Academic literature on the topic 'Flux de données sémantiques'
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Journal articles on the topic "Flux de données sémantiques"
Bugnot, Marie-Ange. "Traduction des discours sur l’islam dans la presse de France et d’Espagne." Meta 57, no. 4 (December 17, 2013): 977–96. http://dx.doi.org/10.7202/1021228ar.
Full textFeltgen, Quentin, Georgeta Cislaru, and Christophe Benzitoun. "Étude linguistique et statistique des unités de performance écrite : le cas de et." SHS Web of Conferences 138 (2022): 10001. http://dx.doi.org/10.1051/shsconf/202213810001.
Full textHavelange, Isabelle. "La Bibliographie de l’histoire de France des lendemains de la Seconde Guerre mondiale à nos jours : le passage progressif de la collection papier au numérique." La Gazette des archives 262, no. 2 (2021): 37–44. http://dx.doi.org/10.3406/gazar.2021.6044.
Full textFontenelle, Thierry. "Towards the Construction of a Collocational Database for Translation Students." Meta 39, no. 1 (September 30, 2002): 47–56. http://dx.doi.org/10.7202/002756ar.
Full textKett, Jürgen. "Allemagne : le puzzle des réseaux sémantiques de données." Réseaux de coopération et bibliothèques, no. 102 (July 1, 2021): 14–15. http://dx.doi.org/10.35562/arabesques.2642.
Full textColazzo, Dario, François Goasdoué, Ionna Manolescu, and Alexandra Roatis. "Analyse de données RDF. Lentilles pour graphes sémantiques." Ingénierie des systèmes d'information 19, no. 4 (August 28, 2014): 87–117. http://dx.doi.org/10.3166/isi.19.4.87-117.
Full textBartikowski, Boris, Jean-Louis Chandon, and Heribert Gierl. "Calibration internationale des échelles sémantiques." Décisions Marketing N° 43-44, no. 3 (August 1, 2006): 207–19. http://dx.doi.org/10.3917/dm.043.0207.
Full textCasali, Alain, Rosine Ciccheti, and Lofti Lakhal. "Extraction de sémantiques dans les bases de données multidimensionnelles." Ingénierie des systèmes d'information 9, no. 1 (February 24, 2004): 35–59. http://dx.doi.org/10.3166/isi.9.1.35-59.
Full textL'Homme, Marie-Claude. "Méthode d'accès informatisé aux combinaisons lexicales en langue technique." Meta 42, no. 1 (September 30, 2002): 15–23. http://dx.doi.org/10.7202/003508ar.
Full textMbaiossum, Bery, Ladjel Bellastreche, Stéphanie Jean, and Mickael Baron. "Comparaison théorique et empirique de systèmes de bases de données sémantiques." Ingénierie des systèmes d'information 18, no. 3 (June 28, 2013): 39–63. http://dx.doi.org/10.3166/isi.18.3.39-63.
Full textDissertations / Theses on the topic "Flux de données sémantiques"
Chevalier, Jules. "Raisonnement incrémental sur des flux de données." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES008/document.
Full textIn this thesis, we propose an architecture for incremental reasoning on triple streams. To ensure scalability, it is composed of independent modules; thus allowing parallel reasoning. That is, several instances of a same rule can be simultaneously executed to enhance performance. We also focused our efforts to limit the duplicates spreading in the system, a recurrent issue for reasoning. To achieve this, we design a shared triplestore which allows each module to filter duplicates as soon as possible. The triples passes through the different independent modules of the architecture allows the reasoner to receive triple streams as input. Finally, our architecture is of agnostic nature regarding the fragment used for the inference. We also present three inference modes for our architecture: the first one infers all the implicit knowledge as fast as possible; the second mode should be used when the priority has to be defined for the inference of a specific type of knowledge; the third one proposes to maximize the amount of triples inferred per second. We implemented this architecture through Slider, an incremental reasoning natively supporting the fragments ρdf and RDFS: It can easily be extended to more complex fragments. Our experimentations show a 65% improvement over the reasoner OWLIM-SE. However, the recently published reasoner RDFox exhibits better performance, although this one does not provide prioritized inference. We also conducted experimentations showing that the use of incremental reasoning over batch-based reasoning offers systematically better performance for all the ontologies and fragments used
Belghaouti, Fethi. "Interopérabilité des systèmes distribués produisant des flux de données sémantiques au profit de l'aide à la prise de décision." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL003.
