"What information do these data reveal?" "Is the information correct?" "How can I make the best use of the information?" The widespread use of computers and our reliance on the data generated by them have made these questions increasingly common and important. Computerized data may be in either digital or analog form and may be relevant to a wide range of applications that include medical monitoring and diagnosis, scientific research, engineering, quality control, seismology, meteorology, political and economic analysis and business and personal financial applications. The sources of the data may be databases that have been developed for specific purposes or may be of more general interest and include those that are accessible on the Internet. In addition, the data may represent either single or multiple parameters. Examining data in its initial form is often very laborious and also makes it possible to "miss the forest for the trees" by failing to notice patterns in the data that are not readily apparent. To address these problems, this monograph describes several accurate and efficient methods for displaying, reviewing and analyzing digital and analog data. The methods may be used either singly or in various combinations to maximize the value of the data to those for whom it is relevant. None of the methods requires special devices and each can be used on common platforms such as personal computers, tablets and smart phones. Also, each of the methods can be easily employed utilizing widely available off-the-shelf software. Using the methods does not require special expertise in computer science or technology, graphical design or statistical analysis. The usefulness and accuracy of all the described methods of data display, review and interpretation have been confirmed in multiple carefully performed studies using independent, objective endpoints. These studies and their results are described in the monograph. Because of their ease of use, accuracy and efficiency, the methods for displaying, reviewing and analyzing data described in this monograph can be highly useful to all who must work with computerized information and make decisions based upon it.

The WWW era made billions of people dramatically dependent on the progress of data technologies, out of which Internet search and Big Data are arguably the most notable. Structured Search paradigm connects them via a fundamental concept of key-objects evolving out of keywords as the units of search. The key-object data model and KeySQL revamp the data independence principle making it applicable for Big Data and complement NoSQL with full-blown structured querying functionality. The ultimate goal is extracting Big Information from the Big Data. As a Big Data Consultant, Mikhail Gilula combines academic background with 20 years of industry experience in the database and data warehousing technologies working as a Sr. Data Architect for Teradata, Alcatel-Lucent, and PayPal, among others. He has authored three books, including The Set Model for Database and Information Systems and holds four US Patents in Structured Search and Data Integration.

Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.

The world is awash with digital data from social networks, blogs, business, science and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice.

This book includes selected papers presented at the 4th International Conference on Data Engineering and Communication Technology (ICDECT 2020), held at Kakatiya Institute of Technology & Science, Warangal, India, during 25-26 September 2020. It features advanced, multidisciplinary research towards the design of smart computing, information systems and electronic systems. It also focuses on various innovation paradigms in system knowledge, intelligence and sustainability which can be applied to provide viable solutions to diverse problems related to society, the environment and industry.

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.

This book presents the outcomes of the 2021 International Conference on Cyber Security Intelligence and Analytics (CSIA 2021), an international conference dedicated to promoting novel theoretical and applied research advances in the interdisciplinary field of cyber security, particularly focusing on threat intelligence, analytics, and countering cybercrime. The conference provides a forum for presenting and discussing innovative ideas, cutting-edge research findings and novel techniques, methods and applications on all aspects of cyber security intelligence and analytics. Due to COVID-19, Authors, Keynote Speakers and PC committees will attend the conference online.

This book deals with the computational complexity of mathematical problems for which available information is partial, noisy and priced. The author develops a general theory of computational complexity of continuous problems with noisy information and gives a number of applications; he considers deterministic as well as stochastic noise. He also presents optimal algorithms, optimal information, and complexity bounds in different settings: worst case, average case, mixed worst-average, average-worst, and asymptotic. Particular topics include: the existence of optimal linear (affine) algorithms, optimality properties of smoothing spline, regularization and least squares algorithms (with the optimal choice of the smoothing and regularization parameters), adaption versus nonadaption, and relations between different settings. The book integrates the work of researchers over the past decade in such areas as computational complexity, approximation theory, and statistics, and includes many new results as well. The author supplies two hundred exercises to increase the reader's understanding of the subject.

As today's organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages. Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections: Big Data Management-considers the research issues related to the management of Big Data, including indexing and scalability aspects Big Data Processing-addresses the problem of processing Big Data across a wide range of resource-intensive computational settings Big Data Stream Techniques and Algorithms-explores research issues regarding the management and mining of Big Data in streaming environments Big Data Privacy-focuses on models, techniques, and algorithms for preserving Big Data privacy Big Data Applications-illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.

