Big Data Technologies and Management

Developments in information technology and its prevalent growth in several areas of business, engineering, medical, and scientific studies are resulting in information as well as data explosion. Knowledge discovery and decision making from such rapidly growing voluminous data are a challenging task in terms of data organization and processing, which is an emerging trend known as big data computing. Big data has gained much attention from the academia and the IT industry. A new paradigm that combines large-scale compute, new data-intensive techniques, and mathematical models to build data analytics. Thus, this chapter discusses the background of big data. It also discusses the various application of big data in detail. The various related work and the future direction would be addressed in this chapter.

Knowledge Mapping of Green Technology Visualization with Bibliometric Tools

An increasing number of research literature studies about green technology have been accepted by journals of different disciplines due to the rapid technical progress and innovation in all types of industry. This study uses bibliometric tools of CiteSpace and VOSviewer to analyse the key authors’ co-citation network, institution cooperation, the keyword clusters of green technology, and the evolution trend of green technology. We find that since 1960s, the number of research papers with green technology theme has been growing. These papers are mainly involved in fields of economics and business, engineering, and chemistry, in which there exist 13 highest cited papers from 2009 to 2020 based on the Web of Science database. In this study, we find the top 20 journals of green technology with the parameter of literature count and centrality. We find that the cooperation of authors is quite weak with the co-authorship analysis, and we obtain the top 12 institutions in 15 countries dominantly in green technology research through country and institution analysis. We conduct a cluster analysis of keywords related to green technology, and we obtain 10 clusters, with three economical clusters, five engineering clusters, and two chemical clusters. Finally, we summarise green technology with the aid of the timeline view function of CiteSpace system.

Artificial Neural Networks and Its Application in Various Fields of Study

Artificial Neural Networks (ANN) approach is an alternate way to classical methods. As a computation and learning paradigm, the approach is used to solve complicated practical problems in numerous fields, such as accounting and business, engineering, medical and healthcare, geological and energy. The application varies from modelling, identification, prediction, and forecasting. In contrast to conventional procedure, ANN is trained using data exemplifying the behaviour of a system. This paper presents applications of ANN in various fields of study. The applications are in the form of designing and modelling, identification and evaluation, and prediction and control. Published literature presented in this study serves as evidence that ANN is a useful tool in various disciplines across many industries. This paper will encourage researchers and professionals to explore ANN.

BUSINESS ENGINEERING IN CANADA AS A TOOL FOR ADAPTATION TO THE NEW REALITY UNDER THE CONDITIONS OF COVID-19

The aim of the work is to analyze the current mechanisms of adaptation of innovative processes in Canadian corporations in the context of COVID-19 by demonstrating technologies and approaches that can be applied to solve modern problems. The authors analyzed the statistical material, evaluated the changes in modern information technologies used to attract potential consumers. Methods of observation, analysis, generalization and interpretation of the results were used in the study. The analysis has shown that the Internet remains the most dynamically growing segment of the market for promoting products and services. It has been revealed that innovations can become the link in the company that will help it survive the crisis and open up opportunities for stating, analyzing and testing new processes. The results of the study strongly prove that the use of new technologies and openness to innovation can be a decisive factor for outperforming competitors in the future.

An Applied Mathematical Model for Business Transformation and Enterprise Architecture

This chapter on an optimal and adaptable enterprise architecture for business systems is one of a series of research chapters on enterprise architecture and business transformations. This one is about estimating the risk for transforming a business environment. It is a conclusion of many years of research, architecture, consulting, and development efforts. The model is based on an applied holistic mathematical model (AHMM) for business transformations. In this chapter, the CSFs are tuned to support the intelligent architecture concepts for business integration in the form of an applied pattern that is also a part or a chapter in this research series. This chapter is related to the feasibility and prototype of the business engineering and risk management pattern (BE&RMP) that should (or shouldn't) prove whether business transformation projects can optimize enterprise business capabilities and how microartefact implementation can offer a sustainable enterprise business system.