De nos jours, les entreprises gèrent un grand volume de données généralement organisées en “silos”. Chaque “silo de données” contient des données relatives à une unité commerciale spécifique ou à un projet. Cet éclatement des données ne facilite pas la prise de décision nécessitant l’utilisation et le croisement de données provenant de différents silos. Un défi reste donc à relever
This paper describes a new temporal graph modelling solution to organize and memorize changes in a business application. To do so, we enrich the basic graph by adding the concepts of states and instances. Our model has first the advantage of representing a complete temporal evolution of the graph, at the level of: (i) the graph structure, (ii) the attribute
With the current trend of patient-centric health-care, blockchain-based Personal Health Records (PHRs) frameworks have been emerging. The adoption of these frameworks is still in its infancy stage and is dependent on a broad range of factors. In this paper we look at some of the typical concerns raised from a centralized medical records solution such as the one deployed in
The class distribution of data is one of the factors that regulates the performance of machine learning models. However, investigations on the impact of different distributions available in the literature are very few, sometimes absent for domain-specific tasks. In this paper, we analyze the impact of natural and balanced distributions of the training set in deep learning (DL) models applied
The goal of query performance prediction (QPP) is to automatically estimate the effectiveness of a search result for any given query, without relevance judgements. Post-retrieval features have been shown to be more effective for this task while being more expensive to compute than pre-retrieval features. Combining multiple post-retrieval features is even more effective, but state-of-the-art QPP methods are impossible to
Worldwide epidemic events have confirmed the need for medical data processing tools while bringing issues of data privacy, transparency and usage consent to the front. Federated Learning and the blockchain are two technologies that tackle these challenges and have been shown to be beneficial in medical contexts where data are often distributed and coming from different sources. In this paper
Missing values occur commonly in the multidimensional data warehouses. They may generate problems of usefulness of data since the analysis performed on a multidimensional data warehouse is through different dimensions with hierarchies where we can roll up or drill down to the different parameters of analysis. Therefore, it’s essential to complete these missing values in order to carry out a
Data lakes are supposed to enable analysts to perform more efficient and efficacious data analysis by crossing multiple existing data sources, processes and analyses. However, it is impossible to achieve that when a data lake does not have a metadata governance system that progressively capitalizes on all the performed analysis experiments. The objective of this paper is to have an
In recent years, the need to use NoSQL systems to store and exploit big data has been steadily increasing. Most of these systems are characterized by the property “schema less” which means absence of the data model when creating a database. This property offers an undeniable flexibility allowing the user to add new data without making any changes on the
Using data warehouses to analyse multidimensional data is a significant task in company decision-making.The data warehouse merging process is composed of two steps: matching multidimensional components and then merging them. Current approaches do not take all the particularities of multidimensional data warehouses into account, e.g., only merging schemata, but not instances; or not exploiting hierarchies nor fact tables. Thus, in