Although an emerging technology, blockchain is here to stay. Since its inception, imaginative thinkers have identified new ways for this powerful technology to bring innovative solutions to problems in the business world. Considered by many as an extreme and disruptive change, how can business leaders overcome resistance to the implementation of blockchain solutions and maximize its potential? The Emerald Handbook of Blockchain for Business equips academics, practitioners, and students with a broad understanding of the cutting-edge developments and applications of emerging blockchain technology. Covering the basic concepts while also showcasing practical applications in intricate real-world situations, this handbook bridges the gap between theory and practice, providing a useful balance of detailed and user-friendly coverage. Facilitating readers with a working knowledge of how blockchain functions and integrates within the business world, this handbook is essential reading for academics looking for a springboard for further research and practitioners needing a go-to resource for navigating the implementation of this fast-moving new technology.

For programmers, analysts, and database administrators, SQL in a Nutshell is the essential reference for the SQL language used in today's most popular database products. This new edition clearly documents every SQL command according to the latest ANSI standard, and details how those commands are implemented in Microsoft SQL Server 2008, Oracle 11g, and the MySQL 5.1 and PostgreSQL 8.3 open source database products. You'll also get a concise overview of the Relational Database Management System (RDBMS) model, and a clear-cut explanation of foundational RDBMS concepts -- all packed into a succinct, comprehensive, and easy-to-use format. This book provides: Background on the Relational Database Model, including current and previous SQL standards Fundamental concepts necessary for understanding relational databases and SQL commands An alphabetical command reference to SQL statements, according to the SQL2003 ANSI standard The implementation of each command by MySQL, Oracle, PostgreSQL, and SQL Server An alphabetical reference of the ANSI SQL2003 functions, as well as the vendor implementations Platform-specific functions unique to each implementation

Beginning where vendor documentation ends, SQL in a Nutshell distills the experiences of professional database administrators and developers who have used SQL variants to support complex enterprise applications. Whether SQL is new to you, or you've been using SQL since its earliest days, you'll get lots of new tips and techniques in this book.

This book systematically summarizes China Internet development over the past 25 years, highlighting its strong impact on China's economy and society, and discussing the Chinese people's transition from beneficiaries and participants to builders, contributors and joint maintainers of cyberspace development. It describes the development achievements, status and development and trends in China Internet in 2019, systematically summarizes the main lessons learned during development, and analyzes China's strategic planning and policy actions. Further, it discusses topics such as development outcomes, future trends in information infrastructure, network information technology, digital economy, e-government, construction and management of network contents, cyberspace security, the legal construction of cyberspace, and international cyberspace governance. In addition, the book suggests improvements to the index system for China Internet development and offers an overall assessment of cyberspace security and informatization work throughout China in order to comprehensively and accurately demonstrate the level of China Internet development.

Comprehensive Coverage of the Entire Area of ClassificationResearch on the problem of classification tends to be fragmented across such areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlying algorithms of classification as well as applications of classification in a variety of problem domains, including text, multimedia, social network, and biological data. This comprehensive book focuses on three primary aspects of data classification: Methods: The book first describes common techniques used for classification, including probabilistic methods, decision trees, rule-based methods, instance-based methods, support vector machine methods, and neural networks. Domains: The book then examines specific methods used for data domains such as multimedia, text, time-series, network, discrete sequence, and uncertain data. It also covers large data sets and data streams due to the recent importance of the big data paradigm. Variations: The book concludes with insight on variations of the classification process. It discusses ensembles, rare-class learning, distance function learning, active learning, visual learning, transfer learning, and semi-supervised learning as well as evaluation aspects of classifiers.

This book contains the proceedings of the 22nd EANN "Engineering Applications of Neural Networks" 2021 that comprise of research papers on both theoretical foundations and cutting-edge applications of artificial intelligence. Based on the discussed research areas, emphasis is given in advances of machine learning (ML) focusing on the following algorithms-approaches: Augmented ML, autoencoders, adversarial neural networks, blockchain-adaptive methods, convolutional neural networks, deep learning, ensemble methods, learning-federated learning, neural networks, recurrent - long short-term memory. The application domains are related to: Anomaly detection, bio-medical AI, cyber-security, data fusion, e-learning, emotion recognition, environment, hyperspectral imaging, fraud detection, image analysis, inverse kinematics, machine vision, natural language, recommendation systems, robotics, sentiment analysis, simulation, stock market prediction.

