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Artykuły w czasopismach na temat "Popularité des données"
Minon, Sophie. "De Babylone à l’Occident méditerranéen : le nom d’homme hellénisé sous la forme Ζώπυρος". Cahiers du Centre de Linguistique et des Sciences du Langage, nr 60 (24.02.2020): 225–38. http://dx.doi.org/10.26034/la.cdclsl.2020.197.
Pełny tekst źródłaLajeunesse1, Marcel. "Le livre religieux au Québec, 1968-2007 : analyse des données de l’édition". Études d’histoire religieuse 76 (20.10.2010): 27–42. http://dx.doi.org/10.7202/044758ar.
Pełny tekst źródłaBERNARD, Paul. "Stratification sociométrique et réseaux sociaux". Sociologie et sociétés 5, nr 1 (30.09.2002): 127–50. http://dx.doi.org/10.7202/001048ar.
Pełny tekst źródłaBlais, André, Neil Nevitte, Elisabeth Gidengil, Henry Brady i Richard Johnston. "L’élection fédérale de 1993 : le comportement électoral des Québécois". Revue québécoise de science politique, nr 27 (5.12.2008): 15–49. http://dx.doi.org/10.7202/040369ar.
Pełny tekst źródłaLapierre-Adamcyk, Évelyne. "Les aspirations des québécois en matière de fécondité en 1980". Cahiers québécois de démographie 10, nr 2 (27.10.2008): 171–88. http://dx.doi.org/10.7202/600849ar.
Pełny tekst źródłaHaldimann, Lucas, Marieke Heers i Patrick Rérat. "Jeunesse (non) mobile? Les facteurs influençant la mobilité temporaire des jeunes adultes suisses". Géo-Regards 13, nr 1 (2020): 103–30. http://dx.doi.org/10.33055/georegards.2020.013.01.103.
Pełny tekst źródłaDesiateryk, Sofiia, Minh T. Do, Sarah Zutrauen, Ze Wang, Ithayavani Iynkkaran, Lina Ghandour, Steven R. McFaull, Greg Butler, James Cheesman i Andre Champagne. "Caractéristiques des traumatismes causés par l’utilisation à l’extérieur d’une trottinette motorisée : analyse des données de la plate-forme électronique du Système canadien hospitalier d’information et de recherche en prévention des traumatismes (eSCHIRPT)". Promotion de la santé et prévention des maladies chroniques au Canada 42, nr 10 (październik 2022): 507–12. http://dx.doi.org/10.24095/hpcdp.42.10.05f.
Pełny tekst źródłaKennepohl, Stephan. "VALIDITÉ ET CRÉDIBILITÉ DANS LE CONTEXTE DE L’EXPERTISE EN NEUROPSYCHOLOGIE CLINIQUE". Revue québécoise de psychologie 39, nr 3 (21.03.2019): 51–74. http://dx.doi.org/10.7202/1058184ar.
Pełny tekst źródłaLemay, Isabelle, i Daniel Tremblay. "Les mesures financières hors normes – La profession comptable pourrait bien détenir la solution". Revue Organisations & territoires 26, nr 1-2 (1.09.2017): 205–23. http://dx.doi.org/10.1522/revueot.v26i1-2.209.
Pełny tekst źródłaMeyer, Jean-Christophe. "«Uns Uwe», héros sportif médiatique sans hybris de la RFA". Revue d’Allemagne et des pays de langue allemande 44, nr 4 (2012): 455–68. http://dx.doi.org/10.3406/reval.2012.6254.
Pełny tekst źródłaRozprawy doktorskie na temat "Popularité des données"
Rakoczy, Monika. "Exploring human interactions for influence modeling in online social networks". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL010/document.
