Editorial for Special issue of FGCS special issue on “Benchmarking big data systems”

© 2019 Even though several big data processing and analytics systems have been introduced with various design architectures, we are still lacking a deeper understanding of the performance characteristics for the various design architectures in addition to lacking comprehensive benchmarks for the various Big Data platforms. There is a crucial need to conduct fundamental research with a more comprehensive performance evaluation for the various Big Data processing systems and architectures. We also lack the availability of validation tools, standard benchmarks, and system performance prediction methods that can help us have a deeper and more solid understanding of the strengths and weaknesses of the various Big Data processing platforms. This special issue is dedicated to original results and achievements by active researchers, designers, and developers working on various issues and challenges related to big data research

Big Data: development, advancement and implementation organizations in information age

With the advancement of the information age and knowledge, new techniques have been designed to allow the development, creation and evolution of strategies, tools and applications that facilitate internal and external actions of organizations. The purpose of this article is to show the results of a literature review of one of these techniques: Big Data. The methodology consisted of a review of the Scopus database throughout the last two years (2014-2016). Based on the findings, the importance, advantages and disadvantages of this application are discussed. It was clear that the use of Big Data is an important technique that contributes to handling large volumes of information and is used in the field of technology management in organizations; it confirmed the existence of organizations who, thanks to the implementation and use of Big Data, have managed to increase their competitiveness over other organization. Big Data has allowed them to reduce costs in operations, to ease processes, to design new business models and to take organizational decisions that have become more difficult to take because the volume of information increases constantly