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Spatial data management in apache spark: the GeoSpark perspective and beyond / Jia Yu in Geoinformatica, vol 23 n° 1 (January 2019)  

Public [article]  inGeoinformatica > vol 23 n° 1 (January 2019) . - pp 37 - 78 Titre : Spatial data management in apache spark: the GeoSpark perspective and beyond Type de document : Article/Communication Auteurs : Jia Yu, Auteur ; Zongsi Zhang, Auteur ; Mohamed Sarwat, Auteur Année de publication : 2019 Article en page(s) : pp 37 - 78 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] analyse comparative [Termes IGN] Apache (serveur) [Termes IGN] arbre k-d [Termes IGN] arbre quadratique [Termes IGN] arbre-R [Termes IGN] données massives [Termes IGN] Hadoop [Termes IGN] index spatial [Termes IGN] performance [Termes IGN] Spark [Termes IGN] traitement réparti Résumé : (auteur) The paper presents the details of designing and developing GeoSpark, which extends the core engine of Apache Spark and SparkSQL to support spatial data types, indexes, and geometrical operations at scale. The paper also gives a detailed analysis of the technical challenges and opportunities of extending Apache Spark to support state-of-the-art spatial data partitioning techniques: uniform grid, R-tree, Quad-Tree, and KDB-Tree. The paper also shows how building local spatial indexes, e.g., R-Tree or Quad-Tree, on each Spark data partition can speed up the local computation and hence decrease the overall runtime of the spatial analytics program. Furthermore, the paper introduces a comprehensive experiment analysis that surveys and experimentally evaluates the performance of running de-facto spatial operations like spatial range, spatial K-Nearest Neighbors (KNN), and spatial join queries in the Apache Spark ecosystem. Extensive experiments on real spatial datasets show that GeoSpark achieves up to two orders of magnitude faster run time performance than existing Hadoop-based systems and up to an order of magnitude faster performance than Spark-based systems. Numéro de notice : A2019-225 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-018-0330-9 Date de publication en ligne : 22/10/2018 En ligne : http://dx.doi.org/10.1007/s10707-018-0330-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92621 [article]

Query rewriting for semantic query optimization in spatial databases / Eduardo Mella in Geoinformatica, vol 23 n° 1 (January 2019)  

Public [article]  inGeoinformatica > vol 23 n° 1 (January 2019) . - pp 79 - 104 Titre : Query rewriting for semantic query optimization in spatial databases Type de document : Article/Communication Auteurs : Eduardo Mella, Auteur ; M. Andrea Rodríguez, Auteur ; Loreto Bravo, Auteur ; Diego Gatica, Auteur Année de publication : 2019 Article en page(s) : pp 79 - 104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] contrainte d'intégrité [Termes IGN] jointure spatiale [Termes IGN] optimisation (mathématiques) [Termes IGN] programmation par contraintes [Termes IGN] relation topologique [Termes IGN] requête (informatique) [Termes IGN] requête spatiale [Termes IGN] système de gestion de bases de données orientées objet Résumé : (auteur) Query processing is an important challenge for spatial databases due to the use of complex data types that represent spatial attributes. In particular, due to the cost of spatial joins, several optimization algorithms based on indexing structures exist. The work in this paper proposes a strategy for semantic query optimization of spatial join queries. The strategy detects queries with empty results and rewrites queries to eliminate unnecessary spatial joins or to replace spatial by thematic joins. This is done automatically by analyzing the semantics imposed by the database schema through topological dependencies and topological referential integrity constraints. In this way, the strategy comes to complement current state-of-art algorithms for processing spatial join queries. The experimental evaluation with real data sets shows that the optimization strategy can achieve a decrease in the time cost of a join query using indexing structures in a spatial database management system (SDBMS). Numéro de notice : A2019-224 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-018-00335-w Date de publication en ligne : 04/01/2019 En ligne : http://dx.doi.org/10.1007/s10707-018-00335-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92623 [article]