Navigating the AI transition in marketing
IEEE Big Data 2025: the shift from scale to smart
IEEE Big Data 2025 signals a shift to secure, hybrid intelligence. CTO Sabri Skhiri unpacks the engineering reality from the conference: the practical shift to embeddings, the real need for security layers, and the limitations of AI agents in production.
Researchers get insights in minutes instead of hours
An international pharmaceutical company sought to unlock the value of its data but lacked the expertise to build a fast, tailored platform, critical for reducing experiment processing from a full day to under an hour.
Unlocking 25 Years of R&D data through graph visualization
A global oil and gas company’s R&D hub amassed decades of complex materials and chemical data, but limited data expertise made it hard for scientists to access, explore, and extract meaningful insights from these siloed datasets
Turning retail data into actionable insights
Centralizing data on the Google Cloud Platform (GCP), an international retailer is transforming complex analytics into trusted, production-ready insights—empowering business teams to measure what really matters.
Navigating the future of AI governance
Beyond the obligation for high risk systems to conduct a data protection impact assessment, the value added of impact assessments for IA models would rely on the risks covered, the economic dimension of such assessment and its interest in terms of IA governance. The panel at CPDP conference discuss the content and benefits of user-centric impact assessments for IA across the value chain.
Tech Insights from the PET Summit 2024
The recent Privacy Enhancing Technologies (PETs) London Conference convened stakeholders across Legal, IT, and various business domains to delve into the evolving data privacy and security landscape. Here, we distil the key themes and insights shared during the conference, shedding light on the interesting talks.
SANGEA: Scalable and Attributed Network Generation
In this paper, we present SANGEA, a sizeable synthetic graph generation framework that extends the applicability of any SGG to large graphs. By first splitting the large graph into communities, SANGEA trains one SGG per community, then links the community graphs back together to create a synthetic large graph.
Tech Insights from the PET Summit 23
In March 2023, our research director Sabri Skhiri travelled to London to attend the Privacy Enhancing Technologies Summit 2023, dedicated to PETs and their uses (enhance data security, facilitate compliance, and create value).
Tech Insights from the PET Summit 2022
In April 2022, our research director Sabri Skhiri travelled to Zurich to attend the Privacy Enhancing Technologies Summit 2022, dedicated to PETs and their uses (enhance data security, facilitate compliance, and create value). In this article, he gives you his opinion about PETs’s’ big trends, and a selection of his favourite talks.
Anomaly Detection: How to Artificially Increase your F1-Score with a Biased Evaluation Protocol
Anomaly detection is a widely explored domain in machine learning. Many models are proposed in the literature, and compared through different metrics measured on various datasets. The most popular metrics used to compare performances are F1-score, AUC and AVPR...
Webinar - Improve Traffic Flow with AI
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).
Data driven- IT operations in banking
A Belgian bank sought to become data‑driven by unifying fragmented IT data, automating reporting, and building secure pipelines to deliver reliable KPIs, faster decisions, and GDPR‑compliant insights across its IT operations.
TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes
In this paper, we introduce TopoGraph, an end-to-end framework for building and analyzing graph cubes. TopoGraph extends the existing graph cube models by defining new types of dimensions and measures and organizing them within a multidimensional space that guarantees multidimensional integrity constraints.
Tech Insights from Flink Forward 2019
Early October 2019, 6 EURA NOVA engineers travelled to Berlin to attend the Flink Forward Conference, dedicated to Apache Flink users and stream processing communities.
Data Mining and ML Techniques Supporting TBS Concept Deployment
The paper presents two methods to allow air traffic controllers to deliver separation minima accurately and safely, on the basis of time intervals instead of distances.
Graph BI & Analytics: Current State and Future Challenges
The paper presents the state of the art of graph BI & analytics, with a focus on graph warehousing. We survey the topics of graph modelling, management, querying, and processing in graph warehouses.
An analytics-aware conceptual model for evolving graphs
Graphs are ubiquitous data structures commonly used to represent highly connected data. Many real-world applications, such as social and biological networks, are modeled as graphs. To answer the surge