When there is a need to make an ultimate decision about the unique features of big data
platforms, one should note that they have configurable parameters. Apache Spark is an …

Choosing the right configuration for Spark deployed in the public cloud to ensure the
efficient running of periodic jobs is hard, because there can be a huge configuration space …

Abstract Big Data Optimization is the term used to refer to optimization problems which have
to manage very large amounts of data. In this paper, we focus on the parallelization of …

The data processing platforms make use of distributed systems to process and store the big
data efficiently. These big data platforms have more than hundreds of configurable …

Multi-objective metaheuristics have become popular techniques for dealing with complex
optimization problems composed of a number of conflicting functions. Nowadays, we are in …

The widespread usage of cloud computing in different fields causes many challenges as
resource scheduling, load balancing, power consumption, and security. To achieve a high …

Due to the rapid growth of production and dissemination of big data from various sources,
the speed of data processing must inevitably increase. In distributed big data processing …

A microarray is a revolutionary tool that generates vast volumes of data that describe the
expression profiles of genes under investigation that can be qualified as Big Data. Hadoop …

Edge computing is a popular computing modality that works by placing computing resources
as close as possible to the sensor data to relieve the burden of network bandwidth and data …

Efficient management of big data becomes challenging in recent decades. Online Feature
Selection (OFS) is one type of online learning in contrast to batch learning, allowing a …