Full textInternet is an infinite source of data coming from sources such as social networks or sensors (home automation, smart city, autonomous vehicle, etc.). These heterogeneous and increasingly large data can be managed through semantic web technologies, which propose to homogenize, link these data and reason above them, and data flow management systems, which mainly address the problems related to volume, volatility and continuous querying. The alliance of these two disciplines has seen the growth of semantic data stream management systems also called RSP (RDF Stream Processing Systems). The objective of this thesis is to allow these systems, via new approaches and "low cost" algorithms, to remain operational, even more efficient, even for large input data volumes and/or with limited system resources.To reach this goal, our thesis is mainly focused on the issue of "Processing semantic data streamsin a context of computer systems with limited resources". It directly contributes to answer the following research questions : (i) How to represent semantic data stream ? And (ii) How to deal with input semantic data when their rates and/or volumes exceed the capabilities of the target system ?As first contribution, we propose an analysis of the data in the semantic data streams in order to consider a succession of star graphs instead of just a success of andependent triples, thus preserving the links between the triples. By using this approach, we significantly impoved the quality of responses of some well known sampling algoithms for load-shedding. The analysis of the continuous query allows the optimisation of this solution by selection the irrelevant data to be load-shedded first. In the second contribution, we propose an algorithm for detecting frequent RDF graph patterns in semantic data streams.We called it FreGraPaD for Frequent RDF Graph Patterns Detection. It is a one pass algorithm, memory oriented and "low-cost". It uses two main data structures : A bit-vector to build and identify the RDF graph pattern, providing thus memory space optimization ; and a hash-table for storing the patterns.The third contribution of our thesis consists of a deterministic load-shedding solution for RSP systems, called POL (Pattern Oriented Load-shedding for RDF Stream Processing systems). It uses very low-cost boolean operators, that we apply on the built binary patterns of the data and the continuous query inorder to determine which data is not relevant to be ejected upstream of the system. It guarantees a recall of 100%, reduces the system load and improves response time. Finally, in the fourth contribution, we propose Patorc (Pattern Oriented Compression for RSP systems). Patorc is an online compression toolfor RDF streams. It is based on the frequent patterns present in RDF data streams that factorizes. It is a data lossless compression solution whith very possible querying without any need to decompression.This thesis provides solutions that allow the extension of existing RSP systems and makes them able to scale in a bigdata context. Thus, these solutions allow the RSP systems to deal with one or more semantic data streams arriving at different speeds, without loosing their response quality while ensuring their availability, even beyond their physical limitations. The conducted experiments, supported by the obtained results show that the extension of existing systems with the new solutions improves their performance. They illustrate the considerable decrease in their engine’s response time, increasing their processing rate threshold while optimizing the use of their system resources
Belghaouti, Fethi. "Interopérabilité des systèmes distribués produisant des flux de données sémantiques au profit de l'aide à la prise de décision." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLL003.
