Résumé: Online social networks play a major role in the spread of information at very large scale and it becomes essential to provide means to analyse this phenomenon. In this paper we address the issue of predicting the temporal dynamics of the information diffusion process. We develop a graph-based approach built on the assumption that the macroscopic dynamics of the spreading process are explained by the topology of the network and the interactions that occur through it, between pairs of users, on the basis of properties at the microscopic level. We introduce a generic model, called T-BaSIC, and describe how to estimate its parameters from users behaviours using machine learning techniques. Contrary to classical approaches where the parameters are fixed in advance, T-BaSIC's parameters are functions depending of time, which permit to better approximate and adapt to the diffusion phenomenon observed in online social networks. Our proposal has been validated on real Twitter datasets. Experiments show that our approach is able to capture the particular patterns of diffusion depending of the studied sub-networks of users and topics. The results corroborate the "two-step" theory (1955) that states that information flows from media to a few "opinion leaders" who then transfer it to the mass population via social networks and show that it applies in the online context. This work also highlights interesting recommendations for future investigations.

Commentaires: Version mise à jour et étendue de l'article "A Predictive Model for the Temporal Dynamics of Information Diffusion in Online Social Networks"

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Résumé: This paper describes SONDY, a tool for analysis of trends and dynamics in online social network data. SONDY addresses two audiences: (i) end-users who want to explore social activity and (ii) researchers who want to experiment and compare mining techniques on social data. SONDY helps end-users like media analysts or journalists understand social network users interests and activity by providing emerging topics and events detection as well as network analysis functionalities. To this end, the application proposes visualizations such as interactive time-lines that summarize information and colored user graphs that reflect the structure of the network. SONDY also provides researchers an easy way to compare and evaluate recent techniques to mine social data, implement new algorithms and extend the application without being concerned with how to make it accessible.

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Ref HAL: hal-00783638_v1
Résumé: Dans cet article, nous portons notre regard sur l'aide à la décision du point de vue des systèmes décisionnels au sens des entrepôts de données et de l'analyse en ligne. Après avoir défini les concepts qui sous-tendent ces systèmes, nous nous proposons d'aborder les problématiques de recherche qui leur sont liées selon quatre points de vue : les données, les environnements de stockage, les utilisateurs et la sécurité. Nous abordons finalement les problèmes qui restent ouverts dans le domaine des entrepôts de données.

Résumé: Ce papier décrit la plateforme SONDY qui permet l'analyse et la fouille de données issues de réseaux sociaux en ligne. La plateforme permet d'explorer et de visualiser l'évolution des thématiques populaires et la structure du réseau social de manière interactive. Elle permet également de comparer les méthodes utilisées et d'en intégrer de nouvelles. La démonstration consistera à expérimenter tous les services de SONDY sur des données issues de Twitter.

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Ref HAL: hal-00728197_v1
Résumé: In this vision paper, we discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, we first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, we propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, we propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).