Florin Pop (editor) - Big Data Platforms and Applications: Case Studies, Methods, Techniques, and Performance Evaluation (Computer Communications and Networks)

The value of Big Data applications and their supporting infrastructure like Cloud/Fog/Edge systems lies in the fact that end-users always operate in a specific context: their role, intentions, locations, data handled, and working environment constantly change. According to the research perspective, the Big Data challenges include fundamental research and innovation problems addressing the efficiency, scalability, and responsiveness of analytics services, such as machine learning, language understanding, data mining, visualization, privacy-aware application, etc. The existing platforms create an ecosystem based on the convergence of Big Data and Cloud/Edge computing technologies, sometimes combined with HPC for advanced analytics, that in connection with the Internet of Things capabilities enable a wide range of innovations in such sectors as e-learning, healthcare, digitalization, manufacturing, energy, natural resource monitoring, finance and insurance, agri-food, space, and security. In this context, our book, coverage several models and use-cases that are strongly correlated with Big Data challenges.

The book provides, in this sense, an excellent venue for the dissemination of research efforts, analysis, implementation, and final results for Big Data platforms and applications being oriented on case studies, methods, techniques, and performance evaluation, being a flagship driver toward presenting and supporting advance research in the area of Big Data platforms and applications. We are convinced that all authors highlight the results obtained in their research projects and in collaboration with various researchers and practitioners. In the case that the presented work is an extension of already published results, we are more than happy to include the new results in our project.

Chapter , Data Center for Smart Cities: Energy and Sustainability Issue, exploiting available dataset recorded in ENEA Data Center (DC), proposes methodologies for energy efficiency evaluation of DCs using appropriate energy and productivity metrics, namely Energy Waste Ratio (EWR) and Data Center Energy Productivity (DCeP). Furthermore, the paper discusses sustainability requirements in the smart city context and evaluate energy productivity at different granularity levels: individual jobs, queues, and DC cluster.

Chapter , Apache Spark for Digitalization, Analysis and Optimization of Discrete Manufacturing Processes, presents digitalization of assembly processes using the latest technologies, analysis of data generated by the monitoring sensors using big data technologies, and optimization of the manufacturing processes by and discuss the research challenges in identifying the steps that have the highest impact on the final output. The main goal is the analysis of the discrete manufacturing processes and more specifically the analysis of the products that are the outcome of the manufacturing processes using as illustrative use case the manufacturing of the regulators in Emerson factory.

Chapter , An Empirical Study on Teleworking among Slovakias Office-Based Academics, investigates the attitudes and viewpoints of potential teleworkers toward the possibility of introducing teleworking in universities. Moreover, the paper outline some of the key issues related to the implementation of teleworking among office-based academics from a Slovakian perspective. The study makes a significant contribution to a limited collection of empirical research on telecommuting practices at universities and also guides institutions in refining and/or redefining future teleworking strategies or programs.