In the rapidly evolving landscape of IoT-Edge-Cloud continuum (IECC), effective management of computational tasks offloaded from mobile devices to edge nodes is crucial.

The term ‘Dynamic Heterogeneous Computing Sphere’ is used to describe a computing paradigm that is heterogeneous, volatile and highly dynamic.

The feature extraction capabilities of Machine Learning (ML) models have led to their wide adoption in a large variety of sectors: from anomaly detection for machinery, to user clustering and behavioral prediction, market trends predictions, or the analysis of text, sound, and image data.

In the world of finance, where every decision can have significant ramifications, the possibility of predicting market movements is invaluable. Traditionally, analysts have relied on a combination of data analysis, market trends, and expert insights to make informed predictions.

Due to its multivariate and multipurpose use and reuse, data’s worth is dramatically increasing, leading to an era characterized by the generation of data marketplaces towards accessing, selling, sharing, and trading data and data assets.

The report “NEM Vision 2030 Towards a future media ecosystem” describes the NEM approach about the European media and creative industries sectors for the next decade.