Pełny tekst źródłaOnline social networks are constantly growing in popularity. They enable users to interact with one another and shifting their relations to the virtual world. Users utilize social media platforms as a mean for a rich variety of activities. Indeed, users are able to express their opinions, share experiences, react to other users' views and exchange ideas. Such online human interactions take place within a dynamic hierarchy where we can observe and distinguish many qualities related to relations between users, concerning influential, trusted or popular individuals. In particular, influence within Social Networks (SN) has been a recent focus in the literature. Many domains, such as recommender systems or Social Network Analysis (SNA), measure and exploit users’ influence. Therefore, models discovering and estimating influence are important for current research and are useful in various disciplines, such as marketing, political and social campaigns, recommendations and others. Interestingly, interactions between users can not only indicate influence but also involve trust, popularity or reputation of users. However, all these notions are still vaguely defined and not meeting the consensus in the SNA community. Defining, distinguishing and measuring the strength of those relations between the users are also posing numerous challenges, on theoretical and practical ground, and are yet to be explored. Modelization of influence poses multiple challenges. In particular, current state-of-the-art methods of influence discovery and evaluation still do not fully explore users’ actions of various types, and are not adaptive enough for using different SN. Furthermore, adopting the time aspect into influence model is important, challenging and in need of further examination part of the research. Finally, exploring possible connections and links between coinciding notions, like influence and reputation, remains to be performed.In this thesis, we focus on the qualities of users connected to four important concepts: influence, reputation, trust, and popularity, in the scope of SNA for influence modeling. We analyze existing works utilizing these notions and we compare and contrast their interpretations. Consequently, we emphasize the most important features that these concepts should include and we make a comparative analysis of them. Accordingly, we present a global classification of the notions concerning their abstract level and distinction of the terms from one another, which is a first and required contribution of the thesis. Consequently, we then propose a theoretical model of influence and present influence-related ontology. We also present a distinction of notion not yet explored in SNA discipline -- micro-influence, which targets new phenomena of users with a small but highly involved audience, who are observed to be still highly impactful. Basing on the definitions of the concepts, we propose a practical model, called Action-Reaction Influence Model (ARIM). This model considers type, quality, quantity, and frequency of actions performed by users in SN, and is adaptive to different SN types. We also focus on the quantification of influence over time and representation of influence causal effect. In order to do that, we focus on a particular SN with a specific characteristic - citation network. Indeed, citation networks are particularly time sensitive. Accordingly, we propose Time Dependent Influence Estimation (TiDIE), a model for determining influence during a particular time period between communities within time-dependent citation networks. Finally, we also combine two of the abovementioned notions, influence and reputation, in order to investigate the dependencies between them. In particular, we propose a transition method, ReTiDIE, that uses influence for predicting the reputation. For each of the proposed approaches, experiments have been conducted on real-world datasets and demonstrate the suitability of the methods
Chuchuk, Olga. "Optimisation de l'accès aux données au CERN et dans la Grille de calcul mondiale pour le LHC (WLCG)". Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4005.
Pełny tekst źródłaThe Worldwide LHC Computing Grid (WLCG) offers an extensive distributed computing infrastructure dedicated to the scientific community involved with CERN's Large Hadron Collider (LHC). With storage that totals roughly an exabyte, the WLCG addresses the data processing and storage requirements of thousands of international scientists. As the High-Luminosity LHC phase approaches, the volume of data to be analysed will increase steeply, outpacing the expected gain through the advancement of storage technology. Therefore, new approaches to effective data access and management, such as caches, become essential. This thesis delves into a comprehensive exploration of storage access within the WLCG, aiming to enhance the aggregate science throughput while limiting the cost. Central to this research is the analysis of real file access logs sourced from the WLCG monitoring system, highlighting genuine usage patterns.In a scientific setting, caching has profound implications. Unlike more commercial applications such as video streaming, scientific data caches deal with varying file sizes—from a mere few bytes to multiple terabytes. Moreover, the inherent logical associations between files considerably influence user access patterns. Traditional caching research has predominantly revolved around uniform file sizes and independent reference models. Contrarily, scientific workloads encounter variances in file sizes, and logical file interconnections significantly influence user access patterns.My investigations show how LHC's hierarchical data organization, particularly its compartmentalization into datasets, impacts request patterns. Recognizing the opportunity, I introduce innovative caching policies that emphasize dataset-specific knowledge, and compare their effectiveness with traditional file-centric strategies. Furthermore, my findings underscore the "delayed hits" phenomenon triggered by limited connectivity between computing and storage locales, shedding light on its potential repercussions for caching efficiency.Acknowledging the long-standing challenge of predicting Data Popularity in the High Energy Physics (HEP) community, especially with the upcoming HL-LHC era's storage conundrums, my research integrates Machine Learning (ML) tools. Specifically, I employ the Random Forest algorithm, known for its suitability with Big Data. By harnessing ML to predict future file reuse patterns, I present a dual-stage method to inform cache eviction policies. This strategy combines the power of predictive analytics and established cache eviction algorithms, thereby devising a more resilient caching system for the WLCG. In conclusion, this research underscores the significance of robust storage services, suggesting a direction towards stateless caches for smaller sites to alleviate complex storage management requirements and open the path to an additional level in the storage hierarchy. Through this thesis, I aim to navigate the challenges and complexities of data storage and retrieval, crafting more efficient methods that resonate with the evolving needs of the WLCG and its global community
Mohammadi, Samin. "Analysis of user popularity pattern and engagement prediction in online social networks". Thesis, Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0019/document.
