This book provides information on data-driven infrastructure design, analytical approaches, and technological solutions with case studies for smart cities. This book aims to attract works on multidisciplinary research spanning across the computer science and engineering, environmental studies, services, urban planning and development, social sciences and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative solutions and big data-powered applications to cope with the real world challenges for building smart cities.

Corporations and governmental agencies of all sizes are embracing a new generation of enterprise-scale business intelligence (BI) and data warehousing (DW), and very often appoint a single senior-level individual to serve as the Enterprise BI/DW Program Manager. This book is the essential guide to the incremental and iterative build-out of a successful enterprise-scale BI/DW program comprised of multiple underlying projects, and what the Enterprise Program Manager must successfully accomplish to orchestrate the many moving parts in the quest for true enterprise-scale business intelligence and data warehousing. Author Alan Simon has served as an enterprise business intelligence and data warehousing program management advisor to many of his clients, and spent an entire year with a single client as the adjunct consulting director for a $10 million enterprise data warehousing (EDW) initiative. He brings a wealth of knowledge about best practices, risk management, organizational culture alignment, and other Critical Success Factors (CSFs) to the discipline of enterprise-scale business intelligence and data warehousing.

The purpose of this book is to review the recent advances in E-health technologies and applications. In particular, the book investigates the recent advancements in physical design of medical devices, signal processing and emergent wireless technologies for E-health. In a second part, novel security and privacy solutions for IoT-based E-health applications are presented. The last part of the book is focused on applications, data mining and data analytics for E-health using artificial intelligence and cloud infrastructure. E-health has been an evolving concept since its inception, due to the numerous technologies that can be adapted to offer new innovative and efficient E-health applications. Recently, with the tremendous advancement of wireless technologies, sensors and wearable devices and software technologies, new opportunities have arisen and transformed the E-health field. Moreover, with the expansion of the Internet of Things, and the huge amount of data that connected E-health devices and applications are generating, it is also mandatory to address new challenges related to the data management, applications management and their security. Through this book, readers will be introduced to all these concepts. This book is intended for all practitioners (industrial and academic) interested in widening their knowledge in wireless communications and embedded technologies applied to E-health, cloud computing, artificial intelligence and big data for E-health applications and security issues in E-health.

"Social Data Analytics" is the first practical guide for professionals who want to employ social data for analytics and business intelligence (BI). This book provides a comprehensive overview of the technologies and platforms and shows you how to access and analyze the data. You'll explore the five major types of social data and learn from cases and platform examples to help you make the most of sentiment, behavioral, social graph, location, and rich media data. A four-step approach to the social BI process will help you access, evaluate, collaborate, and share social data with ease.

You'll learn everything you need to know to monitor social media and get an overview of the leading vendors in a crowded space of BI applications. By the end of this book, you will be well prepared for your organization s next social data analytics project.
Provides foundational understanding of new and emerging technologies-social data, collaboration, big data, advanced analyticsIncludes case studies and practical examples of success and failuresWill prepare you to lead projects and advance initiatives that will benefit you and your organization"

This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices in industry, health care, administration and business.Data science and big data go hand in hand and constitute a rapidly growing area of research and have attracted the attention of industry and business alike. The area itself has opened up promising new directions of fundamental and applied research and has led to interesting applications, especially those addressing the immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data. Data is the lifeblood of today's knowledge-driven economy.Numerous data science models are oriented towards end users and along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence with its underlying methodologies and tools helps address data analytics needs.The book is of interest to those researchers and practitioners involved in data science, Internet engineering, computational intelligence, management, operations research, and knowledge-based systems.

With the improvements of artificial intelligence, processor speeds and database sizes, the rapidly expanding field of data mining continues to provide advancing methods for managing databases and gaining knowledge.Developments in Data Extraction, Management, and Analysis is an essential collection of research on the area of data mining and analytics. Presenting the most recent perspectives on data mining subjects and current issues, this book is useful for practitioners and academics alike.

