SQL is a widely used to access most databases, therefore database developers and system administrators should be familiar with it. This hands-on SQL book will help beginner and intermediate users to write queries that apply complex conditions on a table. The book's unique side by side approach makes it easy for the reader to learn three major query languages in the IT industry. The author has over 20 years of experience in database design. KEY FEATURES: Contains numerous practical screenshots of Oracle SQL, T-SQL, MySQL statements and results. Shows the differences between Oracle SQL, T-SQL and MySQL side by side. Gives a real world experience for SQL developers and database administrators. Sample data is available to work on (available on our website).

Going Digital: Strategies for Access, Preservation, and Conversion of Collections to a Digital Format offers you succinct and analytic views of the problems and benefits of digital resources in the traditional academic library. Library administrators, collection managers, and librarians will learn the advantages and disadvantages of traditional and digital collections and the costs of providing local access or implementing remote access to digital collections. Originally presented at a series of five symposiums sponsored by the Research Libraries Group, the articles inGoing Digital will help you decide upon a cost-effective collection method that will meet the needs of your library, your patrons, and your budget.The chapters in this text are written by the nation 's leading librarians who pose and answer questions about hardware and software needed for digital libraries, the costs involved, establishing and maintaining access to digital collections, copyright concerns, and long-term preservation problems. Going Digital gives you insight into factors that will help you decide what will best meet the goals of your library, such as: the advantages and disadvantages of preserving microfilm and digital conversion choosing the correct hardware and software for your digital preservation program the changes required from librarians when shifting from collection development to digital resources examining the selection process for collections from perspectives of access, public service, technological requirements, and preservation ways to improve access to traditional collections cost comparisons between digital and hard copy resources devising a technical plan for successful digital conversion of projects involving the user 's wants when selecting collections for digital conversion and recognizing the central parts patrons play in the selection processIn light of the changing ways we receive and keep our information, Going Digital discusses new collection preservation criteria and suggests that access and informational values, not just deterioration, should be equal factors in selecting materials to be converted to digital form. Proving that digital collections are changing every facet of library operations, Going Digital shows you the most cost-effective way to begin a digital collection and how to choose what materials to digitize in order to provide your patrons with the information they want and need.

The creation, accumulation, and use of copious amounts of data are driving rapid change across a wide variety of industries and academic disciplines. This 'Big Data' phenomenon is the result of recent developments in computational technology and improved data gathering techniques that have led to substantial innovation in the collection, storage, management, and analysis of data. Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling focuses on the real estate discipline, guiding researchers and practitioners alike on the use of data-centric methods and analysis from applied and theoretical perspectives. In it, the authors detail the integration of Big Data into conventional real estate research and analysis. The book is process-oriented, not only describing Big Data and associated methods, but also showing the reader how to use these methods through case studies supported by supplemental online material. The running theme is the construction of efficient, transparent, and reproducible research through the systematic organization and application of data, both traditional and 'big'. The final chapters investigate legal issues, particularly related to those data that are publicly available, and conclude by speculating on the future of Big Data in real estate.

Deductive Databases and their Applications is an introductory text aimed at undergraduate students with some knowledge of database and information systems. The text comes complete with exercises and solutions to encourage students to tackle problems practically as well as theoretically. The author presents the origins of deductive databases in Prologue before proceeding to analyse the main deductive database paradigm - the data-log model. The final chapters are dedicated to closely related topics such as prepositional expert systems, integrity constraint specification and evaluation, and update propagation. Particular attention is paid to CASE tool repositories.

From predictive policing to self-surveillance to private security, the potential uses to of big data in crime control pose serious legal and ethical challenges relating to privacy, discrimination, and the presumption of innocence. The book is about the impacts of the use of big data analytics on social and crime control and on fundamental liberties. Drawing on research from Europe and the US, this book identifies the various ways in which law and ethics intersect with the application of big data in social and crime control, considers potential challenges to human rights and democracy and recommends regulatory solutions and best practice. This book focuses on changes in knowledge production and the manifold sites of contemporary surveillance, ranging from self-surveillance to corporate and state surveillance. It tackles the implications of big data and predictive algorithmic analytics for social justice, social equality, and social power: concepts at the very core of crime and social control. This book will be of interest to scholars and students of criminology, sociology, politics and socio-legal studies.

