Résumé: In the context of decision making, data warehouses support OLAP technology and they have been very useful for efficient analysis onto structured data. For several years, OLAP is also used to analyze and visualize more complex data. Now, many data sets of interest can be described as a linked collection of interrelated objects. They could be represented as heterogeneous information networks, in which there are multiple object and link types. In this paper, we are focusing on bibliographic data. This type of data constitutes a rich source that is the starting point of research on bibliometrics, scientometrics domains. In this context, we discuss the interest of combining information networks, OLAP and data mining technologies. We propose a framework to materialize this combination and discuss the main challenges to build this framework. The basic idea is to be able to analyze various networks built from the bibliographic data representing different points of view (authors networks, citations networks...) and their dynamic.

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Résumé: Large-scale data analysis has become increasingly important for many enterprises, and Cloud Computing, under the impulse of large companies, has recently endowed a special attention both in industry and academic researches. Hadoop, based on a new distributed computing paradigm, called MapReduce, has allowed to facilitate access to such environments, due to its impressive scalability and flexibility to handle structured as well as unstructured data. The goal of our work is to develop a Cloud Computing environment for exploiting data warehouses and perform online analysis. It consists of handling large nonrelational databases and supporting data warehouse with a new generation of database management systems (DBMS) such as Hive. Thus, to set up such an environment, we implemented a data warehouse under Hadoop and Hive and we used the Map and Reduce functions of this environment, then we compared the cost of loading the warehoused data and constructing OLAP cubes between a virtual and a physical cluster, as well as the rise in data loading on a physical cluster. Obtained results allows MapReduce developers to fully compare the performance, help in the choice of platform, in which a customer application can be developed to translate SQL requests to HQL (Hive-QL) requests, and check if a not-relational model is adequate or not.

Résumé: L’informatique dans les nuages, sous l’impulsion des grandes compagnies telles que Google, Microsoft ou encore Amazon, a récemment suscité une attention particulière que ce soit dans le monde industriel ou académique. L’arrivée d’Hadoop, basé sur le paradigme MapReduce, pour le traitement parallèle des grandes quantités de données, a permis de faciliter l’accès à un tel environnement. L’objectif de cette étude est de mettre en place un environne-ment informatique dans les nuages permettant de créer des entrepôts de don-nées et de réaliser des analyses en ligne. Il s’agit notamment de manipuler de larges bases de données non relationnelles, support des entrepôts de données, avec une nouvelle génération de systèmes de gestion de bases de données (SGBD) tels que Hive. Pour cela, nous avons implémenté un entrepôt de don-nés issu de SSB sous Hive. Nous avons ensuite comparé les résultats des temps de chargement de l’entrepôt et de construction de cube OLAP, ainsi que la montée en charge avec différentes variantes d’architectures. Les résultats obtenus peuvent servir de base pour de futurs travaux dans un but de comparaison des performances, d’aide dans le choix de plateforme, où des applications client peuvent être développées pour traduire des requêtes SQL vers des requêtes Hive-QL.