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Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications / Wenwen Li in Computers, Environment and Urban Systems, vol 98 (December 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 98 (December 2022) . - n° 101884 Titre : Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Sizhe Wang, Auteur ; Sheng wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101884 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] base de données relationnelles [Termes IGN] entrepôt de données [Termes IGN] ontologie [Termes IGN] RDF [Termes IGN] référentiel sémantique [Termes IGN] requête spatiale [Termes IGN] réseau sémantique [Termes IGN] SPARQL [Termes IGN] stockage de données [Termes IGN] test de performance [Termes IGN] web sémantique Résumé : (auteur) Knowledge graph has become a cutting-edge technology for linking and integrating heterogeneous, cross-domain datasets to address critical scientific questions. As big data has become prevalent in today's scientific analysis, semantic data repositories that can store and manage large knowledge graph data have become critical in successfully deploying spatially explicit knowledge graph applications. This paper provides a comprehensive evaluation of the popular semantic data repositories and their computational performance in managing and providing semantic support for spatial queries. There are three types of semantic data repositories: (1) triple store solutions (RDF4j, Fuseki, GraphDB, Virtuoso), (2) property graph databases (Neo4j), and (3) an Ontology-Based Data Access (OBDA) approach (Ontop). Experiments were conducted to compare each repository's efficiency (e.g., query response time) in handling geometric, topological, and spatial-semantic related queries. The results show that Virtuoso achieves the overall best performance in both non-spatial and spatial-semantic queries. The OBDA solution, Ontop, has the second-best query performance in spatial and complex queries and the best storage efficiency, requiring the least data-to-RDF conversion efforts. Other triple store solutions suffer from various issues that cause performance bottlenecks in handling spatial queries, such as inefficient memory management and lack of proper query optimization. Numéro de notice : A2022-720 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101884 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101884 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101654 [article]

GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning / Samantha T. Arundel in Transactions in GIS, Vol 24 n° 3 (June 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 3 (June 2020) . - pp 556 - 572 Titre : GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning Type de document : Article/Communication Auteurs : Samantha T. Arundel, Auteur ; Wenwen Li, Auteur ; Sizhe Wang, Auteur Année de publication : 2020 Article en page(s) : pp 556 - 572 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] cartographie topographique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] collecte de données [Termes IGN] détection automatique [Termes IGN] détection d'objet [Termes IGN] géobalise [Termes IGN] toponyme [Termes IGN] United States Geological Survey Résumé : (Auteur) Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain. Numéro de notice : A2020-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12633 Date de publication en ligne : 08/05/2020 En ligne : https://doi.org/10.1111/tgis.12633 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95307 [article]

Association rules-based multivariate analysis and visualization of spatiotemporal climate data / Feng Wang in ISPRS International journal of geo-information, vol 7 n° 7 (July 2018)  

Public [article]  inISPRS International journal of geo-information > vol 7 n° 7 (July 2018) Titre : Association rules-based multivariate analysis and visualization of spatiotemporal climate data Type de document : Article/Communication Auteurs : Feng Wang, Auteur ; Wenwen Li, Auteur ; Sizhe Wang, Auteur ; Chris R. Johnson, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle [Termes IGN] analyse multivariée [Termes IGN] Arctique [Termes IGN] cyclone [Termes IGN] données météorologiques [Termes IGN] données spatiotemporelles [Termes IGN] phénomène atmosphérique [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) Understanding atmospheric phenomena involves analysis of large-scale spatiotemporal multivariate data. The complexity and heterogeneity of such data pose a significant challenge in discovering and understanding the association between multiple climate variables. To tackle this challenge, we present an interactive heuristic visualization system that supports climate scientists and the public in their exploration and analysis of atmospheric phenomena of interest. Three techniques are introduced: (1) web-based spatiotemporal climate data visualization; (2) multiview and multivariate scientific data analysis; and (3) data mining-enabled visual analytics. The Arctic System Reanalysis (ASR) data are used to demonstrate and validate the effectiveness and usefulness of our method through a case study of “The Great Arctic Cyclone of 2012”. The results show that different variables have strong associations near the polar cyclone area. This work also provides techniques for identifying multivariate correlation and for better understanding the driving factors of climate phenomena. Numéro de notice : A2018-503 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7070266 Date de publication en ligne : 09/07/2018 En ligne : https://doi.org/10.3390/ijgi7070266 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90575 [article]

PolarGlobe : A web-wide virtual globe system for visualizing multidimensional, time-varying, big climate data / Wenwen Li in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 7-8 (July - August 2017) . - pp 1562 - 1582 Titre : PolarGlobe : A web-wide virtual globe system for visualizing multidimensional, time-varying, big climate data Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Sizhe Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1562 - 1582 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Arctique [Termes IGN] changement climatique [Termes IGN] cyberinfrastructure [Termes IGN] données massives [Termes IGN] données multidimensionnelles [Termes IGN] expérience scientifique [Termes IGN] géovisualisation [Termes IGN] globe virtuel [Termes IGN] image multitemporelle [Termes IGN] prototype [Termes IGN] rendu (géovisualisation) [Termes IGN] webGL [Vedettes matières IGN] Géovisualisation Résumé : (Auteur) The increasing research interest in global climate change and the rise of the public awareness have generated a significant demand for new tools to support effective visualization of big climate data in a cyber environment such that anyone from any location with an Internet connection and a web browser can easily view and comprehend the data. In response to the demand, this paper introduces a new web-based platform for visualizing multidimensional, time-varying climate data on a virtual globe. The web-based platform is built upon a virtual globe system Cesium, which is open-source, highly extendable and capable of being easily integrated into a web environment. The emerging WebGL technique is adapted to support interactive rendering of 3D graphics with hardware graphics acceleration. To address the challenges of transmitting and visualizing voluminous, complex climate data over the Internet to support real-time visualization, we develop a stream encoding and transmission strategy based on video-compression techniques. This strategy allows dynamic provision of scientific data in different precisions to balance the needs for scientific analysis and visualization cost. Approaches to represent, encode and decode processed data are also introduced in detail to show the operational workflow. Finally, we conduct several experiments to demonstrate the performance of the proposed strategy under different network conditions. A prototype, PolarGlobe, has been developed to visualize climate data in the Arctic regions from multiple angles. Numéro de notice : A2017-312 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1306863 En ligne : http://dx.doi.org/10.1080/13658816.2017.1306863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85366 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2017041	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2017042	RAB	Revue	Centre de documentation	En réserve L003	Disponible