Pełny tekst źródłaNowadays, social media has widely affected every aspect of human life. The most significant change in people's behavior after emerging Online Social Networks (OSNs) is their communication method and its range. Having more connections on OSNs brings more attention and visibility to people, where it is called popularity on social media. Depending on the type of social network, popularity is measured by the number of followers, friends, retweets, likes, and all those other metrics that is used to calculate engagement. Studying the popularity behavior of users and published contents on social media and predicting its future status are the important research directions which benefit different applications such as recommender systems, content delivery networks, advertising campaign, election results prediction and so on. This thesis addresses the analysis of popularity behavior of OSN users and their published posts in order to first, identify the popularity trends of users and posts and second, predict their future popularity and engagement level for published posts by users. To this end, i) the popularity evolution of ONS users is studied using a dataset of 8K Facebook professional users collected by an advanced crawler. The collected dataset includes around 38 million snapshots of users' popularity values and 64 million published posts over a period of 4 years. Clustering temporal sequences of users' popularity values led to identifying different and interesting popularity evolution patterns. The identified clusters are characterized by analyzing the users' business sector, called category, their activity level, and also the effect of external events. Then ii) the thesis focuses on the prediction of user engagement on the posts published by users on OSNs. A novel prediction model is proposed which takes advantage of Point-wise Mutual Information (PMI) and predicts users' future reaction to newly published posts. Finally, iii) the proposed model is extended to get benefits of representation learning and predict users' future engagement on each other's posts. The proposed prediction approach extracts user embedding from their reaction history instead of using conventional feature extraction methods. The performance of the proposed model proves that it outperforms conventional learning methods available in the literature. The models proposed in this thesis, not only improves the reaction prediction models to exploit representation learning features instead of hand-crafted features but also could help news agencies, advertising campaigns, content providers in CDNs, and recommender systems to take advantage of more accurate prediction results in order to improve their user services
Rakoczy, Monika. "Exploring human interactions for influence modeling in online social networks". Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL010.
Pełny tekst źródłaOnline social networks are constantly growing in popularity. They enable users to interact with one another and shifting their relations to the virtual world. Users utilize social media platforms as a mean for a rich variety of activities. Indeed, users are able to express their opinions, share experiences, react to other users' views and exchange ideas. Such online human interactions take place within a dynamic hierarchy where we can observe and distinguish many qualities related to relations between users, concerning influential, trusted or popular individuals. In particular, influence within Social Networks (SN) has been a recent focus in the literature. Many domains, such as recommender systems or Social Network Analysis (SNA), measure and exploit users’ influence. Therefore, models discovering and estimating influence are important for current research and are useful in various disciplines, such as marketing, political and social campaigns, recommendations and others. Interestingly, interactions between users can not only indicate influence but also involve trust, popularity or reputation of users. However, all these notions are still vaguely defined and not meeting the consensus in the SNA community. Defining, distinguishing and measuring the strength of those relations between the users are also posing numerous challenges, on theoretical and practical ground, and are yet to be explored. Modelization of influence poses multiple challenges. In particular, current state-of-the-art methods of influence discovery and evaluation still do not fully explore users’ actions of various types, and are not adaptive enough for using different SN. Furthermore, adopting the time aspect into influence model is important, challenging and in need of further examination part of the research. Finally, exploring possible connections and links between coinciding notions, like influence and reputation, remains to be performed.In this thesis, we focus on the qualities of users connected to four important concepts: influence, reputation, trust, and popularity, in the scope of SNA for influence modeling. We analyze existing works utilizing these notions and we compare and contrast their interpretations. Consequently, we emphasize the most important features that these concepts should include and we make a comparative analysis of them. Accordingly, we present a global classification