There is a long history of governments, businesses, science and citizens producing and utilizing data in order to monitor, regulate, profit from and make sense of the urban world. Recently, we have entered the age of big data, and now many aspects of everyday urban life are being captured as data and city management is mediated through data-driven technologies. Data and the City is the first edited collection to provide an interdisciplinary analysis of how this new era of urban big data is reshaping how we come to know and govern cities, and the implications of such a transformation. This book looks at the creation of real-time cities and data-driven urbanism and considers the relationships at play. By taking a philosophical, political, practical and technical approach to urban data, the authors analyse the ways in which data is produced and framed within socio-technical systems. They then examine the constellation of existing and emerging urban data technologies. The volume concludes by considering the social and political ramifications of data-driven urbanism, questioning whom it serves and for what ends. This book, the companion volume to 2016's Code and the City, offers the first critical reflection on the relationship between data, data practices and the city, and how we come to know and understand cities through data. It will be crucial reading for those who wish to understand and conceptualize urban big data, data-driven urbanism and the development of smart cities.

The Essentials of Data Science: Knowledge Discovery Using R presents the concepts of data science through a hands-on approach using free and open source software. It systematically drives an accessible journey through data analysis and machine learning to discover and share knowledge from data. Building on over thirty years' experience in teaching and practising data science, the author encourages a programming-by-example approach to ensure students and practitioners attune to the practise of data science while building their data skills. Proven frameworks are provided as reusable templates. Real world case studies then provide insight for the data scientist to swiftly adapt the templates to new tasks and datasets. The book begins by introducing data science. It then reviews R's capabilities for analysing data by writing computer programs. These programs are developed and explained step by step. From analysing and visualising data, the framework moves on to tried and tested machine learning techniques for predictive modelling and knowledge discovery. Literate programming and a consistent style are a focus throughout the book.

Business operations in large organizations today involve massive, interactive, and layered networks of teams and personnel collaborating across hierarchies and countries on complex tasks. To optimize productivity, businesses need to know: what communication patterns do high-performing teams have in common? Is it possible to predict a team's performance before it starts work on a project? How can productive team behavior be fostered? This comprehensive review for researchers and practitioners in data mining and social networks surveys recent progress in the emerging field of network science of teams. Focusing on the underlying social network structure, the authors present models and algorithms characterizing, predicting, optimizing, and explaining team performance, along with key applications, open challenges, and future trends.

This book presents innovative ideas, cutting-edge findings, and novel techniques, methods, and applications in a broad range of cybersecurity and cyberthreat intelligence areas. As our society becomes smarter, there is a corresponding need to secure our cyberfuture. The book describes approaches and findings that are of interest to business professionals and governments seeking to secure our data and underpin infrastructures, as well as to individual users.

Thanks to advances in electronic archiving of biodiversity data and the digitization of climate and other geophysical data, a new era in biogeography, functional ecology, and evolutionary ecology has begun. In Data Mining for Global Trends in Mountain Biodiversity, Christian Korner, Eva M. Spehn, and a team of experts from the Global Mountain Biodiversity Assessment of DIVERSITAS explore two of the hottest subjects in science and technology: biodiversity and data mining. They demonstrate how to harness the scientific power of biological databases for furthering ecological and evolutionary theory. Expert contributors address two aspects of the Global Mountain Biodiversity Assessment. They cover how to link biodiversity data with geophysical data and how to use biodiversity data to substantiate evolutionary and ecological theory. The text provides different methodological approaches and examples of successful mining of geo-referenced data in mountain regions on various scales. It includes: Elevational and latitudinal gradients in plant diversity E-mining trends in diversity of Lepidoptera, beetles, and birds Niche modeling to explain past trends and predict future trends in mountain biodiversity Sharing biodiversity data with the Global Biodiversity Information Facility Using electronic databases opens ways to manage biodiversity in a sustainable fashion, test evolutionary and ecological theories, and measure the impact of climate change on various species and its effect on conservation efforts. The information and examples presented in this book can stimulate the creative use of archive data to answer old questions with new tools, and advance knowledge and understanding of mountain biodiversity worldwide. The book highlights the benefits of and the continuing need for an increase in the amount and quality of georeferenced data provided online in order to meet the challenges of global change.

