Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Nowadays, big data lakes are prominent components of emerging big data architectures.
Basically, big data lakes are the natural evolution of data warehousing systems in the big …

In this work, we propose a unified abstraction for graph algorithms: the Extended General
Einsums language, or EDGE. The EDGE language expresses graph algorithms in the …

In many applications of graph processing, the input data is often generated from an
underlying geometric point data set. However, existing high-performance graph processing …

Big data lakes are now important components of emerging big data architectures. Big data
lakes are essentially the natural evolution of data warehousing systems in the big data …

To address the challenges of big data analytics, several works have focused on big data
optimization using metaheuristics. The constraint satisfaction problem (CSP) is a …

Due to the limitations of cloud computing on latency, bandwidth and data confidentiality,
edge computing has emerged as a novel location-aware way to provide the capacity …

Serverless computing offers an affordable and easy way to code lightweight functions that
can be invoked based on some events to perform simple tasks. For more complicated …

Graphs are found in a plethora of domains, including online social networks, the World Wide
Web and the study of epidemics, to name a few. With the advent of greater volumes of …

Given the evolving character of Big Data, a new kind of way to manage data has become a
requisite. The domain had a growing interest in recent years and has been, therefore …