This book discusses the unique nature and complexity of fog data analytics (FDA) and develops a comprehensive taxonomy abstracted into a process model. The exponential increase in sensors and smart gadgets (collectively referred as smart devices or Internet of things (IoT) devices) has generated significant amount of heterogeneous and multimodal data, known as big data. To deal with this big data, we require efficient and effective solutions, such as data mining, data analytics and reduction to be deployed at the edge of fog devices on a cloud. Current research and development efforts generally focus on big data analytics and overlook the difficulty of facilitating fog data analytics (FDA). This book presents a model that addresses various research challenges, such as accessibility, scalability, fog nodes communication, nodal collaboration, heterogeneity, reliability, and quality of service (QoS) requirements, and includes case studies demonstrating its implementation. Focusing on FDA in IoT and requirements related to Industry 4.0, it also covers all aspects required to manage the complexity of FDA for IoT applications and also develops a comprehensive taxonomy.

Organizations with computer networks, Web sites, and employees carrying laptops and Blackberries face an array of security challenges. Among other things, they need to keep unauthorized people out of the network, thwart Web site hackers, and keep data safe from prying eyes or criminal hands. This book provides a high-level overview of these challenges and more. But it is not for the hard-core IT security engineer who works full time on networks. Instead, it is aimed at the nontechnical executive with responsibility for ensuring that information and assets stay safe and private. Written by a practicing information security officer, Philip Alexander, the book contains the latest information and arms readers with the knowledge they need to make better business decisions. Information Security: A Manager's Guide to Thwarting Data Thieves and Hackers covers the following technical issues in a nontechnical manner: -The concept of "defense in depth" -Network design -Business-continuity planning -Authentication and authorization -Providing security for your mobile work force -Hackers and the challenges they can present -Viruses, Trojans, and worms But it doesn't stop there. The book goes beyond the technical and covers highly important topics related to data security like outsourcing, contractual considerations with vendors, data privacy laws, and hiring practices. In short, Alexander gives the reader a 360-degree look at data security: What to be worried about; what to look for; the tradeoffs among cost, efficiency, and speed; what different technologies can and can't do; and how to make sure technical professionals are keeping their eyes on the right ball. Best of all, it conveys informationin an understandable way, meaning managers won't need to rely solely on the IT people in their own company--who may speak an entirely different language and have entirely different concerns. Hackers and data thieves are getting smarter and bolder every day. Information Security is your first line of defense.

This book covers novel research on construction and analysis of optimal cryptographic functions such as almost perfect nonlinear (APN), almost bent (AB), planar and bent functions. These functions have optimal resistance to linear and/or differential attacks, which are the two most powerful attacks on symmetric cryptosystems. Besides cryptographic applications, these functions are significant in many branches of mathematics and information theory including coding theory, combinatorics, commutative algebra, finite geometry, sequence design and quantum information theory. The author analyzes equivalence relations for these functions and develops several new methods for construction of their infinite families. In addition, the book offers solutions to two longstanding open problems, including the problem on characterization of APN and AB functions via Boolean, and the problem on the relation between two classes of bent functions.

This book presents an exhaustive and timely review of key research work on fuzzy XML data management, and provides readers with a comprehensive resource on the state-of-the art tools and theories in this fast growing area. Topics covered in the book include: representation of fuzzy XML, query of fuzzy XML, fuzzy database models, extraction of fuzzy XML from fuzzy database models, reengineering of fuzzy XML into fuzzy database models, and reasoning of fuzzy XML. The book is intended as a reference guide for researchers, practitioners and graduate students working and/or studying in the field of Web Intelligence, as well as for data and knowledge engineering professionals seeking new approaches to replace traditional methods, which may be unnecessarily complex or even unproductive.

This edited volume addresses the vast challenges of adapting Online Social Media (OSM) to developing research methods and applications. The topics cover generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, behavior detection, mining social content for common trends, identifying and ranking social content sources, building friend-comprehension tools, and many others. Each of the ten chapters tackle one or more of these issues by proposing new analysis methods or new visualization techniques, or both, for famous OSM applications such as Twitter and Facebook. This collection of contributed chapters address these challenges. Online Social Media has become part of the daily lives of hundreds of millions of users generating an immense amount of 'social content'. Addressing the challenges that stem from this wide adaptation of OSM is what makes this book a valuable contribution to the field of social networks.

