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Auteur Peng Yue |
Documents disponibles écrits par cet auteur (6)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externesProvenance in GIServices: A semantic web approach / Zhaoyan Wu in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)
Titre : Provenance in GIServices: A semantic web approach Type de document : Article/Communication Auteurs : Zhaoyan Wu, Auteur ; Hao Li, Auteur ; Peng Yue, Auteur Année de publication : 2023 Article en page(s) : n° 118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] données localisées
[Termes IGN] métadonnées
[Termes IGN] ontologie
[Termes IGN] OWL
[Termes IGN] service web
[Termes IGN] service web sémantique
[Termes IGN] source de données
[Termes IGN] système d'information géographique
[Termes IGN] web sémantiqueRésumé : (auteur) Recent developments in Web Service and Semantic Web technologies have shown great promise for the automatic chaining of geographic information services (GIService), which can derive user-specific information and knowledge from large volumes of data in the distributed information infrastructure. In order for users to have an informed understanding of products generated automatically by distributed GIServices, provenance information must be provided to them. This paper describes a three-level conceptual view of provenance: the automatic capture of provenance in the semantic execution engine; the query and inference of provenance. The view adapts well to the three-phase procedure for automatic GIService composition and can increase understanding of the derivation history of geospatial data products. Provenance capture in the semantic execution engine fits well with the Semantic Web environment. Geospatial metadata is tracked during execution to augment provenance. A prototype system is implemented to illustrate the applicability of the approach. Numéro de notice : A2023-145 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12030118 En ligne : https://doi.org/10.3390/ijgi12030118 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102848
in ISPRS International journal of geo-information > vol 12 n° 3 (March 2023) . - n° 118[article]
Titre : Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services Type de document : Article/Communication Auteurs : Mingyue Xu, Auteur ; Peng Yue, Auteur ; Fan Yu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 380 - 402 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] appariement de données localisées
[Termes IGN] apprentissage profond
[Termes IGN] autopartage
[Termes IGN] Chine
[Termes IGN] distribution spatiale
[Termes IGN] interaction humain-espace
[Termes IGN] modèle de Markov
[Termes IGN] système d'information urbain
[Termes IGN] système multi-agents
[Termes IGN] taxi
[Termes IGN] transmission de données
[Termes IGN] zone d'activité économiqueRésumé : (auteur) The popularity of ride-hailing platforms has significantly improved travel efficiency by providing convenient and personalized transportation services. Designing an effective ride-hailing service generally needs to address two tasks: order matching that assigns orders to available vehicles and proactive vehicle repositioning that deploys idle vehicles to potentially high-demand regions. Recent studies have intensively utilized deep reinforcement learning to solve the two tasks by learning an optimal dispatching strategy. However, most of them generate actions for the two tasks independently, neglecting the interactions between the two tasks and the communications among multiple drivers. To this end, this paper provides an approach based on multi-agent deep reinforcement learning where the two tasks are modeled as a unified Markov decision process, and the colossal state space and competition among drivers are addressed. Additionally, a modifiable agent-specific state representation is proposed to facilitate knowledge transferring and improve computing efficiency. We evaluate our approach on a public taxi order dataset collected in Chengdu, China, where a variable number of simulated vehicles are tested. Experimental results show that our approach outperforms seven existing baselines, reducing passenger rejection rate, driver idle time and improving total driver income. Numéro de notice : A2023-058 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2119477 Date de publication en ligne : 07/09/2022 En ligne : https://doi.org/10.1080/13658816.2022.2119477 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102396
in International journal of geographical information science IJGIS > vol 37 n° 2 (February 2023) . - pp 380 - 402[article]
Titre : A multi-source spatio-temporal data cube for large-scale geospatial analysis Type de document : Article/Communication Auteurs : Fan Gao, Auteur ; Peng Yue, Auteur ; Zhipeng Cao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1853 - 1884 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] cube espace-temps
[Termes IGN] cyberinfrastructure
[Termes IGN] données spatiotemporelles
[Termes IGN] Géocube
[Termes IGN] hypercube
[Termes IGN] informatique en nuage
[Termes IGN] intelligence artificielle
