Auswahl der wissenschaftlichen Literatur zum Thema „Données massives – Analyse informatique“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Inhaltsverzeichnis
Machen Sie sich mit den Listen der aktuellen Artikel, Bücher, Dissertationen, Berichten und anderer wissenschaftlichen Quellen zum Thema "Données massives – Analyse informatique" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Zeitschriftenartikel zum Thema "Données massives – Analyse informatique"
ASSIS, Y., A. NAFI, X. NI, A. SAMET und G. GUARINO. „Analyse textuelle des RPQS pour la constitution de bases de connaissances“. 3, Nr. 3 (22.03.2021): 31–36. http://dx.doi.org/10.36904/tsm/202103031.
Der volle Inhalt der QuelleCORPET, F., und C. CHEVALET. „Analyse informatique des données moléculaires“. INRAE Productions Animales 13, HS (22.12.2000): 191–95. http://dx.doi.org/10.20870/productions-animales.2000.13.hs.3837.
Der volle Inhalt der QuelleMothe, Caroline, Estelle Delfosse und Anne Marie Bocquet. „L’analyse de données textuelles assistée par ordinateur“. Revue Française de Gestion 47, Nr. 295 (März 2021): 11–37. http://dx.doi.org/10.3166/rfg.2021.00525.
Der volle Inhalt der QuelleSondi, Patrick. „Enseignement des modules Architecture-Systèmes-Réseaux en Licence Informatique à l’ère des objets connectés: plébiscite de l’apprentissage par problème ?“ J3eA 21 (2022): 2027. http://dx.doi.org/10.1051/j3ea/20222027.
Der volle Inhalt der QuelleDagher, Georges, Maria Luisa Lavitrano und Paul Hofman. „Le next-generation biobanking“. médecine/sciences 34, Nr. 10 (Oktober 2018): 849–51. http://dx.doi.org/10.1051/medsci/2018203.
Der volle Inhalt der QuelleGaultier, M. „Une base de données en anthropologie adaptée pour l'archéologie préventive. Usages, enjeux et limites au service de l'archéologie du département d'Indre-et-Loire (Sadil)“. Bulletins et Mémoires de la Société d'Anthropologie de Paris 29, Nr. 3-4 (17.03.2017): 159–64. http://dx.doi.org/10.1007/s13219-017-0179-8.
Der volle Inhalt der QuelleASTRUC, A., A. JOUANNIN, E. LOOTVOET, T. BONNET und F. CHEVALLIER. „LES DONNEES A CARACTERE PERSONNEL : QUELLES FORMALITES REGLEMENTAIRES POUR LES TRAVAUX DE RECHERCHE EN MEDECINE GENERALE ?“ EXERCER 32, Nr. 172 (01.04.2021): 178–84. http://dx.doi.org/10.56746/exercer.2021.172.178.
Der volle Inhalt der QuellePierrel, Jean-Marie. „Un ensemble de ressources de référence pour l’étude du français : tlfi, frantext et le logiciel stella“. Revue québécoise de linguistique 32, Nr. 1 (20.02.2006): 155–76. http://dx.doi.org/10.7202/012248ar.
Der volle Inhalt der QuelleNyandue Ompola, José. „La cartographie numérique et son apport dans l’organisation du recensement en République Démocratique du Congo“. Revue Congolaise des Sciences & Technologies 01, Nr. 02 (20.11.2022): 110–18. http://dx.doi.org/10.59228/rcst.022.v1.i2.14.
Der volle Inhalt der QuelleAndry, Tiffany, Julia Bonaccorsi, Gilles Gesquière, Arnaud Grignard und Thierry Joliveau. „À quoi rêvent les maquettes ? Maquette augmentée et médiation urbaine, un défi pluridisciplinaire“. SHS Web of Conferences 147 (2022): 02004. http://dx.doi.org/10.1051/shsconf/202214702004.
Der volle Inhalt der QuelleDissertationen zum Thema "Données massives – Analyse informatique"
Haddad, Raja. „Apprentissage supervisé de données symboliques et l'adaptation aux données massives et distribuées“. Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLED028/document.
