Multivariate Time Series Anomaly Detection in Industry 5.0

Optimizing Industry 5.0 Machine Learning-Based Applications via Synthetic Data Generation

Efficient Data Dissemination via Semantic Filtering at the Tactical Edge

Multi-objective scheduling and resource allocation of Kubernetes replicas across the compute continuum

Enabling Big Data and Machine Learning Applications in High-Stakes Environments

The Evolution of Kubernetes Management: Introducing the KubeTwin Framework

Kubernetes Enhanced Stateful Service Migration for ML-Driven Applications in Industry 4.0 Scenarios

A Machine Learning Operations Platform for Streamlined Model Serving in Industry 5.0

Efficient Microservice Deployment in Kubernetes Multi-Clusters through Reinforcement Learning

VOICE: Value-of-Information for Compute Continuum Ecosystems

RoamML Platform: Enabling Distributed Continual Learning for Disaster Relief Operations

TELKA: Twin-Enhanced Learning for Kubernetes Applications

Using Simulation and DT to Improve Performance of Systems Operation Modeling

RoamML: Distributed Machine Learning at the Tactical Edge

Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments

Adaptive Information Dissemination Over Tactical Edge Networks

Characterization of Microservice Response Time in Kubernetes: A Mixture Density Network Approach

A Data Mesh Approach for Enabling Data-Centric Applications at the Tactical Edge

A Systematic Mapping Study of Italian Research on Workflows

Modeling Digital Twins of Kubernetes-Based Applications