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.

Sustaining a competitive edge in today's business world requires innovative approaches to product, service, and management systems design and performance. Advances in computing technologies have presented managers with additional challenges as well as further opportunities to enhance their business models. Software Engineering for Enterprise System Agility: Emerging Research and Opportunities is a collection of innovative research that identifies the critical technological and management factors in ensuring the agility of business systems and investigates process improvement and optimization through software development. Featuring coverage on a broad range of topics such as business architecture, cloud computing, and agility patterns, this publication is ideally designed for business managers, business professionals, software developers, academicians, researchers, and upper-level students interested in current research on strategies for improving the flexibility and agility of businesses and their systems.

Today, network technology is ubiquitous. Whether at home or on the move, at work or at play, the modern data network is a part of our daily lives. Streaming video, social media and web browsing are just a few of the popular applications that rely on the network, and this list will continue to grow with autonomous vehicles, virtual reality and others, each with their own unique needs. To address the challenges of the demand for these services, the network must continually evolve with new technologies. However, determining which technologies are worth focusing on today is difficult, and the issues which they represent, and address are often complex. In Network Horizons Emerging Technologies and Applications 2018 - 2019 Edition, the author highlights key areas of interest for network technology, helping the reader to identify those of the highest importance by explaining the what, why and when of each of these important areas of development to make sure they and their business are prepared for the future.

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.

Technological development is achievable only when a country has the ability to systematically design and introduce its own new technologies. In spite of the variety of studies regarding technology management, there is still a lack of studies concerning the principle concepts of technology management in the Middle Eastern/North African (MENA) region's firms. The generally low level of ICT diffusion in most of the region's countries widens the gap between MENA countries and the modern world. Private Sector Innovations and Technological Growth in the MENA Region provides innovative insights into investments made for the digital transformation of major cities in the region that have the potential to become a significant driver for economic development and job creation. Highlighting topics such as strategic planning, risk analysis, and customer loyalty, this publication is designed for policymakers, economists, academicians, researchers, business professionals, and students interested in the use of ICT integration for the advancement of the MENA region.

In the digital era, novel applications and techniques in the realm of computer science are increasing constantly. These innovations have led to new techniques and developments in the field of cybernetics. The Handbook of Research on Applied Cybernetics and Systems Science is an authoritative reference publication for the latest scholarly information on complex concepts of more adaptive and self-regulating systems. Featuring exhaustive coverage on a variety of topics such as infectious disease modeling, clinical imaging, and computational modeling, this publication is an ideal source for researchers and students in the field of computer science seeking emerging trends in computer science and computational mathematics.

This book is a general introduction to the statistical analysis of networks, and can serve both as a research monograph and as a textbook. Numerous fundamental tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, graph-based semi-supervised learning and sampling in networks. The description of these concepts is self-contained, with both theoretical justifications and applications provided for the presented algorithms.Researchers, including postgraduate students, working in the area of network science, complex network analysis, or social network analysis, will find up-to-date statistical methods relevant to their research tasks. This book can also serve as textbook material for courses related to thestatistical approach to the analysis of complex networks.In general, the chapters are fairly independent and self-supporting, and the book could be used for course composition "a la carte". Nevertheless, Chapter 2 is needed to a certain degree for all parts of the book. It is also recommended to read Chapter 4 before reading Chapters 5 and 6, but this is not absolutely necessary. Reading Chapter 3 can also be helpful before reading Chapters 5 and 7. As prerequisites for reading this book, a basic knowledge in probability, linear algebra and elementary notions of graph theory is advised. Appendices describing required notions from the above mentioned disciplines have been added to help readers gain further understanding.