Der volle Inhalt der QuelleThis Thesis proposes new supervised methods for Symbolic Data Analysis (SDA) and extends this domain to Big Data. We start by creating a supervised method called HistSyr that converts automatically continuous variables to the most discriminant histograms for classes of individuals. We also propose a new method of symbolic decision trees that we call SyrTree. SyrTree accepts many types of inputs and target variables and can use all symbolic variables describing the target to construct the decision tree. Finally, we extend HistSyr to Big Data, by creating a distributed method called CloudHistSyr. Using the Map/Reduce framework, CloudHistSyr creates of the most discriminant histograms for data too big for HistSyr. We tested CloudHistSyr on Amazon Web Services. We show the efficiency of our method on simulated data and on actual car traffic data in Nantes. We conclude on overall utility of CloudHistSyr which, through its results, allows the study of massive data using existing symbolic analysis methods
Adjout, Rehab Moufida. „Big Data : le nouvel enjeu de l'apprentissage à partir des données massives“. Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCD052.
Der volle Inhalt der QuelleIn recent years we have witnessed a tremendous growth in the volume of data generatedpartly due to the continuous development of information technologies. Managing theseamounts of data requires fundamental changes in the architecture of data managementsystems in order to adapt to large and complex data. Single-based machines have notthe required capacity to process such massive data which motivates the need for scalablesolutions.This thesis focuses on building scalable data management systems for treating largeamounts of data. Our objective is to study the scalability of supervised machine learningmethods in large-scale scenarios. In fact, in most of existing algorithms and datastructures,there is a trade-off between efficiency, complexity, scalability. To addressthese issues, we explore recent techniques for distributed learning in order to overcomethe limitations of current learning algorithms.Our contribution consists of two new machine learning approaches for large scale data.The first contribution tackles the problem of scalability of Multiple Linear Regressionin distributed environments, which permits to learn quickly from massive volumes ofexisting data using parallel computing and a divide and-conquer approach to providethe same coefficients like the classic approach.The second contribution introduces a new scalable approach for ensembles of modelswhich allows both learning and pruning be deployed in a distributed environment.Both approaches have been evaluated on a variety of datasets for regression rangingfrom some thousands to several millions of examples. The experimental results showthat the proposed approaches are competitive in terms of predictive performance while reducing significantly the time of training and prediction
Ledieu, Thibault. „Analyse et visualisation de trajectoires de soins par l’exploitation de données massives hospitalières pour la pharmacovigilance“. Thesis, Rennes 1, 2018. http://www.theses.fr/2018REN1B032/document.
Der volle Inhalt der QuelleThe massification of health data is an opportunity to answer questions about vigilance and quality of care. The emergence of big data in health is an opportunity to answer questions about vigilance and quality of care. In this thesis work, we will present approaches to exploit the diversity and volume of intra-hospital data for pharmacovigilance use and monitoring the proper use of drugs. This approach will be based on the modelling of intra-hospital care trajectories adapted to the specific needs of pharmacovigilance. Using data from a hospital warehouse, it will be necessary to characterize events of interest and identify a link between the administration of these health products and the occurrence of adverse reactions, or to look for cases of misuse of the drug. The hypothesis put forward in this thesis is that an interactive visual approach would be suitable for the exploitation of these heterogeneous and multi-domain biomedical data in the field of pharmacovigilance. We have developed two prototypes allowing the visualization and analysis of care trajectories. The first prototype is a tool for visualizing the patient file in the form of a timeline. The second application is a tool for visualizing and searching a cohort of event sequences The latter tool is based on the implementation of sequence analysis algorithms (Smith-Waterman, Apriori, GSP) for the search for similarity or patterns of recurring events. These human-machine interfaces have been the subject of usability studies on use cases from actual practice that have proven their potential for routine use
El, Ouazzani Saïd. „Analyse des politiques publiques en matière d’adoption du cloud computing et du big data : une approche comparative des modèles français et marocain“. Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLE009/document.
