Open Science is based on a collaborative network to develop transparent, accessible, and shared knowledge. Open Research Data Platforms (ORDPs) are deployed to fulfill the needs for data sharing of a specific community and/or scientific discipline. The high variety of research areas creates a barrier to data sharing between research entities. To enable this research data to be found by the research entities that need it, it is necessary to establish access to different ORDPs that are unknown to these research entities. The goal of this article is to provide a quantitative analysis showing the current limitations of data sharing between ORDPs in Open Science. We then propose a solution to improve data access and sharing based on theoretical foundations and an experimental approach. We propose to extend our theoretical interoperability model, which helps us to define the necessary steps to interoperate ORDPs. We present and discuss a quantitative evaluation of ORDPs’ interoperability. Based on this exploratory study, we propose a solution that enables research entities to discover unknown ORDPs, thereby facilitating access to relevant data. This solution is the Open Science Data Network (OSDN), a decentralized, distributed, and federated network of ORDPs that integrates a query propagation process and robustness features. To enable the deployment of OSDN at an Open Science scale, we designed our solution by considering its adoption cost relative to a non-organized interoperability approach. With two ORDPs integrated into the OSDN, the adoption cost is estimated to be reduced by at least 17%. This reduction approaches 100% as the number of integrated ORDPs increases. To demonstrate the feasibility of the solution, we developed a Proof of Concept (POC) and applied it to two research projects from different domains and involving distinct research communities. For the first research project, we measured a 7% increase in the volume of accessed data and an 80% reduction in the time needed to find this data. In addition, researcher from this experiment was able to formulate new intra- and interdisciplinary research questions thanks to the newly accessed data. In the second research project, we observed an increase in data volume of up to a factor of 3968. More importantly, this process led to the discovery of new essential data that was previously missing.

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 easily accessible, reusable data lake that capitalizes on all user experiences. To meet this need, we propose an analysis-oriented metadata model for data lakes. This model includes the descriptive information of datasets and their attributes, as well as all metadata related to the machine learning analyzes performed on these datasets. To illustrate our metadata solution, we implemented a web application of data lake metadata management. This application allows users to find and use existing data, processes and analyses by searching relevant metadata stored in a NoSQL data store within the data lake. To demonstrate how to easily discover metadata with the application, we present two use cases, with real data, including datasets similarity detection and machine learning guidance.

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 easily accessible, reusable data lake that capitalizes on all user experiences. To meet this need, we propose an analysis-oriented metadata model for data lakes. This model includes the descriptive information of datasets and their attributes, as well as all metadata related to the machine learning analyzes performed on these datasets. To illustrate our metadata solution, we implemented an application of data lake metadata management. This application allows users to find and use existing data, processes and analyses by searching relevant metadata stored in a NoSQL data store within the data lake. To demonstrate how to easily discover metadata with the application, we present two use cases, with real data, including datasets similarity detection and machine learning guidance.

Nowadays, companies manage a large volume of data usually organised in ”silos”. Each ”data silo” contains data related to a specific Business Unit, or a project. This scattering of data does not facilitate decision-making requiring the use and cross-checking of data coming from different silos. So, a challenge remains: the construction of a Business View of all data in a company. In this paper, we introduce the concepts of Enterprise Knowledge Graph (EKG) and Decentralised EKG (DEKG). Our DEKG aims at generating a Business View corresponding to a synthetic view of data sources. We first define and model a DEKG with an original process to generate a Business View before presenting the possible implementation of a DEKG.

Using data warehouses to analyse multidimensional data is a significant task in company decision-making. The need for analyzing data stored in different data warehouses generates the requirement of merging them into one integrated data warehouse. 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 this paper, we propose an automatic merging approach for star schema-modeled data warehouses that works at both the schema and instance levels. We also provide algorithms for merging hierarchies, dimensions and facts. Eventually, we implement our merging algorithms and validate them with the use of both synthetic and benchmark datasets.

Clustering is a data analysis method for extracting knowledge by discovering groups of data called clusters. Among these methods, state-of-the-art density-based clustering methods have proven to be effective for arbitrary-shaped clusters. Despite their encouraging results, they suffer to find low-density clusters, near clusters with similar densities, and high-dimensional data. Our proposals are a new characterization of clusters and a new clustering algorithm based on spatial density and probabilistic approach. First of all, sub-clusters are built using spatial density represented as probability density function (p.d.f) of pairwise distances between points. A method is then proposed to agglomerate similar sub-clusters by using both their density (p.d.f) and their spatial distance. The key idea we propose is to use the Wasserstein metric, a powerful tool to measure the distance between p.d.f of sub-clusters. We show that our approach outperforms other state-of-the-art density-based clustering methods on a wide variety of datasets.

The identification of alignments between heterogeneous ontologies is one of the main research issues in the semantic web.The manual matching of the ontologies is a complex, time consuming and an error prone task.Therefore, ontology matching systems aims to automate this process.Usually, these systems perform the matching process by combining element and structural level matchers.Selecting the optimal string similarity measure associated with its threshold is an important issue in order to enhance the effectiveness of the element level matcher, which in turn will improve the whole ontology system results.In this paper, we present POMap, an ontology matching system based on a syntactic study covering element and structural levels.For the element level matcher we have adopted the best configuration based on the analysis of the performances of many string similarity measures associated with their thresholds.For the structural level, we have performed a syntactic study on both subclasses and siblings in order to infer the structural similarity.Our proposed matching system is validated and evaluated on the Anatomy, the Conference and the Large Biomedical tracks provided by the benchmark of OAEI 2016 ontology matching campaign.

This paper focuses on data warehouse modelling. The conceptual model we defined, is based on object concepts extended with specific concepts like generic classes, temporal classes and archive classes. The temporal classes are used to store the detailed evolutions and the archive classes store the summarised data evolutions. We also provide a flexible concept allowing the administrator to define historised parts and non-historised parts into the warehouse schema. Moreover, we introduce constraints which configure the data warehouse behaviour and these various parts. To validate our propositions, we describe a prototype dedicated to the data warehouse design.