal can terminate earlier by retrieving the distance information stored in watchtowers. More important, by observing that people’s movement often exhibits a strong spatial pattern, we employ Bayesian Information Criterion-based cluster analysis to model mobile users’ check-in data as a mixture of 2-dimensional Gaussian distributions, where each cluster corresponds to a geographical hot zone. Afterwards, POI watchtowers are established in the hot zones and non-hot zones discriminatorily. Moreover, we discuss the optimal watchtower deployment mechanism in order to achieve a desired balance between the off-line pre-computation cost and the on-line query efficiency. Finally, the superiority of our solutions over the state-of-the-art approaches is demonstrated using the real data collected from Gowalla with large-scale road networks. Numéro de notice : A2017-025 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0271-0 En ligne : http://dx.doi.org/10.1007/s10707-016-0271-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83976
in Geoinformatica > vol 21 n° 1 (January - March 2017) . - pp 33 - 55[article]Finding dense locations in symbolic indoor tracking data: modeling, indexing, and processing / Tanvir Ahmed in Geoinformatica, vol 21 n° 1 (January - March 2017)
[article]
Titre : Finding dense locations in symbolic indoor tracking data: modeling, indexing, and processing Type de document : Article/Communication Auteurs : Tanvir Ahmed, Auteur ; Toren Bach Pedersen, Auteur ; Hua Lu, Auteur Année de publication : 2017 Article en page(s) : pp 119 - 150 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] données spatiotemporelles
[Termes IGN] géolocalisation
[Termes IGN] index spatial
[Termes IGN] positionnement en intérieur
[Termes IGN] requête spatiale
[Termes IGN] système de gestion de bases de données relationnellesRésumé : (auteur) Finding the dense locations in large indoor spaces is very useful for many applications such as overloaded area detection, security control, crowd management, indoor navigation, and so on. Indoor tracking data can be enormous and are not immediately ready for finding dense locations. This paper presents two graph-based models for constrained and semi-constrained indoor movement, respectively, and then uses the models to map raw tracking records into mapping records that represent object entry and exit times in particular locations. Subsequently, an efficient indexing structure called Hierarchical Dense Location Time Index (HDLT-Index) is proposed for indexing the time intervals of the mapping table, along with index construction, query processing, and pruning techniques. The HDLT-Index supports very efficient aggregate point, interval, and duration queries as well as dense location queries. A comprehensive experimental study with both real and synthetic data shows that the proposed techniques are efficient and scalable and outperforms RDBMSs significantly. Numéro de notice : A2017-026 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0276-8 En ligne : http://dx.doi.org/10.1007/s10707-016-0276-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83977
in Geoinformatica > vol 21 n° 1 (January - March 2017) . - pp 119 - 150[article]