This book investigates the fundaments, standards, and protocols of Cyber-Physical Systems (CPS) in the industrial transformation environment. It facilitates a fusion of both technologies in the creation of reliable and robust applications. Cyber-Physical Systems for Industrial Transformation: Fundamentals, Standards, and Protocols explores emerging technologies such as artificial intelligence, data science, blockchain, robotic process automation, virtual reality, edge computing, and 5G technology to highlight current and future opportunities to transition CPS to become more robust and reliable. The book showcases the real-time sensing, processing, and actuation software and discusses fault-tolerant and cybersecurity as well. This book brings together undergraduates, postgraduates, academics, researchers, and industry individuals that are interested in exploring new ideas, techniques, and tools related to CPS and Industry 4.0.

This book presents innovative ideas, cutting-edge findings, and novel techniques, methods, and applications in a broad range of cybersecurity and cyberthreat intelligence areas. As our society becomes smarter, there is a corresponding need to secure our cyberfuture. The book describes approaches and findings that are of interest to business professionals and governments seeking to secure our data and underpin infrastructures, as well as to individual users. 1. Highlights recent applications and techniques in cyber intelligence 2. Includes the proceedings of the 2021 International Conference on Applications and Techniques in Cyber Intelligence (ATCI 2021) 3. Presents a broad range of scientific research on cyber intelligence

'Big data' is now readily available to economic historians, thanks to the digitisation of primary sources, collaborative research linking different data sets, and the publication of databases on the internet. Key economic indicators, such as the consumer price index, can be tracked over long periods, and qualitative information, such as land use, can be converted to a quantitative form. In order to fully exploit these innovations it is necessary to use sophisticated statistical techniques to reveal the patterns hidden in datasets, and this book shows how this can be done. A distinguished group of economic historians have teamed up with younger researchers to pilot the application of new techniques to 'big data'. Topics addressed in this volume include prices and the standard of living, money supply, credit markets, land values and land use, transport, technological innovation, and business networks. The research spans the medieval, early modern and modern periods. Research methods include simultaneous equation systems, stochastic trends and discrete choice modelling. This book is essential reading for doctoral and post-doctoral researchers in business, economic and social history. The case studies will also appeal to historical geographers and applied econometricians.

This comprehensive and timely book, New Age Analytics: Transforming the Internet through Machine Learning, IoT, and Trust Modeling, explores the importance of tools and techniques used in machine learning, big data mining, and more. The book explains how advancements in the world of the web have been achieved and how the experiences of users can be analyzed. It looks at data gathering by the various electronic means and explores techniques for analysis and management, how to manage voluminous data, user responses, and more. This volume provides an abundance of valuable information for professionals and researchers working in the field of business analytics, big data, social network data, computer science, analytical engineering, and forensic analysis. Moreover, the book provides insights and support from both practitioners and academia in order to highlight the most debated aspects in the field.