Full textInternet is an infinite source of data coming from sources such as social networks or sensors (home automation, smart city, autonomous vehicle, etc.). These heterogeneous and increasingly large data can be managed through semantic web technologies, which propose to homogenize, link these data and reason above them, and data flow management systems, which mainly address the problems related to volume, volatility and continuous querying. The alliance of these two disciplines has seen the growth of semantic data stream management systems also called RSP (RDF Stream Processing Systems). The objective of this thesis is to allow these systems, via new approaches and "low cost" algorithms, to remain operational, even more efficient, even for large input data volumes and/or with limited system resources.To reach this goal, our thesis is mainly focused on the issue of "Processing semantic data streamsin a context of computer systems with limited resources". It directly contributes to answer the following research questions : (i) How to represent semantic data stream ? And (ii) How to deal with input semantic data when their rates and/or volumes exceed the capabilities of the target system ?As first contribution, we propose an analysis of the data in the semantic data streams in order to consider a succession of star graphs instead of just a success of andependent triples, thus preserving the links between the triples. By using this approach, we significantly impoved the quality of responses of some well known sampling algoithms for load-shedding. The analysis of the continuous query allows the optimisation of this solution by selection the irrelevant data to be load-shedded first. In the second contribution, we propose an algorithm for detecting frequent RDF graph patterns in semantic data streams.We called it FreGraPaD for Frequent RDF Graph Patterns Detection. It is a one pass algorithm, memory oriented and "low-cost". It uses two main data structures : A bit-vector to build and identify the RDF graph pattern, providing thus memory space optimization ; and a hash-table for storing the patterns.The third contribution of our thesis consists of a deterministic load-shedding solution for RSP systems, called POL (Pattern Oriented Load-shedding for RDF Stream Processing systems). It uses very low-cost boolean operators, that we apply on the built binary patterns of the data and the continuous query inorder to determine which data is not relevant to be ejected upstream of the system. It guarantees a recall of 100%, reduces the system load and improves response time. Finally, in the fourth contribution, we propose Patorc (Pattern Oriented Compression for RSP systems). Patorc is an online compression toolfor RDF streams. It is based on the frequent patterns present in RDF data streams that factorizes. It is a data lossless compression solution whith very possible querying without any need to decompression.This thesis provides solutions that allow the extension of existing RSP systems and makes them able to scale in a bigdata context. Thus, these solutions allow the RSP systems to deal with one or more semantic data streams arriving at different speeds, without loosing their response quality while ensuring their availability, even beyond their physical limitations. The conducted experiments, supported by the obtained results show that the extension of existing systems with the new solutions improves their performance. They illustrate the considerable decrease in their engine’s response time, increasing their processing rate threshold while optimizing the use of their system resources
Dia, Amadou Fall. "Filtrage sémantique et gestion distribuée de flux de données massives." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS495.
Full textOur daily use of the Internet and related technologies generates, at a rapid and variable speeds, large volumes of heterogeneous data issued from sensor networks, search engine logs, multimedia content sites, weather forecasting, geolocation, Internet of Things (IoT) applications, etc. Processing such data in conventional databases (Relational Database Management Systems) may be very expensive in terms of time and memory storage resources. To effectively respond to the needs of rapid decision-making, these streams require real-time processing. Data Stream Management Systems (SGFDs) evaluate queries on the recent data of a stream within structures called windows. The input data are different formats such as CSV, XML, RSS, or JSON. This heterogeneity lock comes from the nature of the data streams and must be resolved. For this, several research groups have benefited from the advantages of semantic web technologies (RDF and SPARQL) by proposing RDF data streams processing systems called RSPs. However, large volumes of RDF data, high input streams, concurrent queries, combination of RDF streams and large volumes of stored RDF data and expensive processing drastically reduce the performance of these systems. A new approach is required to considerably reduce the processing load of RDF data streams. In this thesis, we propose several complementary solutions to reduce the processing load in centralized environment. An on-the-fly RDF graphs streams sampling approach is proposed to reduce data and processing load while preserving semantic links. This approach is deepened by adopting a graph-oriented summary approach to extract the most relevant information from RDF graphs by using centrality measures issued from the Social Networks Analysis. We also adopt a compressed format of RDF data and propose an approach for querying compressed RDF data without decompression phase. To ensure parallel and distributed data streams management, the presented work also proposes two solutions for reducing the processing load in distributed environment. An engine and parallel processing approaches and distributed RDF graphs streams. Finally, an optimized processing approach for static and dynamic data combination operations is also integrated into a new distributed RDF graphs streams management system
Belaid, Nabil. "Modélisation de services et de workflows sémantiques à base d'ontologies de services et d'indexations." Phd thesis, Chasseneuil-du-Poitou, Ecole nationale supérieure de mécanique et d'aérotechnique, 2011. https://tel.archives-ouvertes.fr/tel-00605153.
