Constructing energy-efficient database systems to reduce economic costs and
environmental impact has been studied for 10 years. With the emergence of the big data …

The paper narrates the review of cost-based query optimizers designed using database
strategies, deterministic, stochastic, hybrid and energy efficiency-based techniques. It was …

As big data becomes the norm of various industrial applications, the complexity of database
workloads and database system design has increased significantly. To address these …

We propose a generalization of the classical database query optimization problem: multi-
objective parametric query (MPQ) optimization. MPQ compares alternative processing plans …

Obviously, the complexity of mathematical database cost models increases with the
evolution of the database technology brought by emerging hardware and the new …

Traditional database systems result in high energy consumption and low energy efficiency
due to the lack of consideration of energy issues and environmental adaptation in the design …

In the presence of exponential growth of the data produced every day in volume, velocity,
and variety, online analytical processing (OLAP) is becoming increasingly challenging …

The goal of multi-objective query optimization (MOQO) is to find query plans that realize a
good compromise between conflicting objectives such as minimizing execution time and …

The ever-increasing demand for scalable database systems is limited by their energy
consumption, which is one of the major challenges in research today. While existing …

During the last few years, we are witnessing increasing development in the Internet of
Things (IoT) and big data. To address increasing workload complexity with better …