Several fundamental changes in technology indicate domain-specific hardware and
software co-design is the only path left. In this context, architecture, system, data …

The complexity and diversity of big data and AI workloads make understanding them difficult
and challenging. This paper proposes a new approachto modelling and characterizing big …

In this era of big and fast data, software architects tend to find it really hard to make
consistent decisions about which architecture and technologies are ideal for a certain …

Distributed big data processing and analytics applications demand a comprehensive end-to-
end architecture stack consisting of big data technologies. However, there are many …

Today, huge amounts of data are being collected in many areas, which create new
opportunities to understand phenomena in meteorology, health, finance, and many other …

Today's state-of-the-art Big data analytics engines handle masses of data, but will reach to
their limits, as the future Big data flood is predicted to still grow with an increasing speed …

In recent years, there has been a convergence of Big Data (BD), High Performance
Computing (HPC), and Machine Learning (ML) systems. This convergence is due to the …

Big data systems address the challenges of capturing, storing, managing, analyzing, and
visualizing big data. Within this context, developing benchmarks to evaluate and compare …

Most AI projects start with a Python notebook running on a single laptop; however, one
usually needs to go through a mountain of pains to scale it to handle larger dataset (for both …

Everyone loves to talk about big data, of course for various reasons. We got into that
discussion when it seemed that there is a serious problem that big data is throwing down to …