Manufacturing, like other industries, is rising to the challenges imposed by aggressive consumer demands and the need for cost-effective processing that delivers quality in the fastest possible time. Fierce competition means that keeping abreast of new developments and applications in technology is essential if companies are to meet demands profitably and keep ahead of competitors. This book investigates the design and management of digital manufacturing and assembly systems for an efficient, flexible, and modular production of customized products using the I40 (industry 4.0)-enabling technologies. This book will also provide case studies covering modeling, simulation, and optimization. eBook includes color figures. Discusses how the advancement of data communication and storage through the Internet of Things (IoT) opens the possibilities of connecting sensors, robots, and devices Sheds light on how the human role in industry is decreasing due to the development of connected manufacturing floors, allowing them to take more control over the manufacturing processes, decisions, and even maintenance Covers the benefits from exploiting digital manufacturing, manufacturing enterprises, and what they expect to achieve Explains the important roles that modeling, simulation, and optimization play Investigates the design and management of digital manufacturing and assembly systems for an efficient, flexible, and modular production of customized products exploiting the I40 (industry 4.0)-enabling technologies

This book presents the first broad reflection on the challenges, opportunities, and implications of Artificial Intelligence (AI) in the Gulf Cooperation Council (GCC). Unique results and insights are derived through case studies from diverse disciplines, including engineering, economics, data science, policy-making, governance, and humanscience. Particularly related to these 'softer' disciplines, we make some unexplored yet topical contributions to the literature, with a focus on the GCC (but by no means limited to it), including AI and implications for women, Islamic schools of thought on AI, and the power of AI to help deliver wellbeing and happiness in cities and urban spaces. Finally, the readers are provided with a synthesis of ideas, lessons learned, and a path forward based on the diverse content of the chapters. The book caters to the educated non specialist with interest in AI, targeting a wide audience including professionals, academics, government officials, policymakers, entrepreneurs, and non-governmental organizations.

For programmers, analysts, and database administrators, this Nutshell guide is the essential reference for the SQL language used in today's most popular database products. This new fourth edition clearly documents SQL commands according to the latest ANSI/ISO standard and details how those commands are implemented in Microsoft SQL Server 2019 and Oracle 19c, as well as in the MySQL 8, MariaDB 10.5, and PostgreSQL 14 open source database products. You'll also get a concise overview of the relational database management system (RDBMS) model and a clear-cut explanation of foundational RDBMS concepts--all packed into a succinct, comprehensive, and easy-to-use format. Sections include: Background on the relational database model, including current and previous SQL standards Fundamental concepts necessary for understanding relational databases and SQL commands An alphabetical command reference to SQL statements, according to the SQL:2016 ANSI standard The implementation of each command by MySQL, Oracle, PostgreSQL, and SQL Server An alphabetical reference of the ANSI SQL:2016 functions and constructs as well as the vendor implementations Platform-specific functions unique to each implementation

Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book introduces a broad range of Big Data concepts, tools, and techniques. It covers a wide range of research, and provides comparisons between state-of-the-art approaches. Comprised of five sections, the book focuses on: What Big Data is and why it is important Semantic technologies Tools and methods Business and economic perspectives Big Data applications across industries

India is showing incredibly strong appearance in Information Technology (IT) sector worldwide, but the benefits of IT revolution have not actually percolated into the everyday life of the common man, particularly in rural areas. National E Governance Plan is a major initiative of the Government of India, the first time under which a concerted effort is being made to take Information Technology to the masses in areas of concern to the common man. It aims to make services available online, ensuring that all citizens would have access to them, thereby improving the quality of basic governance on an unprecedented scale. This book attempts to disseminate information about several E Governance projects and possible Data Mining benefits which are the future of good governance in India. Strategic Management of these projects through Data Mining would certainly encourage policy makers to understand better models of E Governance, thorough evaluation of projects, perceptive interrelations between projects, keeping track of the objectives and outcomes and to develop a more collaborative approach towards implementation of the National e- Governance Plan. This revolutionary approach will help the government in assessing the upcoming requirements and developing competencies for further project management, thereby making a difference in the lives of millions. It will help innovators to find out optimum solutions to achieve the end objectives of E Governance. Technical topics discussed in the book include: * E Governance * Fundamental of Data, Data Warehousing, Data Mining, E Governance * World Wide Status of E Governance * Status of E Governance projects in India * E Governance Data Management Framework * E Governance Data Center * Perspective Application of Data Warehousing and Data Mining in E Governance * Case Studies on Education and Health Data Mining * E Governance Data Mining Applications in Various Government Departments, Public Sector, Private Sector * SWOT and PESTLE Analysis for Data Warehousing, Data Mining, E Governance Data Warehousing and Data Mining are related technologies which have seen a significant boost in the last decades, in a way that many of their concepts and techniques have reached a significant level of maturity. They are applied today in most fields of human activity, from commercial to scientific or industrial areas. Today, decision support, data mining, trend analysis and pattern discovery have a large impact on businesses and science alike. Given this evolution, it is important to understand the potential advantages of Data Mining and Data Warehousing and their positive effects on E Governance applications. The book "E Governance Data Center, Data Warehousing and Data Mining: Vision to Realities" is useful for students, application developers, government officials, policy makers, as well as researchers involved in E Governance and Data Mining Applications. This book presents an overall picture of the E Governance Data Mining applications, including Data Management Framework, Data Center and Data Warehousing applications along with possible research directions. An important motivation for writing this book was the need to build an organized framework for E Governance using Data Mining-a challenging task, owing to the extensive multidisciplinary nature of this fastdeveloping field. We hope that this book will encourage people with different backgrounds and experiences to exchange their views regarding Data Mining applications in E Governance so as to contribute toward the establishment of good governance for the developed society.