[Termes IGN] observation de la TerreRésumé : (auteur) Data management and analysis are challenging with big Earth observation (EO) data. Expanding upon the rising promises of data cubes for analysis-ready big EO data, we propose a new geospatial infrastructure layered over a data cube to facilitate big EO data management and analysis. Compared to previous work on data cubes, the proposed infrastructure, GeoCube, extends the capacity of data cubes to multi-source big vector and raster data. GeoCube is developed in terms of three major efforts: formalize cube dimensions for multi-source geospatial data, process geospatial data query along these dimensions, and organize cube data for high-performance geoprocessing. This strategy improves EO data cube management and keeps connections with the business intelligence cube, which provides supplementary information for EO data cube processing. The paper highlights the major efforts and key research contributions to online analytical processing for dimension formalization, distributed cube objects for tiles, and artificial intelligence enabled prediction of computational intensity for data cube processing. Case studies with data from Landsat, Gaofen, and OpenStreetMap demonstrate the capabilities and applicability of the proposed infrastructure. Numéro de notice : A2022-643 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2087222 Date de publication en ligne : 14/06/2022 En ligne : https://doi.org/10.1080/13658816.2022.2087222 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101458
in International journal of geographical information science IJGIS > vol 36 n° 9 (September 2022) . - pp 1853 - 1884[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2022091 SL Revue Centre de documentation Revues en salle Disponible
Titre : Linked Data and SDI: The case on Web geoprocessing workflows Type de document : Article/Communication Auteurs : Peng Yue, Auteur ; Xia Guo, Auteur ; Mingda Zhang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 245 – 257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] flux de travaux
[Termes IGN] géomatique web
[Termes IGN] ModelBuilder
[Termes IGN] partage de données localisées
[Termes IGN] service web géographique
[Termes IGN] traitement de données localisées
[Termes IGN] web des donnéesRésumé : (auteur) Linked Data transforms traditional ways of structuring, publishing, discovering, accessing, and integrating data. The advantages of Linked Data, including the common data model, standardized data access mechanism, and link-based data discovery, allow effective sharing and discovery of geospatial resources in Spatial Data Infrastructures (SDI). Web geoprocessing workflows have been widely used in SDI to support distributed geoprocessing. Geospatial data and services are discovered from SDI and chained as geoprocessing workflows. Workflow results can be published as new resources in SDI. The whole process could be improved by the Linked Data approach, so that sensors, observations, data, services, workflows, and provenance can be linked and published into the Web of Data. This paper explores the integration of Linked Data and Web geoprocessing workflows by discovering geospatial resources in the Web of Data to build geoprocessing workflows. It adopts the Linked Data approach to publish geospatial data including in-situ observations and satellite images, as well as geospatial Web services. The workflow results, including data products and processing steps, are also exposed as Linked Data in turn for tracing provenance. The results not only support semantic discovery and integration of heterogeneous geospatial resources, but also provide transparency in data sharing and processing. The approach is implemented as extensions to an existing geoprocessing workflow tool, GeoJModelBuilder, to illustrate its applicability. Numéro de notice : A2016-536 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.11.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.11.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81680
in ISPRS Journal of photogrammetry and remote sensing > vol 114 (April 2016) . - pp 245 – 257[article]
Titre : Geoscience data provenance : An overview Type de document : Article/Communication Auteurs : Liping Di, Auteur ; Peng Yue, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 5065 - 5072 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] acquisition de données
[Termes IGN] cyberinfrastructure
[Termes IGN] données localisées
[Termes IGN] état de l'art
[Termes IGN] processus spatio-temorel
[Termes IGN] service web
[Termes IGN] source de données
[Termes IGN] utilisateurRésumé : (Auteur) The advancement of Earth observing sensors, data, and information systems enhances significantly the capabilities to access and process large volumes of geoscience data, which are often consumed by scientific workflows and processed in a distributed information environment. Consequently, data provenance becomes important since it allows users to determine the usability and reliability of data products. Motivation for capturing and sharing provenance also comes from the distributed data and information infrastructure that has been benefiting the Earth science community in the past decade, such as spatial data and information infrastructure, e-Science, and cyberinfrastructure. This paper provides an overview of geoscience data provenance in supporting provenance-aware geoscience data and information systems by summarizing state-of-the-art technologies and methodologies of geoscience data provenance and highlighting key considerations and possible solutions for geoscience data provenance. Numéro de notice : A2013-610 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2242478 En ligne : https://doi.org/10.1109/TGRS.2013.2242478 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32746
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 11 (November 2013) . - pp 5065 - 5072[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2013111 RAB Revue Centre de documentation En réserve L003 Disponible Permalink