Combining knowledge with strategies, Data Structure Practice for Collegiate Programming Contests and Education presents the first comprehensive book on data structure in programming contests. This book is designed for training collegiate programming contest teams in the nuances of data structure and for helping college students in computer-related majors to gain deeper understanding of data structure. Based on successful experiences in many world-level contests, the book includes 204 typical problems and detailed analyses selected from the ACM International Collegiate Programming Contest and other major programming contests since 1990. It is divided into four sections that focus on: Fundamental programming skills Experiments for linear lists Experiments for trees Experiments for graphs Each chapter contains a set of problems and includes hints. The book also provides test data for most problems as well as sources and IDs for online judgments that help with improving programming skills. Introducing a multi-options model and considerations of context, Data Structure Practice for Collegiate Programming Contests and Education encourages students to think creatively in solving programming problems. By taking readers through practical contest problems from analysis to implementation, it provides a complete source for enhancing understanding and polishing skills in programming.

This book presents the 2nd International Conference on Artificial Intelligence and Computer Visions (AICV 2021) proceeding, which took place in Settat, Morocco, from June 28- to 30, 2021. AICV 2021 is organized by the Scientific Research Group in Egypt (SRGE) and the Computer, Networks, Mobility and Modeling Laboratory (IR2M), Hassan 1st University, Faculty of Sciences Techniques, Settat, Morocco. This international conference highlighted essential research and developments in the fields of artificial intelligence and computer visions. The book is divided into sections, covering the following topics: Deep Learning and Applications; Smart Grid, Internet of Things, and Mobil Applications; Machine Learning and Metaheuristics Optimization; Business Intelligence and Applications; Machine Vision, Robotics, and Speech Recognition; Advanced Machine Learning Technologies; Big Data, Digital Transformation, AI and Network Analysis; Cybersecurity; Feature Selection, Classification, and Applications.

The arrival of the digital age has created the need to be able to store, manage, and digitally use an ever-increasing amount of video and audio material. Thus, video cataloguing has emerged as a requirement of the times. Video Cataloguing: Structure Parsing and Content Extraction explains how to efficiently perform video structure analysis as well as extract the basic semantic contents for video summarization, which is essential for handling large-scale video data. This book addresses the issues of video cataloguing, including video structure parsing and basic semantic word extraction, particularly for movie and teleplay videos. It starts by providing readers with a fundamental understanding of video structure parsing. It examines video shot boundary detection, recent research on video scene detection, and basic ideas for semantic word extraction, including video text recognition, scene recognition, and character identification. The book lists and introduces some of the most commonly used features in video analysis. It introduces and analyzes the most popular shot boundary detection methods and also presents recent research on movie scene detection as another important and critical step for video cataloguing, video indexing, and retrieval. The authors propose a robust movie scene recognition approach based on a panoramic frame and representative feature patch. They describe how to recognize characters in movies and TV series accurately and efficiently as well as how to use these character names as cataloguing items for an intelligent catalogue. The book proposes an interesting application of highlight extraction in basketball videos and concludes by demonstrating how to design and implement a prototype system of automatic movie and teleplay cataloguing (AMTC) based on the approaches introduced in the book.

Get up to speed on the nuances of NoSQL databases and what they mean for your organization This easy to read guide to NoSQL databases provides the type of no-nonsense overview and analysis that you need to learn, including what NoSQL is and which database is right for you. Featuring specific evaluation criteria for NoSQL databases, along with a look into the pros and cons of the most popular options, NoSQL For Dummies provides the fastest and easiest way to dive into the details of this incredible technology. You'll gain an understanding of how to use NoSQL databases for mission-critical enterprise architectures and projects, and real-world examples reinforce the primary points to create an action-oriented resource for IT pros. If you're planning a big data project or platform, you probably already know you need to select a NoSQL database to complete your architecture. But with options flooding the market and updates and add-ons coming at a rapid pace, determining what you require now, and in the future, can be a tall task. This is where NoSQL For Dummies comes in! * Learn the basic tenets of NoSQL databases and why they have come to the forefront as data has outpaced the capabilities of relational databases * Discover major players among NoSQL databases, including Cassandra, MongoDB, MarkLogic, Neo4J, and others * Get an in-depth look at the benefits and disadvantages of the wide variety of NoSQL database options * Explore the needs of your organization as they relate to the capabilities of specific NoSQL databases Big data and Hadoop get all the attention, but when it comes down to it, NoSQL databases are the engines that power many big data analytics initiatives. With NoSQL For Dummies, you'll go beyond relational databases to ramp up your enterprise's data architecture in no time.