More and more, the advance of enterprise computing and cloud technologies means that managers are responsible for retrieving data ad-hoc and constructing business reports for decision-making and storytelling. The technical competencies necessary for such tasks can be daunting, and most database teaching methods do little to mitigate the confusion. They tend to follow traditional computer science methods that expose all computational and matrix theory complexities as well as various design theories, and in so doing, they present an excess of information that unnecessarily complicates the learning process for business-minded readers. Zygiaris simplifies his teaching method in order to provide an accessible walkthrough of all technological advances of databases in the business environment. Readers learn how to design, develop, and use databases to provide business analytical reports with the three major database management systems: Microsoft Access, Oracle Express and MariaDB (formerly MySQL). This is all delivered through clearly structured, streamlined chapters, all of which link to online videos that demonstrate visually, in step-by-step tutorials, how to implement the processes outlined in the book. All of these features help the non-IT student or manager to understand the importance of databases in the business environment and to learn how to use those databases to solve real-world problems. This book is of particular interest to students of management and to business managers, and it is of keen interest to anyone who works with major business database systems.

This book addresses issues related to managing data across a distributed database system. It is unique because it covers traditional database theory and current research, explaining the difficulties in providing a unified user interface and global data dictionary. The book gives implementers guidance on hiding discrepancies across systems and creating the illusion of a single repository for users. It also includes three sample frameworks--implemented using J2SE with JMS, J2EE, and Microsoft .Net--that readers can use to learn how to implement a distributed database management system. IT and development groups and computer sciences/software engineering graduates will find this guide invaluable.

Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book introduces a broad range of Big Data concepts, tools, and techniques. It covers a wide range of research, and provides comparisons between state-of-the-art approaches. Comprised of five sections, the book focuses on: What Big Data is and why it is important Semantic technologies Tools and methods Business and economic perspectives Big Data applications across industries

Networking for Big Data supplies an unprecedented look at cutting-edge research on the networking and communication aspects of Big Data. Starting with a comprehensive introduction to Big Data and its networking issues, it offers deep technical coverage of both theory and applications. The book is divided into four sections: introduction to Big Data, networking theory and design for Big Data, networking security for Big Data, and platforms and systems for Big Data applications. Focusing on key networking issues in Big Data, the book explains network design and implementation for Big Data. It examines how network topology impacts data collection and explores Big Data storage and resource management. Addresses the virtual machine placement problem Describes widespread network and information security technologies for Big Data Explores network configuration and flow scheduling for Big Data applications Presents a systematic set of techniques that optimize throughput and improve bandwidth for efficient Big Data transfer on the Internet Tackles the trade-off problem between energy efficiency and service resiliency The book covers distributed Big Data storage and retrieval as well as security, trust, and privacy protection for Big Data collection, storage, and search. It discusses the use of cloud infrastructures and highlights its benefits to overcome the identified issues and to provide new approaches for managing huge volumes of heterogeneous data. The text concludes by proposing an innovative user data profile-aware policy-based network management framework that can help you exploit and differentiate user data profiles to achieve better power efficiency and optimized resource management.

We overspend on data center storage ... yet, we fall short of business requirements. It's not about the technologies. It's about the proper application of technologies to deliver storage services efficiently and affordably. It's about meeting business requirements dependent on data center storage. Spend less, deliver more. Data Center Storage: Cost-Effective Strategies, Implementation, and Management provides an industry insider's insight on how to properly scope, plan, evaluate, and implement storage technologies to maximize performance, capacity, reliability, and power savings. It provides business and use-case focused coverage of storage technology, including storage area networks (SAN), capacity-optimized drives, and solid-state drives. It offers key insights on financially responsible spending for data center storage. Delivered in accessible language, the book starts with a discussion of the business merits of replacing direct attached, compartmentalized storage with consolidated SAN-attached storage. The author advises on the use of service level applications (SLAs) as a tool to drive business unit collaboration with IT and prioritize those actions that impact productivity and profit from those that are less critical. This business guide to applied technologies disassembles big problems into digestible segments to help you understand, quantify, and fix any problems that arise as you work towards meeting your growing storage needs. The book builds on the consolidation and SLA driven approach to take advantage of the compelling benefits and potential savings of managed hosting and cloud storage.