Der volle Inhalt der QuelleOur research concerns the public policy analysis on how Cloud Computing and Big data are adopted by French and Moroccan States with a comparative approach between the two models. We have covered these main areas: The impact of the digital on the organization of States and Government ; The digital Public Policy in both France and Morocco countries ;The concept related to the data protection, data privacy ; The limits between security, in particular home security, and the civil liberties ; The future and the governance of the Internet ; A use case on how the Cloud could change the daily work of a public administration ; Our research aims to analyze how the public sector could be impacted by the current digital (re) evolution and how the States could be changed by emerging a new model in digital area called Cyber-State. This term is a new concept and is a new representation of the State in the cyberspace. We tried to analyze the digital transformation by looking on how the public authorities treat the new economics, security and social issues and challenges based on the Cloud Computing and Big Data as the key elements on the digital transformation. We tried also to understand how the States – France and Morocco - face the new security challenges and how they fight against the terrorism, in particular, in the cyberspace. We studied the recent adoption of new laws and legislation that aim to regulate the digital activities. We analyzed the limits between security risks and civil liberties in context of terrorism attacks. We analyzed the concepts related to the data privacy and the data protection. Finally, we focused also on the future of the internet and the impacts on the as is internet architecture and the challenges to keep it free and available as is the case today
Belghache, Elhadi. „AMAS4BigData : analyse dynamique de grandes masses de données par systèmes multi-agents adaptatifs“. Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30149.
Der volle Inhalt der QuelleUnderstanding data is the main purpose of data science and how to achieve it is one of the challenges of data science, especially when dealing with big data. The big data era brought us new data processing and data management challenges to face. Existing state-of-the-art analytics tools come now close to handle ongoing challenges and provide satisfactory results with reasonable cost. But the speed at which new data is generated and the need to manage changes in data both for content and structure lead to new rising challenges. This is especially true in the context of complex systems with strong dynamics, as in for instance large scale ambient systems. One existing technology that has been shown as particularly relevant for modeling, simulating and solving problems in complex systems are Multi-Agent Systems. The AMAS (Adaptive Multi-Agent Systems) theory proposes to solve complex problems for which there is no known algorithmic solution by self-organization. The cooperative behavior of the agents enables the system to self-adapt to a dynamical environment so as to maintain the system in a functionality adequate state. In this thesis, we apply this theory to Big Data Analytics. In order to find meaning and relevant information drowned in the data flood, while overcoming big data challenges, a novel analytic tool is needed, able to continuously find relations between data, evaluate them and detect their changes and evolution over time. The aim of this thesis is to present the AMAS4BigData analytics framework based on the Adaptive Multi-agent systems technology, which uses a new data similarity metric, the Dynamics Correlation, for dynamic data relations discovery and dynamic display. This framework is currently being applied in the neOCampus operation, the ambient campus of the University Toulouse III - Paul Sabatier
Cantu, Alma. „Proposition de modes de visualisation et d'interaction innovants pour les grandes masses de données et/ou les données structurées complexes en prenant en compte les limitations perceptives des utilisateurs“. Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0068/document.
Der volle Inhalt der QuelleAs a result of the improvement of data capture and storage, recent years have seen the amount of data to be processed increase dramatically. Many studies, ranging from automatic processing to information visualization, have been performed, but some areas are still too specific to take advantage of. This is the case of ELectromagnetic INTelligence(ELINT). This domain does not only deal with a huge amount of data but also has to handle complex data and usage as well as populations of users with less and less experience. In this thesis we focus on the use of existing and new technologies applied to visualization to propose solutions to the combination of issues such as huge amount and complex data. We begin by presenting an analysis of the ELINT field which made it possible to extract the issues that it must faces. Then, we focus on the visual solutions handling the combinations of such issues but the existing work do not contain directly such solutions. Therefore, we focus on the description of visual issues and propose a characterization of these issues. This characterization allows us to describe the existing representations and to build a recommendation tool based on how the existing work solves the issues. Finally, we focus on identifying new metaphors to complete the existing work and propose an immersive representation to solve the issues of ELINT. These contributions make it possible to analyze and use the existing and deepen the use of immersive representations for the visualization of information
Soler, Maxime. „Réduction et comparaison de structures d'intérêt dans des jeux de données massifs par analyse topologique“. Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS364.
