El papel del Banco Mundial en la crisis económica actual

Universidad de Sevilla. Grado en Finanzas y Contabilida

La fourniture de services : une approche novatrice pour l'ouverture des systèmes de formation et du marché de la FOAD

Dans cet article, nous proposons un concept novateur de « système fournisseur de services applicatifs » qui favorise l'ouverture et l'interopérabilité des systèmes de formation. Le but de cette ouverture est de faciliter l'interaction entre systèmes et, par la même occasion, d'ouvrir le marché de la FOAD par l'introduction d'un nouveau modèle économique. Les premiers services que nous développons dans cet article sont des services de gestion des contenus et des services de gestion des parcours d'apprentissage

Characteristic sets profile features: Estimation and application to SPARQL query planning

RDF dataset profiling is the task of extracting a formal representation of a dataset’s features. Such features may cover various aspects of the RDF dataset ranging from information on licensing and provenance to statistical descriptors of the data distribution and its semantics. In this work, we focus on the characteristics sets profile features that capture both structural and semantic information of an RDF dataset, making them a valuable resource for different downstream applications. While previous research demonstrated the benefits of characteristic sets in centralized and federated query processing, access to these fine-grained statistics is taken for granted. However, especially in federated query processing, computing this profile feature is challenging as it can be difficult and/or costly to access and process the entire data from all federation members. We address this shortcoming by introducing the concept of a profile feature estimation and propose a sampling-based approach to generate estimations for the characteristic sets profile feature. In addition, we showcase the applicability of these feature estimations in federated querying by proposing a query planning approach that is specifically designed to leverage these feature estimations. In our first experimental study, we intrinsically evaluate our approach on the representativeness of the feature estimation. The results show that even small samples of just 0.5% of the original graph’s entities allow for estimating both structural and statistical properties of the characteristic sets profile features. Our second experimental study extrinsically evaluates the estimations by investigating their applicability in our query planner using the well-known FedBench benchmark. The results of the experiments show that the estimated profile features allow for obtaining efficient query plans