Artificial intelligence operations can place different demands on storage during training, inference etc. We look at NAS, SAN and object storage for AI and how to balance them for AI projects.

In the realm of data modeling, many-to-many relationships are often considered an "odd duck." Unlike one-to-one or one-to-many relationships, which can be directly implemented in physical database ...

Open source in-process OLAP system launches rival to Iceberg and Delta Lake table format, and more With a combined market ...

An axiomatic foundation for object-oriented data modeling and efficient algorithms for checking whether a given data model satisfies the axioms are presented. The data model is the foundation for a ...

Amazon Web Services (AWS) strives to deliver reliable services that customers can trust completely. This requires maintaining ...

A groundbreaking new project is underway to make interspecies communication a reality. Google DeepMind, in collaboration with ...

In this paper, the data model is formally defined and a nonredundancy preserving primitive operator, the merge, is described. It is proven that nonredundancy is always preserved in the model. An ...

Better data annotation—more accurate, detailed or contextually rich—can drastically improve an AI system’s performance, ...

Today, companies require more efficient methods to manage and interpret the vast amounts of data that support strategic ...

It is a pretty bold claim for anyone to call themselves the creator of the operating system for AI. Many have done it, but ...