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Distributed and parallel architectures for spatial data / Alberto Belussi (2020)  

Public Titre : Distributed and parallel architectures for spatial data Type de document : Monographie Auteurs : Alberto Belussi, Éditeur scientifique ; Sara Migliorini, Éditeur scientifique ; Damiano Carra, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 170 p. ISBN/ISSN/EAN : 978-3-03936-751-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] base de données localisées [Termes IGN] collecte de données [Termes IGN] développement durable [Termes IGN] données localisées [Termes IGN] données massives [Termes IGN] entrepôt de données localisées [Termes IGN] géoportail [Termes IGN] Hadoop [Termes IGN] métadonnées [Termes IGN] modèle numérique de surface [Termes IGN] objet mobile [Termes IGN] OLAP [Termes IGN] OpenStreetMap [Termes IGN] PostGIS [Termes IGN] réseau social [Termes IGN] SQL [Termes IGN] système d'information géographique [Termes IGN] téléphone intelligent [Termes IGN] traitement parallèle [Termes IGN] zone tampon Résumé : (Editeur) [Préface] In recent years, an increasing amount of spatial data has been collected by different types of devices, such as mobile phones, sensors, satellites, space telescope, and medical tools for analysis, or is generated by social networks, such as geotagged tweets. The processing of this huge amount of information, including spatial properties, which are frequently represented in heterogeneous ways, is a challenging task that has boosted research in the big data area in an attempt to investigate cases and propose new solutions for dealing with its peculiarities. In the literature, many different proposals and approaches for facing the problem have been proposed, addressing different goals and different types of users. However, most are obtained by customizing existing approaches which were originally developed for the processing of big data of the alphanumeric type, without any specific support for spatial or spatiotemporal properties. Thus, the proposed solutions can exploit the parallelism provided by these kinds of systems, but without taking into account, in a proficient way, the space and time dimensions that intrinsically characterize the analyzed datasets. As described in the literature, current solutions include: (i) the on-top approach, where an underlying system for traditional big datasets is used as a black box while spatial processing is added through the definition of user-defined functions that are specified on top of the underlying system; (ii) the from-scratch approach, where a completely new system is implemented for a specific application context; and (iii) the built-in approach, where an existing solution is extended by injecting spatial data functions into its core. This book aims at promoting new and innovative studies, proposing new architectures or innovative evolutions of existing ones, and illustrating experiments on current technologies in order to improve the efficiency and effectiveness of distributed and cluster systems when they deal with spatiotemporal data. Note de contenu : Preface 1- Distributed Processing of Location-Based Aggregate Queries Using MapReduce 2- Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa 3- HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time 4- Mobility DataWarehouses 5- Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC 6- LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data 7- Mr4Soil: A MapReduce-Based Framework Integrated with GIS for Soil Erosion Modelling 8- High-Performance Geospatial Big Data Processing System Based on MapReduce Numéro de notice : 25884 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03936-751-1 En ligne : https://doi.org/10.3390/books978-3-03936-751-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95762