Full textServices and workflows allow computer processing and information exchange. However, only information relevant to their computer management (storage, delivery, etc. ) is specified in the syntactic description languages such as WSDL, BPEL or XPDL. Indeed, these descriptions do not explicitly link the services and workflows to the implemented functions. To overcome these limitations, we propose an approach based on the definition of ontology of services (shared conceptualizations) and semantic indexations. Our proposal in ontology based databases to store and index the different services and workflows. The implementation of our approach is a prototype that enables to store, search, replace, reuse existing IT services and workflows and build new ones incrementally. This work is validated by being applied to the geological modeling field
Ren, Xiangnan. "Traitement et raisonnement distribués des flux RDF." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1139/document.
Full textReal-time processing of data streams emanating from sensors is becoming a common task in industrial scenarios. In an Internet of Things (IoT) context, data are emitted from heterogeneous stream sources, i.e., coming from different domains and data models. This requires that IoT applications efficiently handle data integration mechanisms. The processing of RDF data streams hence became an important research field. This trend enables a wide range of innovative applications where the real-time and reasoning aspects are pervasive. The key implementation goal of such application consists in efficiently handling massive incoming data streams and supporting advanced data analytics services like anomaly detection. However, a modern RSP engine has to address volume and velocity characteristics encountered in the Big Data era. In an on-going industrial project, we found out that a 24/7 available stream processing engine usually faces massive data volume, dynamically changing data structure and workload characteristics. These facts impact the engine's performance and reliability. To address these issues, we propose Strider, a hybrid adaptive distributed RDF Stream Processing engine that optimizes logical query plan according to the state of data streams. Strider has been designed to guarantee important industrial properties such as scalability, high availability, fault-tolerant, high throughput and acceptable latency. These guarantees are obtained by designing the engine's architecture with state-of-the-art Apache components such as Spark and Kafka. Moreover, an increasing number of processing jobs executed over RSP engines are requiring reasoning mechanisms. It usually comes at the cost of finding a trade-off between data throughput, latency and the computational cost of expressive inferences. Therefore, we extend Strider to support real-time RDFS+ (i.e., RDFS + owl:sameAs) reasoning capability. We combine Strider with a query rewriting approach for SPARQL that benefits from an intelligent encoding of knowledge base. The system is evaluated along different dimensions and over multiple datasets to emphasize its performance. Finally, we have stepped further to exploratory RDF stream reasoning with a fragment of Answer Set Programming. This part of our research work is mainly motivated by the fact that more and more streaming applications require more expressive and complex reasoning tasks. The main challenge is to cope with the large volume and high-velocity dimensions in a scalable and inference-enabled manner. Recent efforts in this area still missing the aspect of system scalability for stream reasoning. Thus, we aim to explore the ability of modern distributed computing frameworks to process highly expressive knowledge inference queries over Big Data streams. To do so, we consider queries expressed as a positive fragment of LARS (a temporal logic framework based on Answer Set Programming) and propose solutions to process such queries, based on the two main execution models adopted by major parallel and distributed execution frameworks: Bulk Synchronous Parallel (BSP) and Record-at-A-Time (RAT). We implement our solution named BigSR and conduct a series of evaluations. Our experiments show that BigSR achieves high throughput beyond million-triples per second using a rather small cluster of machines
De, Oliveira Joffrey. "Gestion de graphes de connaissances dans l'informatique en périphérie : gestion de flux, autonomie et adaptabilité." Electronic Thesis or Diss., Université Gustave Eiffel, 2023. http://www.theses.fr/2023UEFL2069.
