Euranova has 3 fundamental pillars: explore, craft and serve. The explore pillar of Euranova is an independent research centre dedicated to data science, software engineering and AI.
Through the exploration of tomorrow’s engineering and data science to answer today’s problems, our research centre is dedicated to anticipating the challenges that European businesses face. We find solutions to current and future digital challenges with passion, creativity and integrity.
Euranova has 3 fundamental pillars: explore, craft and serve. The explore pillar of Euranova is an independent research centre dedicated to data science, software engineering and AI.
Through the exploration of tomorrow’s engineering and data science to answer today’s problems, our research centre is dedicated to anticipating the challenges that European businesses face. We find solutions to current and future digital challenges with passion, creativity and integrity.
A Combined Rule-Based and Machine Learning Approach for Automated GDPR Compliance Checking
The General Data Protection Regulation (GDPR) requires data controllers to implement end-to-end compliance. Controllers must therefore ensure that the terms agreed with the data subject and their own obligations under GDPR are respected in the data flows from data subject to controllers, processors and sub-processors (i.e. data supply chain).
Our Research Director Is Co-chair at DEBS 2021 [Call for Paper]
Congratulations to our research director Sabri Skhiri on his appointment as industry co-chair of the international conference on distributed and event-based systems.
DMMM: Data Management Maturity Model
The assessment of the digital transformation progress is essential to understand and undertake in order to evaluate the level of maturity of data-driven companies in terms of data capabilities and to plan for improvement actions.
A Survey of Maturity Models in Data Management
Maturity models are helpful business tools that refine and develop how organizations conduct their businesses and benchmark their maturity status against a scale or with industry peers. They serve to prioritize the actions for improvement better and control the progress in reaching the target maturity stage.
MIC: Multi-view Image Classifier using Generative Adversarial Networks for Missing Data Imputation
In this paper, we propose a framework for image classification tasks, named MIC, that takes as input multi-view images, such as RGB-T images for surveillance purposes. We combine auto-encoder and generative adversarial network architectures to ensure the multi-view embedding in a common latent space.
MDPI Data Journal, special issue – Paper Submission Opening
After the success of five international workshops co-located at IEEE Big Data, the MDPI Data Journal is dedicating a special issue to real-time stream analytics, stream mining, CER/CEP and stream data management in big data.
Towards a Continuous Evaluation of Calibration
For safety-critical systems involving AI components (such as in planes, cars, or healthcare), safety and associated certification tasks are one of the main challenges, which can become costly and difficult to address.
One key aspect is to ensure that the decisions a machine-learning classifier makes are properly calibrated.
Reinforcement Learning Course at ENSI
Reinforcement learning is one of the most active research areas in artificial intelligence and applies to a wide range of use cases in different sectors. To provide students with the skills needed in a transforming AI landscape, the ENSI school invited us to give a course on the subject.
Padhoc: a Computational Pipeline for Pathway Reconstruction On The Fly
Molecular pathway databases represent cellular processes in a structured and standardized way. These databases support the community-wide utilization of pathway information in biological research and the computational analysis of high-throughput biochemical data. We present Padhoc, a pipeline for pathway ad hoc reconstruction.
2Be3-Net : Combining 2D and 3D convolutional neural networks for 3D PET scans predictions
Radiomics is the main approach used to develop predictive models based on 3D Positron Emission Tomography (PET) scans of patients suffering from cancer. We propose a deep learning architecture associating a 2D feature extractor to a 3D CNN predictor.
Talking Graph Analytics With Students
Last Saturday, our Tunisian team Safa, Ichraf Hamza and Amine took part in the ENSI (Ecole Nationale des Sciences de l’Informatique) virtual forum to share their experience and meet the students! Our graph specialist Amine Ghrab talked to students about the power of graph analytics.
INTERNSHIPS 2021
This document presents internships supervised by our software engineering department or by our research & development department. Each project is an opportunity to feel both empowered and responsible for your own professional development and for your contribution to the company.