In the context of increasing cyber threats and stringent regulations, this article presents a data security approach based on multi-level labeling to track data. By classifying data sensitivity levels and labeling them, our method enables tracking data throughout its lifecycle, from collection to destruction. Integrating technologies like machine learning enhances this traceability, allowing real-time tracking and risk anticipation. Our model demonstrates superior performance compared to existing studies in terms of precision and reliability, while ensuring compliance with international standards.

The search for information becomes a primordial need nowadays and it is possible that the information sought cannot be found by searching in a single data source, actually, an information may require collecting its parts from several distributed data sources. Our work aims to set up an aggregated search engine able to respond to a query by collecting data from independent data sources via a single user interface, and query processing in our system goes through several steps before returning final answers. Process speed is one of the main qualities of any search engine, and this speed can be affected if the search engine interacts with several data sources, which is the case of our work. In this regard, we propose in this paper a solution to optimize runtime in our aggregated search system, firstly, we present runtime evaluation of each process step in order to identify the costliest in terms of execution time, then, we propose a parallel processing architecture to optimize runtime without any data loss. The experimental results confirm the efficiency of our proposed architecture.

Aggregated search approach in the web of data is to look for results of a single query by aggregating pieces of data from distributed data sources and integrating them, if possible, into an entire entity. However, it may be possible that some parts of the query return null results which affects answers processing. In this work, we propose a star-group patterns-based solution to prepare a SPARQL query to be executed over distributed data sources without having prior knowledge of contributing data sources. The first objective of this work is identifying the complementarity between query parts after decomposing it, and the second one is rewriting the user’s query considering only parts with not null results based on star-group patterns in order to return semantically significant answers. The evaluation of our proposed solution shows that this query rewriting method allows to return as much as possible the sought information.

International audience

Summary With the constant rise of data volumes in many disciplines, various new Big data management systems have emerged to provide scalable tools for efficient data integration, processing, and analysis. In this article, we provide an overview of biomedical data integration systems focusing on ontology‐based semantic systems and Big data technologies based systems such as Apache Spark. We also propose a new semantic data integration system, called Integrated Proteomics Data System (IPDS), which uses a mediator approach. IPDS provides users a unified interface for query processing and data exploration. This system takes advantage of the Apache Spark framework to perform the query transformation and execution needed to question the integrated data sources. We develop a domain ontology that allows the user to formulate its queries in terms defined in the ontology. IPDS is a case study of semantic proteomics data integration linking four data sources UniProt (protein annotation), String (protein‐protein interaction), PDB (protein structure), and Pubmed (biomedical citation).

Summary Organizational perspectives of process mining consist of organizing and classifying the organization in terms of missions, roles as well as the interactions between the performers. Social mining is a branch of process mining that centralizes on construction social graphs based on the information held in the process. However, standard clustering approaches are not always proper to business processes as they are known for their complex, flexible, and intrinsic nature. Therefore, fuzzy clustering is capable of identifying indeterminate frontiers that hard clustering omits to identify. In this article, we propose a plugin that applies entropy‐based fuzzy clustering for mining similar tasks using event data. The plugin is intended to be integrated into ProM6 framework as a package. It is the first plugin that uses fuzzy clustering for mining social networks and adopts data‐driven documents library to visualize graphs. The results of the plugins' applicability are illustrated using a case study of a Dutch financial institute.

In the big data era, the cloud computing services have been adopted to face the emergence of data that needs to be stored and processed properly. However, these services need to provide safety mechanisms to insure its secure adoption. Thus, several solutions have been proposed including the use of secure architectures by customers. In that context, an architecture based on multi-agent systems has been proposed which aims to secure both storage and exploration of data hosted in the Cloud. In this paper, we present a brief synthesis of data security methods. We then focus on the multi-agent system architecture. Finally, we propose our solution considering the design and implementation in Java of an alteration agent which will ensure the secure storage of data stored in the Cloud. We finally present the test results of this agent on real datasets.

Cloud computing services have been adopted to provide the necessary tools and resources to face the emergence of data that needs to be stored and processed properly. However, these promising services, raise the issue of security and reliability in terms of data confidentiality, control and loss of intellectual property. In this work, we exploit the characteristics of multi agent systems to deliver an optimal and secure solution for data storage and exploration in the Cloud. Our solution is based on an encryption process before storage, while an intelligent multi agent system was designed and simulated to optimize the exploration of the data in a Cloud environment. Our architecture aims to use adaptive agents able to predict alerts, make decisions and block any intrusion.

Social networks play an important role in the dissemination of information and the spread of influence. Identifying the most influential individuals spreading information or infectious diseases can assist or hinder information dissemination, product exposure, and contagious disease detection. The leader or influential members may be even more critical to product diffusion and the formation of widespread contagions. This paper proposes a new algorithm LeadersRank, which use the network structure to identify influential members, i.e. the leaders' nodes, for community detection in social networks, without a priori knowledge of k number of communities in the network.

Undeniably, cloud computing has been reshaping information technology drastically, and eventually making utility computing comes true. Internet services, ranging from simple word processing to online social networking, are progressively being shifted to the cloud. These services are interesting because they assure high scalability, availability, and reliability. Nevertheless, they are missing security as a vital non-functional requirement. As users no longer control their data and hand its governance over to cloud providers, data is significantly exposed to misuses and malicious attacks. In this article we present a lightweight software based agent that runs along with the cloud database on every host machine (i.e. virtual machine). The agents implement all the logic needed to prevent malicious attacks, monitor, and assess database transactions.

Mobile cloud computing has been introduced to be a powerful technology for mobile services by combining mobile computing and cloud computing technology. Though, a direct integration of two technologies can overcome a many of hurdles related to the performance, flexibility, security, and dynamic management discussed in mobile computing. Mobile cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device. However, to make this vision a reality is far from being achieved and opens many new research questions. In addition, the collaboration between a mobile device and a cloud server poses complex performance issues associated with synchronization of data, network condition, security etc.