Full textThe research work carried out as part of this PhD thesis lies at the interface between the Semantic Web, databases and edge computing. Indeed, our objective is to design, develop and evaluate a database management system (DBMS) based on the W3C Resource Description Framework (RDF) data model, which must be adapted to the terminals found in Edge computing.The possible applications of such a system are numerous and cover a wide range of sectors such as industry, finance and medicine, to name but a few. As proof of this, the subject of this thesis was defined with the team from the Computer Science and Artificial Intelligence Laboratory (CSAI) at ENGIE Lab CRIGEN. The latter is ENGIE's research and development centre dedicated to green gases (hydrogen, biogas and liquefied gases), new uses of energy in cities and buildings, industry and emerging technologies (digital and artificial intelligence, drones and robots, nanotechnologies and sensors). CSAI financed this thesis as part of a CIFRE-type collaboration.The functionalities of a system satisfying these characteristics must enable anomalies and exceptional situations to be detected in a relevant and effective way from measurements taken by sensors and/or actuators. In an industrial context, this could mean detecting excessively high measurements, for example of pressure or flow rate in a gas distribution network, which could potentially compromise infrastructure or even the safety of individuals. This detection must be carried out using a user-friendly approach to enable as many users as possible, including non-programmers, to describe risk situations. The approach must therefore be declarative, not procedural, and must be based on a query language, such as SPARQL.We believe that Semantic Web technologies can make a major contribution in this context. Indeed, the ability to infer implicit consequences from explicit data and knowledge is a means of creating new services that are distinguished by their ability to adjust to the circumstances encountered and to make autonomous decisions. This can be achieved by generating new queries in certain alarming situations, or by defining a minimal sub-graph of knowledge that an instance of our DBMS needs in order to respond to all of its queries.The design of such a DBMS must also take into account the inherent constraints of Edge computing, i.e. the limits in terms of computing capacity, storage, bandwidth and sometimes energy (when the terminal is powered by a solar panel or a battery). Architectural and technological choices must therefore be made to meet these limitations. With regard to the representation of data and knowledge, our design choice fell on succinct data structures (SDS), which offer, among other advantages, the fact that they are very compact and do not require decompression during querying. Similarly, it was necessary to integrate data flow management within our DBMS, for example with support for windowing in continuous SPARQL queries, and for the various services supported by our system. Finally, as anomaly detection is an area where knowledge can evolve, we have integrated support for modifications to the knowledge graphs stored on the client instances of our DBMS. This support translates into an extension of certain SDS structures used in our prototype
Giustozzi, Franco. "STEaMINg : semantic time evolving models for industry 4.0 Stream reasoning to improve decision-making in cognitive systems Smart condition monitoring for industry 4.0 manufacturing processes: an ontology-based approach." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMIR13.
Full textIn Industry 4.0, factory assets and machines are equipped with sensors that collect data for effective condition monitoring. This is a difficult task since it requires the integration and processing of heterogeneous data from different sources, with different temporal resolutions and underlying meanings. Ontologies have emerged as a pertinent method to deal with data integration and to represent manufacturing knowledge in a machine-interpretable way through the construction of semantic models. Moreover, the monitoring of industrial processes depends on the dynamic context of their execution. Under these circumstances, the semantic model must evolve in order to represent in which situation(s) a resource is in during the execution of its tasks to support decision making. This thesis studies the use of knowledge representation methods to build an evolving semantic model that represents the industrial domain, with an emphasis on context modeling to provide the notion of situation
Ait, Oubelli Lynda. "Transformations sémantiques pour l'évolution des modèles de données." Thesis, Toulouse, INPT, 2020. http://www.theses.fr/2020INPT0040.