This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

During the last few years, software evolution research has explored new domains such as the study of socio-technical aspects and collaboration between different individuals contributing to a software system, the use of search-based techniques and meta-heuristics, the mining of unstructured software repositories, the evolution of software requirements, and the dynamic adaptation of software systems at runtime. Also more and more attention is being paid to the evolution of collections of inter-related and inter-dependent software projects, be it in the form of web systems, software product families, software ecosystems or systems of systems.

With this book, the editors present insightful contributions on these and other domains currently being intensively explored, written by renowned researchers in the respective fields of software evolution. Each chapter presents the state of the art in a particular topic, as well as the current research, available tool support and remaining challenges. The book is complemented by a glossary of important terms used in the community, a reference list of nearly 1,000 papers and books and tips on additional resources that may be useful to the reader (reference books, journals, standards and major scientific events in the domain of software evolution and datasets).This book is intended for all those interested in software engineering, and more particularly, software maintenance and evolution. Researchers and software practitioners alike will find in the contributed chapters an overview of the most recent findings, covering a broad spectrum of software evolution topics. In addition, it can also serve as the basis of graduate or postgraduate courses on e.g., software evolution, requirements engineering, model-driven software development or social informatics.

The main objectives of this book are to expose key aspects that have a relevance when dealing with open data viewed from different perspectives and to provide appealing examples of how open data is implemented worldwide. The concept of open data as we know it today is the result of many different initiatives, both of a legislative and non-legislative nature, and promoted by a wide range of actors. Numerous regulatory antecedents to foster the concept of open data and embed it in national and international policy agendas have been undertaken on both sides of the Atlantic, as well as at a supranational level. The book highlights a number of the efforts made to promote open data in Europe, Asia and the United States. In addition to new insights, practical guidance and multiple disciplinary perspectives on open data, the book also addresses the transformation of current developments towards open data, which may be referred to as the democratisation of data. This book will support open data practitioners as well as open data scholars in their endeavours to promote open data implementation and research. Bastiaan van Loenen is associate professor and director of the Knowledge Centre Open Data at the Faculty of Architecture and The Built Environment of Delft University of Technology in the Netherlands, as is Glenn Vancauwenberghe, who is a post-doctoral researcher, and Joep Crompvoets is a professor at the Public Governance Institute of the KU Leuven in Belgium.

In the digital world, the need to protect communications increases every day. While traditional digital encryption methods are useful, there are many other options for hiding your information. "Information Hiding in Speech Signals for Secure Communication" provides a number of methods to hide secret speech information using a variety of digital speech coding standards. Professor Zhijun Wu has conducted years of research in the field of speech information hiding, and brings his state-of-the-art techniques to readers of this book, including a mathematical model for information hiding, the core concepts of secure speech communication, the ABS-based information hiding algorithm, and much more.

This book shows how to implement a secure speech communication system, including applications to various network security states. Readers will find information hiding algorithms and techniques (embedding and extracting) that are capable of withstanding the advanced forms of attack. The book presents concepts and applications for all of the most widely used speech coding standards, including G.711, G.721, G.728, G.729 and GSM, along with corresponding hiding and extraction algorithms. Readers will also learn how to use a speech covert communication system over an IP network as well as a speech secure communication system applied in PSTN.
Presents information hiding theory and the mathematical model used for information hiding in speech.Provides a number of methods to hide secret speech information using the most common digital speech coding standards.A combination of practice and theory enables programmers and system designers not only to implement tried and true encryption procedures, but also to consider probable future developments in their designs.