In Data Sketches, Nadieh Bremer and Shirley Wu document the deeply creative process behind 24 unique data visualization projects, and they combine this with powerful technical insights which reveal the mindset behind coding creatively. Exploring 12 different themes - from the Olympics to Presidents & Royals and from Movies to Myths & Legends - each pair of visualizations explores different technologies and forms, blurring the boundary between visualization as an exploratory tool and an artform in its own right. This beautiful book provides an intimate, behind-the-scenes account of all 24 projects and shares the authors' personal notes and drafts every step of the way. The book features: Detailed information on data gathering, sketching, and coding data visualizations for the web, with screenshots of works-in-progress and reproductions from the authors' notebooks Never-before-published technical write-ups, with beginner-friendly explanations of core data visualization concepts Practical lessons based on the data and design challenges overcome during each project Full-color pages, showcasing all 24 final data visualizations This book is perfect for anyone interested or working in data visualization and information design, and especially those who want to take their work to the next level and are inspired by unique and compelling data-driven storytelling.

Blockchains have become increasingly important for organizing contemporary economic and social activities. This Element offers a deeper understanding of blockchains to both management scholars and practitioners, with an emphasis on blockchains' strategic implications for fundamental issues in organizing. It provides a critical examination of the core themes, theoretical lenses, and methodologies used in blockchain research in business and management scholarship. Furthermore, it offers an in-depth discussion of why and how blockchains offer a new way of organizing, providing profound implications for three major issues of strategic organization: contracting, trust, and organizational design. It also discusses several limitations of the technology in its current stage of development. Finally, this Element points to the implication of blockchains on both scholarly research and business practice.

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

Written by two very experienced instructors, with more than thirty years of teaching experience between them; Presents material that is grounded in practical applications that are representative of the problems researchers encounter in real life; Teaches readers the core features of modern JavaScript; Covers programming with callbacks and promises; Describes how to build data services and data visualization;

Innovation based in data analytics is a contemporary approach to developing empirically supported advances that encourage entrepreneurial activity inspired by novel marketing inferences. Data Analytics in Marketing, Entrepreneurship, and Innovation covers techniques, processes, models, tools, and practices for creating business opportunities through data analytics. It features case studies that provide realistic examples of applications. This multifaceted examination of data analytics looks at: Business analytics Applying predictive analytics Using discrete choice analysis for decision-making Marketing and customer analytics Developing new products Technopreneurship Disruptive versus incremental innovation The book gives researchers and practitioners insight into how data analytics is used in the areas of innovation, entrepreneurship, and marketing. Innovation analytics helps identify opportunities to develop new products and services, and improve existing methods of product manufacturing and service delivery. Entrepreneurial analytics facilitates the transformation of innovative ideas into strategy and helps entrepreneurs make critical decisions based on data-driven techniques. Marketing analytics is used in collecting, managing, assessing, and analyzing marketing data to predict trends, investigate customer preferences, and launch campaigns.

Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book's second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.

This new book in our popular 'Learning' series offers an easy-to-use resource for newcomers to the MySQL relational database. This tutorial explains in plain English how to set up MySQL and related software from the beginning, and how to do common tasks. The book also provides you with a foundation for more advanced database management. Readers new to the subject will gain a thorough grounding in database concepts and the most reliable ways to use MySQL. You'll learn the basics of Structured Query Language (SQL), which is used to insert and manipulate data, along with ways to: design a database; secure a database server; get more value out of a database; and, interact with the database through the medium most people prefer: a web server. Learning MySQL is the ideal introduction to relational databases, and MySQL in particular, for the growing number of people who are turning to databases for both large systems and small web-based applications: The perfect tutorial for the fast-growing database market.