Full textWhen developing a complex system, data models are the key to a successful engineering process because they contain and organize all the information manipulated by the different functions involved in system design. The fact that the data models evolve throughout the design raises problems of maintenance of the data already produced. Our work addresses the issue of evolving data models in a model-driven engineering environment (IDM). We focus on minimizing the impact of the evolution of the data model on the system development process in the specific area of space engineering. In the space industry, model-driven engineering (MDI) is a key area for modeling data exchange with satellites. When preparing a space mission, the associated data models are often updated and must be compared from one version to another. Thus, because of the growth of the changes, it becomes difficult to follow them. New methods and techniques to understand and represent the differences and commonalities between different versions of the model are essential. Recent research deals with the evolution process between the two architectural layers (M2 / M1) of the IDM. In this thesis, we have explored the use of the (M1 / M0) layers of the same architecture to define a set of complex operators and their composition that encapsulate both the evolution of the data model and the data migration. The use of these operators improves the quality of results when migrating data, ensuring the complete preservation of the information contained in the data. In the first part of this thesis, we focused on how to deal with structural differences during the evolution process. The proposed approach is based on the detection of differences and the construction of evolution operators. Then, we studied the performance of the model-based approach (MBD) on two space missions, named PHARAO and MICROSCOPE. Then, we presented a semantic observational approach to deal with the evolution of data models at M1 level. The main interest of the proposed approach is the transposition of the problem of accessibility of the information in a data model, into a problem of path in a labeled directed graph. The approach proved to be able to capture all the evolutions of a data model in a logical operator list instead of a non-exhaustive list of evolution operators. It is generic because, regardless of the type of input data model, if the data model is correctly interpreted to ldg and then project it onto a set of lts, we can check the conservation of the information
Chiky, Raja. "Résumé de flux de données ditribués." Phd thesis, Télécom ParisTech, 2009. http://pastel.archives-ouvertes.fr/pastel-00005137.
Full textBooks on the topic "Flux de données sémantiques"
Sid-Ahmed, Abdelkader. Les flux d'échanges en Méditerranée: Données, fondements historiques et perspectives. [France]: Edisud, 1996.
Find full textFrance. Secrétariat général. Direction de la documentation. Flux Transfrontière de Données: Les Problèmes Qu'ils Soulèvent : le Bilan de Leur Utilisation. S.l: s.n, 1985.
Find full textBenyekhlef, Karim. La protection de la vie privée dans les échanges internationaux d'informations. Montréal, Qué: Éditions Thémis, 1992.
Find full textJörg, Becker, and Szecskö Tamás 1933-, eds. Europe speaks to Europe: International information flows between east and west Europe. Oxford: Published for KomTech by Pergamon, 1989.
Find full textEurope, Council of, ed. Additional Protocol to the Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data, regarding supervisory authorities and transborder data flows =: Protocole additionnel à la convention pour la protection des personnes à l'égard du traitement automatisé des données à caractère personnel, concernant les autorités de contrôle et les flux transfrontières de données. Strasbourg: Council of Europe, 2002.
Find full textIntegration of services into workflow applications. Boca Raton: CRC Press/Taylor & Francis Group, 2015.
Find full textB, Lam Richard, ed. Programming workflow applications with Domino. Lawrence, KS: R&D Books, 2000.
Find full textVie privée sans frontières: Les flux transfrontières de renseignements personnels en provenance du Canada. Ottawa, Ont: Direction des communications et affaires publiques, Ministère de la justice du Canada, 1991.
Find full textLignes directrices de l'OCDE sur la protection de la vie privée et les flux transfrontières de données de caractère personnel. Éditions OCDE, 2002. http://dx.doi.org/10.1787/9789264296398-fr.
Full textData Sharing: Recent Progress and Remaining Challenges. Nova Science Publishers, Incorporated, 2019.
Find full textBook chapters on the topic "Flux de données sémantiques"
Lafourcade, Mathieu, and Nathalie Lebrun. "Vers un réseau lexico-sémantique de grande taille obtenu par crowdsourcing." In Le Crowdsourcing, 75–94. Editions des archives contemporaines, 2021. http://dx.doi.org/10.17184/eac.3913.
