The success of many companies through the assistance of bitcoin proves that technology continually dominates and transforms how economics operate. However, a deeper, more conceptual understanding of how these technologies work to identify innovation opportunities and how to successfully thrive in an increasingly competitive environment is needed for the entrepreneurs of tomorrow. Transforming Businesses With Bitcoin Mining and Blockchain Applications provides innovative insights into IT infrastructure and emerging trends in the realm of digital business technologies. This publication analyzes and extracts information from Bitcoin networks and provides the necessary steps to designing open blockchain. Highlighting topics that include financial markets, risk management, and smart technologies, the research contained within the title is ideal for entrepreneurs, business professionals, managers, executives, academicians, researchers, and business students.

BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 1 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more.

The development of information technology in supply chains has shown that this digital revolution can be a source of performance for enterprises and governments. Among these technologies is blockchain. The application of blockchains in cryptocurrency reduces information security risks and eliminates several processing and transaction fees and allows countries with volatile currencies to have a more stable currency. Blockchain Applications in Cryptocurrency for Technological Evolution features a collection of contributions related to the application of blockchain technology in cryptocurrency. It further explains the ways in which these applications have affected the industry. Covering topics such as crypto mining attacks, data processing architecture, and purchase power, this premier reference source is an excellent resource for business leaders and executives, IT managers, logistics specialists, students and faculty of higher education, librarians, researchers, and academicians.

Continual advancements in web technology have highlighted the need for formatted systems that computers can utilize to easily read and sift through the hundreds of thousands of data points across the internet. Therefore, having the most relevant data in the least amount of time to optimize the productivity of users becomes a priority. Semantic Web Science and Real-World Applications provides emerging research exploring the theoretical and practical aspects of semantic web science and real-world applications within the area of big data. Featuring coverage on a broad range of topics such as artificial intelligence, social media monitoring, and microblogging recommendation systems, this book is ideally designed for IT consultants, academics, professionals, and researchers of web science seeking the current developments, requirements and standards, and technology spaces presented across academia and industries.

Big Data Analytics and Its Impact on Basin Water Agreements and International Water Law represents the state of the art when it comes to the use of disruptive technologies in the transboundary water context and its impact on international water law. Indeed, the case study provided in this manuscript which represents the most relevant example where big data is being used in the transboundary water context highlights this reality. The readers will understand current and also future potential impact of big data on water resources in the general context of disruptive technologies.

Big Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time. In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data. The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting. The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.

The internet of things (IoT) is quickly growing into a large industry with a huge economic impact expected in the near future. However, the users' needs go beyond the existing web-like services, which do not provide satisfactory intelligence levels. Ambient intelligence services in IoT environments is an emerging research area that can change the way that technology and services are perceived by the users. Ambient Intelligence Services in IoT Environments: Emerging Research and Opportunities is a unique source that systemizes recent trends and advances for service development with such key technological enablers of modern ICT as ambient intelligence, IoT, web of things, and cyber-physical systems. The considered concepts and models are presented using a smart spaces approach with a particular focus on the Smart-M3 platform, which is now shaping into an open source technology for creating ontology-based smart spaces and is shifting towards the development of web of things applications and socio-cyber-physical systems. Containing coverage on a broad range of topics such as fog computing, smart environments, and virtual reality, multitudes of researchers, students, academicians, and professionals will benefit from this timely reference.

Information Security and Ethics: Social and Organizational Issues brings together examples of the latest research from a number of international scholars addressing a wide range of issues significant to this important and growing field of study. These issues are relevant to the wider society, as well as to the individual, citizen, educator, student and industry professional. With individual chapters focusing on areas including web accessibility; the digital divide; youth protection and surveillance; Information security; education; ethics in the Information professions and Internet voting; this book provides an invaluable resource for students, scholars and professionals currently working in information Technology related areas.

With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail in the way of better success. Such limitations as the lack of better methods, deeper understanding of problems, and advanced tools are hindering progress. Challenges and Applications of Data Analytics in Social Perspectives provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The content within this publication examines topics that include collaborative filtering, data visualization, and edge computing. It provides research ideal for data scientists, data analysts, IT specialists, website designers, e-commerce professionals, government officials, software engineers, social media analysts, industry professionals, academicians, researchers, and students.