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Auteur Jianqiu Xu |
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Querying visible points in large obstructed space / Jianqiu Xu in Geoinformatica, vol 19 n° 3 (July - September 2015)
[article]
Titre : Querying visible points in large obstructed space Type de document : Article/Communication Auteurs : Jianqiu Xu, Auteur ; Ralf Hartmut Güting, Auteur Année de publication : 2015 Article en page(s) : pp 435 - 461 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données localisées
[Termes IGN] géomètrie algorithmique
[Termes IGN] noeud
[Termes IGN] performance
[Termes IGN] point d'intérêt
[Termes IGN] structure de données localisées
[Termes IGN] traitement automatique de données
[Termes IGN] traitement de données localisées
[Termes IGN] triangulation (topologie)Résumé : (auteur) Querying visible points is a fundamental problem in computational geometry and spatial databases. With the development of new applications such as trip planning and nearest neighbors, querying visible points plays a key role in obstacle space and the result can be further used such as defining the shortest path. Thereby, efficiently finding the result is essentially important. However, the performance of current methods decrease substantially for large datasets. To solve the problem, we proposes a new and fast algorithm to find visible points for an arbitrary query location inside a large polygon containing obstacles. The method is based on polygon triangulation. By decomposing the polygon into a set of triangles, we manage the polygon by organizing triangles in an efficient way instead of maintaining a large number of vertices. We propose a data structure to partition the searching space into several parts, each of which is independently processed. Afterwards, by recursively calling a method we search visible points by accessing triangles and return the result in a progressive way. Through a theoretical analysis, assuming the polygon contains N vertices in total, the time complexity of our algorithm is O(N), improving the existing method O(N l o g N). We prove the correctness of the algorithm and analyze the space complexity, which is O(N). The technique is extended to return visible points less than a threshold distance to the query location. Using both synthetic and real datasets, we perform extensive experiments to test our algorithm and demonstrate its efficiency and effectiveness. Visible points are efficiently processed in a large obstacle space with over one million vertices. Experimental results show that our technique gains more than one order of magnitude speedup compared to competitive methods using large datasets. Numéro de notice : A2015-495 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-014-0213-7 Date de publication en ligne : 15/08/2014 En ligne : https://doi.org/10.1007/s10707-014-0213-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77307
in Geoinformatica > vol 19 n° 3 (July - September 2015) . - pp 435 - 461[article]The TM-RTree: an index on generic moving objects for range queries / Jianqiu Xu in Geoinformatica, vol 19 n° 3 (July - September 2015)
[article]
Titre : The TM-RTree: an index on generic moving objects for range queries Type de document : Article/Communication Auteurs : Jianqiu Xu, Auteur ; Ralf Hartmut Güting, Auteur ; Yu Zheng, Auteur Année de publication : 2015 Article en page(s) : pp 487 - 524 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] arbre-R
[Termes IGN] distance
[Termes IGN] données spatiotemporelles
[Termes IGN] index spatial
[Termes IGN] objet mobile
[Termes IGN] requête spatiale
[Termes IGN] transportRésumé : (auteur) Existing works on moving objects mainly focus on a single environment such as free space and road network, and do not investigate the complete trip for humans who can pass several environments, e.g., road network, pavement areas, indoor. In this paper, we consider multiple environments and study moving objects with different transportation modes, also called generic moving objects. We aim to answer a new class of queries supporting three kinds of conditions: temporal, spatial, and transportation modes. To efficiently provide the result, we propose an index structure called TM-RTree, which takes into account the feature of moving objects in different environments and has the capability of managing objects on not only temporal and spatial data but also transportation modes. This property is not maintained by existing indices for moving objects. Different cases on transportation modes are supported. Correspondingly, several algorithms are developed. The TM-RTree and related algorithms are developed in a real DBMS to have a practical and solid result for applications. In the experiment, we conduct the performance evaluation using extensive datasets and compare the proposed technique with the other two competitors, demonstrating the efficiency and significant superiority of our solution in various settings. Numéro de notice : A2015-496 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-014-0218-2 Date de publication en ligne : 11/09/2014 En ligne : https://doi.org/10.1007/s10707-014-0218-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77308
in Geoinformatica > vol 19 n° 3 (July - September 2015) . - pp 487 - 524[article]GMOBench: Benchmarking generic moving objects / Jianqiu Xu in Geoinformatica, vol 19 n° 2 (April - June 2015)
[article]
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 performanceRésumé : (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
in Geoinformatica > vol 19 n° 2 (April - June 2015) . - pp 227 - 276[article]