Full textPajou, Jean-Charles. "Flux de données entre éditeurs et bibliothèques : le format ONIX." In Vers de nouveaux catalogues, 115. Éditions du Cercle de la Librairie, 2016. http://dx.doi.org/10.3917/elec.berme.2016.01.0115.
Full text"Examen de la littérature consacrée aux flux transfrontières de données." In Rapport sur l'économie numérique, 53–68. United Nations, 2022. http://dx.doi.org/10.18356/9789210058261c008.
Full textJARLAN, Lionel, Bertrand BONAN, Jean-Christophe CALVET, Patricia DE ROSNAY, Catherine OTTLÉ, and Philippe PEYLIN. "Assimilation de données de télédétection pour le suivi des surfaces continentales." In Inversion et assimilation de données de télédétection, 45–95. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9142.ch2.
Full textSackur, Jérôme. "Chapitre 4. Quelles données subjectives pour l’étude du flux de conscience ?" In Les signatures neurobiologiques de la conscience, 85–106. EDP Sciences, 2021. http://dx.doi.org/10.1051/978-2-7598-2612-4.c007.
Full text"État des lieux des politiques nationales relatives aux flux transfrontières de données." In Rapport sur l'économie numérique, 133–60. United Nations, 2022. http://dx.doi.org/10.18356/9789210058261c011.
Full text"Approches régionales et internationales de la réglementation des flux transfrontières de données." In Rapport sur l'économie numérique, 161–91. United Nations, 2022. http://dx.doi.org/10.18356/9789210058261c012.
Full textGUERAUD-PINET, Guylaine. "Analyser la musique dans les programmes de flux de la télévision française (1949-2016)." In Corpus audiovisuels, 41–54. Editions des archives contemporaines, 2022. http://dx.doi.org/10.17184/eac.5699.
Full textISOARD, Gilbert. "Algorithmes et biais cognitifs." In Algorithmes et Société, 89–100. Editions des archives contemporaines, 2021. http://dx.doi.org/10.17184/eac.4556.
Full textDIOUF, François Singue, Meissa Birima FALL, and Sow MOUHAMADOU. "Site touristique et évolution spatiotemporelle d’un milieu géographique. Exemple de la station balnéaire de Saly Portudal au Sénégal." In Revue Internationale des Sciences Économiques et Sociales (RISES) No. 4, 17–30. Editions des archives contemporaines, 2024. http://dx.doi.org/10.17184/eac.8164.
Full textConference papers on the topic "Flux de données sémantiques"
CARTIER, Adrien, and Arnaud HEQUETTE. "Evaluation des flux sédimentaires sur les plages macrotidales du Nord-Pas-de-Calais à partir de données hydrodynamiques et de piégeages in situ." In Journées Nationales Génie Côtier - Génie Civil. Editions Paralia, 2010. http://dx.doi.org/10.5150/jngcgc.2010.023-c.
Full textVérézubova, Ekatérina. "Le champ lexical de l’eau et son imaginaire dans les cultures française et russe (étude comparative)." In XXV Coloquio AFUE. Palabras e imaginarios del agua. Valencia: Universitat Politècnica València, 2016. http://dx.doi.org/10.4995/xxvcoloquioafue.2016.3792.
Full textBaranes, M., and T. Fortin. "Planification et chirurgie guidée - Avis d’experts : Apports des nouvelles technologies en implantologie : de la planification à la réalisation de la prothèse provisoire immédiate." In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206601011.
Full textReports on the topic "Flux de données sémantiques"
Warin, Thierry, Nathalie de Marcellis-Warin, Sarah Elimam, Molivann Panot, and Jéremy Schneider. La diplomatie à l’heure de la science des données : réflexions stratégiques et perspectives. CIRANO, June 2023. http://dx.doi.org/10.54932/jrbv7364.
Full text