This book is the best way to make the leap from SQL-92 to SQL: 1999, but it is much more than just a simple bridge between the two. The latest from celebrated SQL experts Jim Melton and Alan Simon, "SQL: 1999" is a comprehensive, eminently practical account of SQL's latest incarnation and a potent distillation of the details required to put it to work. Written to accommodate both novice and experienced SQL users, "SQL: 1999" focuses on the language's capabilities, from the basic to the advanced, and the way that real applications take advantage of them. Throughout, the authors illustrate features and techniques with clear and often entertaining references to their own custom database, which can be downloaded from the companion Web site.
* Gives authoritative coverage from an expert team that includes the editor of the SQL-92 and SQL: 1999 standards.
* Provides a general introduction to SQL that helps you understand its constituent parts, history, and place in the realm of computer languages.
* Explains SQL: 1999's more sophisticated features, including advanced value expressions, predicates, advanced SQL query expressions, and support for active databases.
* Explores key issues for programmers linking applications to SQL databases.
* Provides guidance on troubleshooting, internationalization, and changes anticipated in the next version of SQL.
* Contains appendices devoted to database design, a complete SQL: 1999 example, the standardization process, and more.

Today's work is characterized by a high degree of innovation and thus demands a thorough overview of relevant knowledge in the world and in organizations. Semantic Work Environments support the work of the user by collecting knowledge about needs and providing processed and improved knowledge to be integrated into work. ""Emerging Technologies for Semantic Work Environments: Techniques, Methods, and Applications"" describes an overview of the emerging field of Semantic Work Environments by combining various research studies and underlining the similarities between different processes, issues and approaches in order to provide the reader with techniques, methods, and applications of the study.

This textbook guides readers through their first steps into the challenging world of mimicking human vision with computational tools and techniques pertaining to the field of image processing and analysis. While today's theoretical and applied processing and analysis of images meet with challenging and complex problems, this primer is confined to a much simpler, albeit critical, collection of image-to-image transformations, including image normalisation, enhancement, and filtering.It serves as an introduction to beginners, a refresher for undergraduate and graduate students, as well as engineers and computer scientists confronted with a problem to solve in computer vision. The book covers basic image processing/computer vision pipeline techniques, which are widely used in today's computer vision, computer graphics, and image processing, giving the readers enough knowledge to successfully tackle a wide range of applied problems.

Here's a thorough introduction to the latest developments in database systems design presented from an applications point of view. Featuring contributions from well-known experts in the field, this book pays special attention to issues raised by new trends in database design, and how these developments affect the programmer and database administrator.

This book provides an interdisciplinary approach to complexity, combining ideas from areas like complex networks, cellular automata, multi-agent systems, self-organization and game theory. The first part of the book provides an extensive introduction to these areas, while the second explores a range of research scenarios. Lastly, the book presents CellNet, a software framework that offers a hands-on approach to the scenarios described throughout the book. In light of the introductory chapters, the research chapters, and the CellNet simulating framework, this book can be used to teach undergraduate and master's students in disciplines like artificial intelligence, computer science, applied mathematics, economics and engineering. Moreover, the book will be particularly interesting for Ph.D. and postdoctoral researchers seeking a general perspective on how to design and create their own models.

Whether an old photograph or a single video frame, there is a wealth of data hidden in a picture. Image processing and pattern analysis play a vital role in engineering science and can be applied in diverse areas to solve existing and practical problems. The Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing discusses the advances of image processing and pattern analysis and addresses how new innovations will cater to the demands of daily life. This handbook provides the resources necessary for technology developers, scientists, and policymakers to adopt and implement new inventions across the globe. The chapters presented in this publication encompass various aspects of recent image processing and pattern analysis innovations including, but not limited to, mobile image tracking, motion picture analysis, pattern classification, multisensory data fusion, 3D imaging, supporting routing protocols, brain computer interfaces, image restoration, and medical imaging.

Intelligent methods are used in distributed environments in countless ways, with examples such as propagation, communication, collaboration, and cooperation. With the abundant purposes for intelligence in distributed systems, it is pertinent for researchers, technicians, and students in various areas of computer science to discover the most current and definitive advances in the field.""Intelligence Integration in Distributed Knowledge Management"" provides recent technologies and practices in intelligence for distributed systems, while covering major aspects of the agent based systems. This book is a must for those striving to enhance their understanding of distributed knowledge management and extend their ideas of cooperation using for numerous real-world problems.