This book systematically reviews world Internet development over the past 50 years, and comprehensively discusses the great contributions it has made to economic and social advances. Further, it describes the development, status and trends related to the Internet in major countries around the globe in 2019, and provides an in-depth analysis of the latest conditions, dynamics and development trends in key areas, including information infrastructure, information technology, digital economy, digital government, Internet media, cyberspace security, and international cyberspace governance. Moreover, the book further modifies and enhances the Global Internet Development Index System, in order to better show the Internet development strengths and advantages in various countries, and to reflect the global development trends more comprehensively, accurately and objectively. This book reviews the significant developments and summarizes the lessons learned as well as the future challenges. From a global perspective, it offers a vision of building a community with a shared future in cyberspace based on the new concepts, new ideas and new achievements of various countries participating in cyberspace development and construction. As such it is a valuable reference resource for anyone working in Internet related fields, such as those in government departments, internet enterprises, scientific research institutions, colleges and universities wanting to fully understand world Internet development.

"Time and Relational Theory" provides an in-depth description of temporal database systems, which provide special facilities for storing, querying, and updating historical and future data. Traditionally, database management systems provide little or no special support for temporal data at all. This situation is changing because: Cheap storage enables retention of large volumes of historical data in data warehousesUsers are now faced with temporal data problems, and need solutions Temporal features have recently been incorporated into the SQL standard, and vendors have begun to add temporal support to their DBMS products

Based on the groundbreaking text "Temporal Data & the Relational Model" (Morgan Kaufmann, 2002) and new research led by the authors, "Time and Relational Theory" is the only book to offer a complete overview of the functionality of a temporal DBMS. Expert authors Nikos Lorentzos, Hugh Darwen, and Chris Date describe an approach to temporal database management that is firmly rooted in classical relational theory and will stand the test of time.

This book covers the SQL:2011 temporal extensions in depth and identifies and discusses the temporal functionality still missing from SQL.
Understand how the relational model provides an ideal basis for taming the complexities of temporal databases Learn how to analyze and evaluate commercial temporal products with this timely and important information Be able to use sound principles in designing and using temporal databases Understand the temporal support recently added to SQL with coverage of the new SQL features in this unique, accurate, and authoritative reference Appreciate the benefits of a truly relational approach to the problem with this clear, user friendly presentation

Data Mining techniques are gradually becoming essential components of corporate intelligence systems and progressively evolving into a pervasive technology within activities that range from the utilization of historical data to predicting the success of an awareness campaign. In reality, data mining is becoming an interdisciplinary field driven by various multi-dimensional applications. Data Mining Applications for Empowering Knowledge Societies presents an overview on the main issues of data mining, including its classification, regression, clustering, and ethical issues. This comprehensive book also provides readers with knowledge enhancing processes as well as a wide spectrum of data mining applications.

Global cities are facing an almost unprecedented challenge of change. As they re-emerge from the Covid 19 pandemic and get ready to face climate change and other, potentially existential threats, they need to look for new ways to support wealth and wellbeing creation - leveraging Big Data and AI and suing them into their physical reality and to become greener, more inclusive and resilient, hence sustainable. This book describes how new digital technologies could be used to design digital and physical twins of cities that are able to feed into each other to optimize their working and ability to create new wealth and wellbeing. The book also describes how to increase cities' social and economic resilience during crisis time and addressing their almost fatal weaknesses - as it became all too obvious during the recent COVID 19 crisis. Also, the book presents a framework for a critical discussion of the concept of "smart-city", suggesting its development into a "cyber" and "meta" one - meaning, not only digital systems can allow physical ones (e.g. cities, citizens, households and companies) to become "smarter", but also the vice versa is true, as off line data and real life behaviours can support the optimization and development of virtual brains as a sum of big data and artificial intelligence apps all sitting "over the cloud". An analysis of the fundamental dynamics of this emerging "info-telligence" economy, and of the potential role of big digital players like Amazon, Google and Facebook is then paving the way to discuss a few strategic forays on how traditional sectors such as financial services, real estate, TMT or health could also evolve, leveraging Big Data and AI in a cyber-physical integrated setting. Finally, a number of thought provoking use cases that could be designed around individuals, and to improve the success and the resilience of households and companies living and working in urban areas are discussed, as an example of one of the most exciting future markets to come: the one of global, sustainable cities

This book presents the latest developments regarding a detailed mobile agent-enabled anomaly detection and verification system for resource constrained sensor networks; a number of algorithms on multi-aspect anomaly detection in sensor networks; several algorithms on mobile agent transmission optimization in resource constrained sensor networks; an algorithm on mobile agent-enabled in situ verification of anomalous sensor nodes; a detailed Petri Net-based formal modeling and analysis of the proposed system, and an algorithm on fuzzy logic-based cross-layer anomaly detection and mobile agent transmission optimization. As such, it offers a comprehensive text for interested readers from academia and industry alike.

