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 …

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

For the architecture community, reasonable simulation time is a strong requirement in
addition to performance data accuracy. However, emerging big data and AI workloads are …

We study many Big Data applications from a variety of research and commercial areas and
suggest a set of characteristic features and possible kernel benchmarks that stress those …

One of the major applications of future generation parallel and distributed systems is in big-
data analytics. Data repositories for such applications currently exceed exabytes and are …

Context: Architectural patterns are well-proven solutions to solve software systems issues
related to quality attributes. They define a set of high level structure properties, a vocabulary …

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 …

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 …

Abstract “Big data” has become a major force of innovation across enterprises of all sizes.
New platforms with increasingly more features for managing big datasets are being …

MEME (Multiple Em for Motif Elicitation) is the most commonly used tool to identify motifs
within deoxyribonucleic acid (DNA) or protein sequences. However, the results generated …