Bøger i Studies in Big Data serien

This book has a multidisciplinary approach to Metaverse studies and the relevance of Metaverse with the current and popular topics that concern society and how it will change them in the future. In addition, academic texts are included since not much scientific content is available in this field. In short, there are sections in the book that everyone will find useful. Most importantly, the topics are grouped under four main parts. The first is the Introduction, where the main issues are explained. In the second section¿Technical Topics, the technological infrastructure of the subject is explained, followed by section three where the social and human dimensions of Metaverse are explained. The fourth and final section is on Industrial Applications.

This book discusses integration of internet of things (IoT), cloud computing, and big data. It presents a unique platform where IoT, cloud computing, and big data are fused together and can be foreseen as a perfect solution to many applications. Usually, IoT, cloud computing, and big data are researched separately on the basis of their properties, underlying technologies, and other open issues. Integration of IoT, cloud computing and big data is not that easy and can face key open issues like standardization of interfaces, power and energy efficiency in both data processing and transmission, security and privacy, storage mechanisms for future applications, scalability and flexibility, and QoS provisioning for end user applications. Integration of IoT, cloud computing, and big data represents the next big rise for future industry and business applications. This integration opens new exhilarating directions for research and it is discussed in this book.

This book highlights applied artificial intelligence techniques, tools, and systems to drive strategic advantages, improve operational efficiency, and create added value. The focus is very much on practical applications and how to maximize the value of these technologies. They are being applied across businesses to enhance innovation, improve performance, increase profit, support critical thinking, and ultimately create customer-added value. Whether you are a researcher, manager, or decision-maker, this book provides valuable insights to help you harness the power of AI and big data analytics in your organization. This book attempts to provide answers to the most important questions: Quo Vadis applied artificial intelligence?Quo Vadis cutting-edge business technologies?

The combination of DEA and ratio analysis is introduced as a suitable field for evaluating the performance of DMUs. In this regard, DEA-R is also proposed as a hybrid technique for calculating efficiency, ranking DMUs, and finding efficient faces. Therefore, the relationship between DEA and DEA-R provides a suitable field for researchers in the field of evaluating the performance of DMUs. The audience of this book is not limited to researchers in mathematics fields, but experts and students in industrial engineering and management fields also benefit from the topics of this book.

What changes have affected the definition of the boundaries of journalism in the last decade? How do technologies influence the boundaries of journalism? Are threats and opportunities identified in those blurred areas of journalism? The aim of this book is to answer these questions and to address, from different perspectives, the redefinition of the boundaries of journalism according to the most recent changes in digital media concerning actors, models, and practices.More than 40 authors from eleven countries contribute to this book, which is structured into six sections to analyze the principles of journalism today, sustainability strategies in the digital context, old and new actors, formats and narratives, adaptation to the mobile scenario and to social platforms, and the changes introduced by artificial intelligence. Undoubtedly, this book is of interest to both academics and professionals, as well as a crucial reference for scholars and students of media and journalism.Chapter 7 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Search and navigation in hyperlinked networks have been subjects of research since the Internet emerged. Due to its incompleteness in terms of linking related content, the existing linking structure of the Web and similar networks cannot be utilized as a searchable index without prior application of suitable crawling strategies and content categorization. Following the example of sitemaps, a map-like extension to the existing link structure of the network is proposed that creates additional contextual links. For this, a concept and algorithms are devised that allow the creation of contextual cluster files, to which documents are assigned and between which semantically relevant links are established. The resulting WebMap covers all searchable resources on the original network in a contextual overlay network and enables new search and navigation approaches.

This book focuses in detail on data science and data analysis and emphasizes the importance of data engineering and data management in the design of big data applications. The author uses patterns discovered in a collection of big data applications to provide design principles for hypothesis generation, integrating big data processing and management, machine learning and data mining techniques.The book proposes and explains innovative principles for interpreting hypotheses by integrating micro-explanations (those based on the explanation of analytical models and individual decisions within them) with macro-explanations (those based on applied processes and model generation). Practical case studies are used to demonstrate how hypothesis-generation and -interpretation technologies work. These are based on ¿social infrastructure¿ applications like in-bound tourism, disaster management, lunar and planetary exploration, and treatment of infectious diseases.The novel methods and technologies proposed in Hypothesis Generation and Interpretation are supported by the incorporation of historical perspectives on science and an emphasis on the origin and development of the ideas behind their design principles and patterns. Academic investigators and practitioners working on the further development and application of hypothesis generation and interpretation in big data computing, with backgrounds in data science and engineering, or the study of problem solving and scientific methods or who employ those ideas in fields like machine learning will find this book of considerable interest.