Der volle Inhalt der QuelleIn this thesis, we propose different methods, based on topological data analysis, in order to address modern problematics concerning the increasing difficulty in the analysis of scientific data. In the case of scalar data defined on geometrical domains, extracting meaningful knowledge from static data, then time-varying data, then ensembles of time-varying data proves increasingly challenging. Our approaches for the reduction and analysis of such data are based on the idea of defining structures of interest in scalar fields as topological features. In a first effort to address data volume growth, we propose a new lossy compression scheme which offers strong topological guarantees, allowing topological features to be preserved throughout compression. The approach is shown to yield high compression factors in practice. Extensions are proposed to offer additional control over the geometrical error. We then target time-varying data by designing a new method for tracking topological features over time, based on topological metrics. We extend the metrics in order to overcome robustness and performance limitations. We propose a new efficient way to compute them, gaining orders of magnitude speedups over state-of-the-art approaches. Finally, we apply and adapt our methods to ensemble data related to reservoir simulation, for modeling viscous fingering in porous media. We show how to capture viscous fingers with topological features, adapt topological metrics for capturing discrepancies between simulation runs and a ground truth, evaluate the proposed metrics with feedback from experts, then implement an in-situ ranking framework for rating the fidelity of simulation runs
Liu, Rutian. „Semantic services for assisting users to augment data in the context of analytic data sources“. Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS208.
Der volle Inhalt der QuelleThe production of analytic datasets is a significant big data trend and has gone well beyond the scope of traditional IT-governed dataset development. Analytic datasets are now created by data scientists and data analysts using bigdata frameworks and agile data preparation tools. However, it still remains difficult for a data analyst to start from a dataset at hand and customize it with additional attributes coming from other existing datasets. This thesis presents a new solution for business users and data scientists who want to augment the schema of analytic datasets with attributes coming from other semantically related datasets : We introduce attribute graphs as a novel concise and natural way to represent literal functional dependencies over hierarchical dimension level types to infer unique dimension and fact table identifiers We give formal definitions for schema augmentation, schema complement, and merge query in the context of analytic tables. We then introduce several reduction operations to enforce schema complements when schema augmentation yields a row multiplication in the augmented dataset. We define formal quality criteria and algorithms to control the correctness, non-ambiguity, and completeness of generated schema augmentations and schema complements. We describe the implementation of our solution as a REST service within the SAP HANA platform and provide a detailed description of our algorithms. We evaluate the performance of our algorithms to compute unique identifiers in dimension and fact tables and analyze the effectiveness of our REST service using two application scenarios
Baudin, Alexis. „Cliques statiques et temporelles : algorithmes d'énumération et de détection de communautés“. Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS609.
Der volle Inhalt der QuelleGraphs are mathematical objects used to model interactions or connections between entities of various types. A graph can represent, for example, a social network that connects users to each other, a transport network like the metro where stations are connected to each other, or a brain with the billions of interacting neurons it contains. In recent years, the dynamic nature of these structures has been highlighted, as well as the importance of taking into account the temporal evolution of these networks to understand their functioning. While many concepts and algorithms have been developed on graphs to describe static network structures, much remains to be done to formalize and develop relevant algorithms to describe the dynamics of real networks. This thesis aims to better understand how massive graphs are structured in the real world, and to develop tools to extend our understanding to structures that evolve over time. It has been shown that these graphs have particular properties, which distinguish them from theoretical or randomly drawn graphs. Exploiting these properties then enables the design of algorithms to solve certain difficult problems much more quickly on these instances than in the general case. My PhD thesis focuses on cliques, which are groups of elements that are all connected to each other. We study the enumeration of cliques in static and temporal graphs and the detection of communities they enable. The communities of a graph are sets of vertices such that, within a community, the vertices interact strongly with each other, and little with the rest of the graph. Their study helps to understand the structural and functional properties of networks. We are evaluating our algorithms on massive real-world graphs, opening up new perspectives for understanding interactions within these networks. We first work on graphs, without taking into account the temporal component of interactions. We begin by using the clique percolation method of community detection, highlighting its limitations in memory, which prevent it from being applied to graphs that are too massive. By introducing an approximate problem-solving algorithm, we overcome this limitation. Next, we improve the enumeration of maximal cliques in the case of bipartite graphs. These correspond to interactions between groups of vertices of different types, e.g. links between people and viewed content, participation in events, etc. Next, we consider interactions that take place over time, using the link stream formalism. We seek to extend the algorithms presented in the first part, to exploit their advantages in the study of temporal interactions. We provide a new algorithm for enumerating maximal cliques in link streams, which is much more efficient than the state-of-the-art on massive datasets. Finally, we focus on communities in link streams by clique percolation, developing an extension of the method used on graphs. The results show a significant improvement over the state of the art, and we analyze the communities obtained to provide relevant information on the organization of temporal interactions in link streams. My PhD work has provided new insights into the study of massive real-world networks. This shows the importance of exploring the potential of graphs in a real-world context, and could contribute to the emergence of innovative solutions for the complex challenges of our modern society
Larroche, Corentin. „Network-wide intrusion detection through statistical analysis of event logs : an interaction-centric approach“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT041.
