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International audience; With the emergence of new technologies and the ubiquitous connecti-vity, large amounts of data are being generated everyday with the need to be stored properly and explored rapidly. In this context, the cloud computing services have been adopted to face these rising challenges. But in a cloud environment, data and the application are controlled by the service provider and the customer does not always have the possibility to increase the security level imposed. This leads users to apply encryption mechanisms before storing their data in the cloud. In this paper we propose a new approach that combines the strengths of both steganography and cryptography called Data Alteration. The technique aims to hide the data by modifying it completely as it remains readable, meaningful and therefore shows no suspicions to malicious cloud providers and pirates. The proposed appoach was implemented in Java and tested on realistic datasets in a multi agent systems based architecture.; Avec l’émergence de nouvelles technologies et la connectivité omniprésente, de grandes quantités de données sont générées chaque jour, il est nécessaire de les stocker correctement et de les explorer rapidement. Dans ce contexte, les services de cloud computing ont été adoptés pour faire face à ces défis croissants. Mais dans un environnement en nuage, les données et l'application sont contrôlées par le fournisseur de services et le client n'a pas toujours la possibilité d'augmenter le niveau de sécurité imposé. Cela conduit les utilisateurs à appliquer des mécanismes de cryptage avant de stocker leurs données dans le cloud. Dans cet article, nous proposons une nouvelle approche combinant les forces de la stéganographie et de la cryptographie appelée altération des données. La technique vise à masquer les données en les modifiant complètement, car elles restent lisibles, ont un sens et ne montrent donc aucune suspicion aux fournisseurs de cloud et aux pirates malveillants. L’application proposée a été mise en œuvre en Java et testée sur des ensembles de données réalistes dans une architecture à systèmes multi-agents.

In the recent past, we have witnessed dramatic increases in the volume of data literally in every area: business, science,and daily life to name a few. The Hadoop framework - an open source project based on the MapReduce paradigm - isa popular choice for big data analytics. However, the performance gained from Hadoop’s features is currently limitedby its default block placement policy, which does not take any data characteristics into account. Indeed, the efficiencyof many operations can be improved by a careful data placement, including indexing, grouping, aggregation and joins.In our work we propose a data warehouse partitioning strategy to improve query gain performances. We investigatethe performance gain for OLAP cube construction with and without data organization on a Hadoop cluster. And this,by varying the number of nodes and data warehouse size.Our experiments suggest that a good data placement on acluster during the implementation of the data warehousecan significantly increase the OLAP cube construction andquerying performances. In the next step, we will extendthe experiments to study the effects of other configurationparameters on collocation data in the context of paralleldata warehousing, such as partitions size, replication factorand OLAP query complexity. We plan also to study an in-telligent system for warehouses data placement on clustersby integrating Multi-Agent System (MAS) and IntelligentAgents to the process.

Nowadays, many governments and states are involved in an opening strategy of their public data. However, the volume of these opened data is constantly increasing, and will reach in the near future limitations of current treatment and storage capacity. On the other hand, the MapReduce paradigm is one of the most used parallel programming models. With a master-slave architecture, it allows parallel processing of very large data sets. In this paper, we propose a parallel approach based on Mapreduce to process public open data. Applied, as a case study, to the official data sets from the French Ministry of Communication. We implement a parallel algorithm as a solution to define a ranking of national museums and galleries according to the accessibility degrees for people with disabilities. We studied the feasibility of our approach in two main parts: The performance in terms of execution time, and, the visualization of the obtained results in order to integrate them into solutions such as geographic BI. This work can be applied to other cases with very large data sets.

Le modèle MapReduce est aujourd'hui l'un des modèles de programmation parallèle les plus utilisés. Définissant une architecture Maître-Esclave, il permet le traitement parallèle de grandes masses de données. Dans ce papier, nous proposons un algorithme basé sur MapReduce qui permet, à partir des données publiques du Ministère Français de la Communication et de la Culture, de définir un classement des galeries et musées nationaux selon leurs degré d'accessibilité aux personnes handicapées. Tout en profitant de la puissance et de la flexibilité du paradigme MapReduce, les décideurs pourront mettre en place des stratégies efficaces à moindre coût et avoir ainsi une vision plus précise sur les établissements culturels et leurs limites relatives à cette catégorie de personnes. L'algorithme que nous proposons peut être exploité et appliqué à d'autres cas d'études avec des jeux de données plus volumineux.

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.

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.

BELMANDT is the collective pseudonym under which a group of mathematicians and computer scientists have decide to publish their common works on Pretopology and to give a nod to BOURBAKI. These works would never have been realized without the dynamic leadership of professor Marcel Brissaud, now retired, and who is of course associated to the publication of this book. Marcel Brissaud, during the seventies, has been the first to observe the interest of developing mathematical concepts adapted to humanities. Pretopology theory relies on the statement that the axiomatic of mathematical topology is too strong to model the phenomena of social sciences. This axiomatic has to be reconsidered to give birth to a theory that is adapted to data issued from these fields of expertise, better than trying to force data to fit to an existing and inadequate theory. This book is the first English revised version of a first book, published in French, which is planned to be the first of a series dedicated to the whole set of results obtained in Pretopology: continuity, connectedness, associated metric... Many applications of Pretopology were conducted in various fields: economy with the structural analysis of a economy, game theory with the study of the dynamics of coalitions in cooperative games, sociology with analysis of social networks, data analysis with the construction of clustering algorithms, pattern recognition with digital image analysis as in mathematical morphology, etc. Purely mathematical work in Pretopology has enabled the building of processing algorithms that have been put in a software library, PRETOPOLIB, allowing researchers to implement a pretopological approach in their work. We wish the reader of this book the pleasure of exploring the basics of Pretopology and we hope that he will find the materials for his own work and we give him a rendezvous for the next books on the subject.