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The adoption of Big Data systems by the companies is relatively new, although the data modeling and system design are ages old. Despite the fact that traditional databases are built on solid foundations, they cannot handle the swift and massive flow of data coming from multiple different sources. Herein, NoSQL databases are an inevitable alternative. However, these systems are schemaless compared to traditional databases. It is important to emphasize that schemaless does not mean no-schema which would mean that NoSQL databases do not need modeling. Hence, there is a need for conceptual models to define the data structure in these databases. This paper sheds a light on the importance of the UML in showing how to store Big Data described through meta-models within NoSQL databases. We propose a novel Big Data modeling methodology for NoSQL databases called UML4NoSQL, which is independent of the target system, and taking into account the four Big Data characteristics: Variety, Volume, Velocity, and Veracity (4 V's). The approach relies on the UML blocks with a data-up technique; it starts with a use-case and the class diagram resulting from the understanding of the data at hand and the definition of the developer's strategies while focusing on the user's needs. To illustrate our approach, we take a case study from health care domain. We show that our approach produces designs that can be implemented on NoSQL document-oriented system with respect to Big Data 4 V's.

International audience; In the last decade, Online Analytical Processing (OLAP) has taken an increasingly important role in Business Intelligence. Approaches, solutions and tools have been provided for both databases and data warehouses, which focus mainly on numerical data. These solutions are not suitable for textual data. Because of the fast growing of this type of data, there is a need for new approaches that take into account the textual content of data. In the context of Text OLAP (OLAP on text or documents), the measure can be textual and need a suitable aggregation function for OLAP operations such as roll-up.We present in this paper a new aggregation function for textual data. Our approach is based on the affinity between keywords and uses the search of cycles in a graph to find the aggregated keywords. We also present performances and a comparison with three other methods. The experimental study shows good results for our approach.

International audience; The index selection problem (ISP) concerns the selection of an appropriate indexes set to minimize the total cost for a given workload under storage constraint. Since the ISP has been proven to be an NP-hard problem, most studies focus on heuristic algorithms to obtain approximate solutions. The problem becomes more difficult for indexes defined on multiple tables such as bitmap join indexes, since it requires the exploration of a large search space. Studies dealing with the problem of selecting bitmap join indexes mainly focused on proposing pruning solutions of the search space by the means of data mining techniques or heuristic strategies. The main shortcoming of these approaches is that the indexes selection process is performed in two steps. The generation of a large number of indexes is followed by a pruning phase. An alternative is to constrain the input data earlier in the selection process thereby reducing the output size to directly discover indexes that are of interest for the administrator. For example, to select a set of indexes, the administrator may put limits on the number of attributes or the cardinality of the attributes to be included in the indexes configuration he is seeking. In this paper we addressed the bitmap join indexes selection problem using a constraint-based approach. Unlike previous approaches, the selection is performed in one step by introducing constraints in the selection process. The proposed approach is evaluated using APB-1 benchmark.