of the notions concerning their abstract level and distinction of the terms from one another, which is a first and required contribution of the thesis. Consequently, we then propose a theoretical model of influence and present influence-related ontology. We also present a distinction of notion not yet explored in SNA discipline -- micro-influence, which targets new phenomena of users with a small but highly involved audience, who are observed to be still highly impactful. Basing on the definitions of the concepts, we propose a practical model, called Action-Reaction Influence Model (ARIM). This model considers type, quality, quantity, and frequency of actions performed by users in SN, and is adaptive to different SN types. We also focus on the quantification of influence over time and representation of influence causal effect. In order to do that, we focus on a particular SN with a specific characteristic - citation network. Indeed, citation networks are particularly time sensitive. Accordingly, we propose Time Dependent Influence Estimation (TiDIE), a model for determining influence during a particular time period between communities within time-dependent citation networks. Finally, we also combine two of the abovementioned notions, influence and reputation, in order to investigate the dependencies between them. In particular, we propose a transition method, ReTiDIE, that uses influence for predicting the reputation. For each of the proposed approaches, experiments have been conducted on real-world datasets and demonstrate the suitability of the methods
Mohammadi, Samin. "Analysis of user popularity pattern and engagement prediction in online social networks". Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0019.
Pełny tekst źródłaNowadays, social media has widely affected every aspect of human life. The most significant change in people's behavior after emerging Online Social Networks (OSNs) is their communication method and its range. Having more connections on OSNs brings more attention and visibility to people, where it is called popularity on social media. Depending on the type of social network, popularity is measured by the number of followers, friends, retweets, likes, and all those other metrics that is used to calculate engagement. Studying the popularity behavior of users and published contents on social media and predicting its future status are the important research directions which benefit different applications such as recommender systems, content delivery networks, advertising campaign, election results prediction and so on. This thesis addresses the analysis of popularity behavior of OSN users and their published posts in order to first, identify the popularity trends of users and posts and second, predict their future popularity and engagement level for published posts by users. To this end, i) the popularity evolution of ONS users is studied using a dataset of 8K Facebook professional users collected by an advanced crawler. The collected dataset includes around 38 million snapshots of users' popularity values and 64 million published posts over a period of 4 years. Clustering temporal sequences of users' popularity values led to identifying different and interesting popularity evolution patterns. The identified clusters are characterized by analyzing the users' business sector, called category, their activity level, and also the effect of external events. Then ii) the thesis focuses on the prediction of user engagement on the posts published by users on OSNs. A novel prediction model is proposed which takes advantage of Point-wise Mutual Information (PMI) and predicts users' future reaction to newly published posts. Finally, iii) the proposed model is extended to get benefits of representation learning and predict users' future engagement on each other's posts. The proposed prediction approach extracts user embedding from their reaction history instead of using conventional feature extraction methods. The performance of the proposed model proves that it outperforms conventional learning methods available in the literature. The models proposed in this thesis, not only improves the reaction prediction models to exploit representation learning features instead of hand-crafted features but also could help news agencies, advertising campaigns, content providers in CDNs, and recommender systems to take advantage of more accurate prediction results in order to improve their user services
Książki na temat "Popularité des données"
Preston, Katherine K. The Renaissance of English-Language Opera in America. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199371655.003.0003.
Pełny tekst źródłaCzęści książek na temat "Popularité des données"
Curti, Roberto, i Roberto Curti. "A Celibate Founder". W Blood and Black Lace, 99–106. Liverpool University Press, 2020. http://dx.doi.org/10.3828/liverpool/9781911325932.003.0012.
Pełny tekst źródłaRaporty organizacyjne na temat "Popularité des données"
Brinkerhoff, Derick W., Sarah Frazer i Lisa McGregor. S'adapter pour apprendre et apprendre pour s'adapter : conseils pratiques tirés de projets de développement internationaux. RTI Press, styczeń 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0015.1801.fr.
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