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Chapters in this volume include: Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression Hybrid Structural and Behavioral Diversity Methods in GP Multi-Population Competitive Coevolution for Anticipation of Tax Evasion Evolving Artificial General Intelligence for Video Game Controllers A Detailed Analysis of a PushGP Run Linear Genomes for Structured Programs Neutrality, Robustness, and Evolvability in GP Local Search in GP PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification Relational Structure in Program Synthesis Problems with Analogical Reasoning An Evolutionary Algorithm for Big Data Multi-Class Classification Problems A Generic Framework for Building Dispersion Operators in the Semantic Space Assisting Asset Model Development with Evolutionary Augmentation Building Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

It is hard to believe that the pursuit of artificial intelligence is not a phenomenon of the twentieth century. For over three hundred years, the boundaries between bodies and machines - the natural and the artificial, the animate and the inanimate - have been passionately explored. These explorations, beginning in the seventeenth and eighteenth and increasing during the nineteenth century, have been all but forgotten, lost beneath the commotion of the modern day world. This book retrieves these lost histories, giving voice to the hopes, dreams, and fears of philosophers, medical practitioners, engineers, craftsmen and artisans who have all been fascinated by the interface between bodies and machines. The journey back in time unfolds with the mysterious advent of mechanical philosophies, which conceptualized the body and the surrounding world largely in terms of mechanistic interactions. These theories develop in intriguing directions and fuel experiments in such areas as material production and social punishment, spiritualism and mental health. From reanimating dead bodies with electricity, which led to the introduction of the electric chair, through to the use of machines to render 'hysterics' and the insane fit for reintroduction into society, this book conveys the dark truths behind our relationship with machines.This book is not only an exceptional contribution to the history of technology but also to contemporary debates about humans and machines.

The ability to communicate with anyone at anytime, anywhere, necessitates a need to provide reliable, resilient, and sustainable applications to support the evolving uses of mobile technologies. Movement-Aware Applications for Sustainable Mobility: Technologies and Approaches focuses on the use of location sensing technology and its wider applicability in supporting sustainable mobility, and presents current research on developing innovative approaches for gathering, representing, storing and analyzing movement data sets being generated from location sensing technology. Offering a unique opportunity for learning about how different techniques are being used to develop new movement-aware applications, this reference work gives students, researchers and practitioners the opportunity to acquire new concepts in the production, awareness and use of sensors, data, and information products for supporting sustainable mobility.

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

In this era of healthcare applications predominantly occupy both individuals as well as the healthcare industries, so the need for analytical reports becomes an essential component for success. Especially, the IoT applications employed for healthcare which generate a huge amount of data that needs to be analyzed to produce the expected reports. To accomplish this task, a cloud-based analytical solution will be the right choice by which the reports can be generated faster compared to the traditional ways. In this book, the different analytical methods coupled with AI to analyze the IoT data on the cloud are discussed. This book applies AI in edge analytics for healthcare applications, analyzes the impact of tools and techniques in edge analytics for healthcare, and provides security solutions for edge analytics in healthcare IoT. Each chapter provides in-depth details on how to apply different analytical methods and tools for analytics of healthcare applications devised using IoT. As the IoT devices are generating huge amounts of data, it is highly essential to do the analytics on the cloud and this book showcases the mechanisms that are going to be applied for it. Hence, this book provides a holistic idea on how to do edge analytics for healthcare IoT using AI.

As the healthcare industry continues to expand, it must utilize technology to ensure efficiencies are maintained. Healthcare needs to move in a direction where computational methods and algorithms can relieve the routine work of medical doctors, leaving them more time to carry out more important and skilled tasks such as surgery. Computational Methods and Algorithms for Medicine and Optimized Clinical Practice discusses some of the most interesting aspects of theoretical and applied research covering complementary facets of computational methods and algorithms to achieve greater efficiency and support medical personnel. Featuring research on topics such as healthcare reform, artificial intelligence, and disease detection, this book will particularly appeal to medical professionals and practitioners, hospitals, administrators, students, researchers, and academicians.

From Digital Traces to Algorithmic Projections describes individual digital fingerprints in interaction with the different algorithms they encounter throughout life. Centered on the human user, this formalism makes it possible to distinguish the voluntary projections of an individual and their systemic projections (suffered, metadata), both open (public) and closed. As the global algorithmic projection of an individual is now the focus of attention (Big Data, neuromarketing, targeted advertising, sentiment analysis, cybermonitoring, etc.) and is used to define new concepts, this resource discusses the ubiquity of place and the algorithmic consent of a user.

This book addresses the intellectual foundations, function, modeling approaches and complexity of cellular automata; explores cellular automata in combination with genetic algorithms, neural networks and agents; and discusses the applications of cellular automata in economics, traffic and the spread of disease. Pursuing a blended approach between knowledge and philosophy, it assigns equal value to methods and applications.

With the proliferation of devices connected to the internet and connected to each other, the volume of data collected, stored, and processed is increasing every day, which brings new challenges in terms of information security. As big data expands with the help of public clouds, traditional security solutions tailored to private computing infrastructures and confined to a well-defined security perimeter, such as firewalls and demilitarized zones (DMZs), are no longer effective. New security functions are required to work over the heterogenous composition of diverse hardware, operating systems, and network domains. Security, Privacy, and Forensics Issues in Big Data is an essential research book that examines recent advancements in big data and the impact that these advancements have on information security and privacy measures needed for these networks. Highlighting a range of topics including cryptography, data analytics, and threat detection, this is an excellent reference source for students, software developers and engineers, security analysts, IT consultants, academicians, researchers, and professionals.