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GMOBench: Benchmarking generic moving objects / Jianqiu Xu in Geoinformatica, vol 19 n° 2 (April - June 2015)  

Public [article]  inGeoinformatica > vol 19 n° 2 (April - June 2015) . - pp 227 - 276 Titre : GMOBench: Benchmarking generic moving objects Type de document : Article/Communication Auteurs : Jianqiu Xu, Auteur ; Ralf Hartmut Güting, Auteur ; Xiaolin Qin, Auteur Année de publication : 2015 Article en page(s) : pp 227 - 276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] arbre-R [Termes IGN] base de données orientée objet [Termes IGN] implémentation (informatique) [Termes IGN] index spatial [Termes IGN] mobilité humaine [Termes IGN] mobilité urbaine [Termes IGN] objet mobile [Termes IGN] point de repère [Termes IGN] requête spatiale [Termes IGN] test de performance Résumé : (auteur) In real world scenarios, people’s movement include several environments rather than one, for example, road network, pavement areas and indoor. This imposes a new challenge for moving objects database that the complete trip needs to be managed by a database system. In the meantime, novel queries regarding different transportation modes should also be supported. Since existing methods are limited to trips in a single environment and do not support queries on moving objects with different transportation modes, new technologies are essentially needed in a database system. In this paper, we introduce a benchmark called GMOBench that aims to evaluate the performance of a database system managing moving objects in different environments. GMOBench is settled in a realistic scenario and is comprised of three components: (1) a data generator with the capability of creating a scalable set of trips representing the complete movement of humans (both indoor and outdoor); (2) a set of carefully designed and benchmark queries; (3) Mode-RTree, an index structure for managing generic moving objects. The generator defines some parameters so that users can control the characteristics of results. We create the benchmark data in such a way that the dataset can mirror important characteristics and real world distributions of human mobility. Efficient access methods and optimization techniques are developed for query processing. In particular, we propose an index structure called Mode-RTree to manage moving objects in different environments. By employing the proposed index, the cost of benchmark queries is greatly reduced. GMOBench is implemented in a real database system to have a practical result. We perform an extensive experimental study on comprehensive datasets to evaluate the performance. The results show that by using the Mode-RTree we achieve significant performance improvement over the baseline method, demonstrating the effectiveness and efficiency of our approaches. Numéro de notice : A2015-488 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-014-0211-9 Date de publication en ligne : 26/06/2014 En ligne : https://doi.org/10.1007/s10707-014-0211-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77281 [article]