This book presents breakthroughs in the design of Wireless Energy Harvesting (WEH) networks. It bridges the gap between WEH through radio waves communications and power transfer, which have largely been designed separately. The authors present an overview of the RF-EHNs including system architecture and RF energy harvesting techniques and existing applications. They also cover the idea of WEH in novel discoveries of information, the theoretical bounds in WEH, wireless sensor networks, usage of modern channel coding together with WEH, energy efficient resource allocation mechanisms, distributed self-organized energy efficient designs, delay-energy trade-off, specific protocols for energy efficient communication designs, D2D communication and energy efficiency, cooperative wireless networks, and cognitive networks.

Big Data in History introduces a project to create a world-historical archive that will trace the last four centuries of historical dynamics and change. The archive will link research on social, economic, and political affairs, plus health and climate, for societies throughout the world. The care, detail, and advanced technology that go into building such an archive are outlined in this book, and the benefits of gathering and disseminating data from our long history are clearly mapped out. Chapters address the archive's overall plan, how to interpret the past through a global archive, how to organize historical research on five continents, and the missions of gathering widespread records, linking local data into global patterns, and exploring the results. The concluding chapters summarize project plans and compare it with two major and successful projects in worldwide data: the modelling of climate and documenting the human genome.

This book offers comprehensive coverage of information retrieval by considering both Text Based Information Retrieval (TBIR) and Content Based Image Retrieval (CBIR), together with new research topics. The approach to TBIR is based on creating a thesaurus, as well as event classification and detection. N-gram thesaurus generation for query refinement offers a new method for improving the precision of retrieval, while event classification and detection approaches aid in the classification and organization of information using web documents for domain-specific retrieval applications. In turn, with regard to content based image retrieval (CBIR) the book presents a histogram construction method, which is based on human visual perceptions of color. The book's overarching goal is to introduce readers to new ideas in an easy-to-follow manner.

The book gives a broad overview of the Internet of Things (IoT) concept from various angles. The book provides rationale for: the concept development; its regulatory and technical background associated aspects such as the ambient and edge intelligence; fog computing; capillary networks and machine-type communications; etc. Each of these items is then extended in further respective chapters that deal with technicalities behind them. Chapters: 2-5, 8, 10-11 are addressed to those who seek expository IoT-related information on aspects such as the pathloss calculation, narrowband radio interfaces, radiation masks, spectrum matters, medium access control, and a transmission frame construction. That section ends with an exhaustive description of the six most popular IoT systems: LoRa, Weightless, SigFox, NB-IoT, LTE-M(TC) and EC-GSM IoT. Specialists and network designers may find chapters 6 and 7 interesting where a novel methodology is proposed for testing narrowband IoT systems performance for immunity to electromagnetic interference (EMI) and multipath propagation, both emulated in artificial conditions of the anechoic and the reverberation chamber.

This book highlights some of the most fascinating current uses, thought-provoking changes, and biggest challenges that Big Data means for our society. The explosive growth of data and advances in Big Data analytics have created a new frontier for innovation, competition, productivity, and well-being in almost every sector of our society, as well as a source of immense economic and societal value. From the derivation of customer feedback-based insights to fraud detection and preserving privacy; better medical treatments; agriculture and food management; and establishing low-voltage networks - many innovations for the greater good can stem from Big Data. Given the insights it provides, this book will be of interest to both researchers in the field of Big Data, and practitioners from various fields who intend to apply Big Data technologies to improve their strategic and operational decision-making processes.