Der volle Inhalt der QuelleEvent logs are structured records of all kinds of activities taking place in a computer network. In particular, malicious actions taken by intruders are likely to leave a trace in the logs, making this data source useful for security monitoring and intrusion detection. However, the considerable volume of real-world event logs makes them difficult to analyze. This limitation has motivated a fair amount of research on malicious behavior detection through statistical methods. This thesis addresses some of the challenges that currently hinder the use of this approach in realistic settings. First of all, building an abstract representation of the data is nontrivial: event logs are complex and multi-faceted, making it difficult to capture all the relevant information they contain in a simple mathematical object. We take an interaction-centric approach to event log representation, motivated by the intuition that malicious events can often be seen as unexpected interactions between entities (users, hosts, etc.). While this representation preserves critical information, it also makes statistical modelling difficult. We thus build an ad hoc model and design a suitable inference procedure, using elements of latent space modelling, Bayesian filtering and multi-task learning.Another key challenge in event log analysis is that benign events account for a vast majority of the data, including a lot of unusual albeit legitimate events. Detecting individually anomalous events is thus not enough, and we also deal with spotting clusters of potentially malicious events. To that end, we leverage the concept of event graph and recast event-wise anomaly scores as a noisy graph-structured signal. This allows us to use graph signal processing tools to improve anomaly scores provided by statistical models.Finally, we propose scalable methods for anomalous cluster detection in node-valued signals defined over large graphs
Bücher zum Thema "Données massives – Analyse informatique"
Herman, Jacques. Analyse de données qualitatives. Paris: Masson, 1986.
Den vollen Inhalt der Quelle findenJambu, Michel. Introduction au data mining: Analyse intelligente des données. Paris: Eyrolles, 1999.
Den vollen Inhalt der Quelle findenGross, Ju rgen. Grundlegende Statistik mit R: Eine anwendungsorientierte Einfu hrung in die Verwendung der Statistik Software R. Wiesbaden: Teubner, 2010.
Den vollen Inhalt der Quelle findenA, Accomazzi, Heck A und Murtagh Fionn, Hrsg. Knowledge-based systems in astronomy: A topical volume with contributions by A. Accomazzi ... [et al.]. Berlin: Springer-Verlag, 1989.
Den vollen Inhalt der Quelle findenDas, Swagatam. Metaheuristic clustering. Berlin: Springer, 2009.
Den vollen Inhalt der Quelle findenArson, Benoît. Web analytics: Méthode pour l'analyse Web. Paris: Pearson, 2012.
Den vollen Inhalt der Quelle findenCoad, Peter. Object-oriented design. Englewood Cliffs, N.J: Yourdon Press, 1991.
Den vollen Inhalt der Quelle findenCoad, Peter. Object oriented design. Hemel Hempstead: Prentice-Hall, 1991.
Den vollen Inhalt der Quelle findenMcCumber, John. Assessing and managing security risk in IT systems: A structured methodology. Boca Raton, FL: Auerbach Publications, 2005.
Den vollen Inhalt der Quelle findenYourdon, Edward. Object-oriented systems design: An integrated approach. Englewood Cliffs, NJ: Prentice-Hall International, 1994.
Den vollen Inhalt der Quelle finden