AI-driven systems must manage data across millions of devices, study finds
As companies deploy sensors and computing power everywhere—from factories to cities—managing that flood of information requires new machine learning approaches. A new survey identifies critical gaps in how businesses can intelligently handle, secure, and optimize data flowing between cloud centers and edge devices.
Originaltitel: Learning-driven Data Fabric Trends and Challenges for cloud-to-thing continuum
<p>This special issue is a collection of emerging trends and challenges in applying learning-driven approaches to data fabric architectures within the cloud-to-thing continuum. As data generation and processing increasingly occur at the edge, there is a growing need for intelligent, adaptive data management solutions that seamlessly operate across distributed environments. In this special issue, we received research contributions from various groups around the world. We chose the eight most appropriate and novel contributions to include in this special issue. These eight contributions were further categorized into three themes: Data Handling approaches, resource optimization and management, and security and attacks. Additionally, this editorial suggests future research directions that will potentially lead to groundbreaking insights, which could pave the way for a new era of learning techniques in Data Fabric and the Cloud-to-Thing Continuum.</p>