While laboratory research is the backbone of collecting experimental data in cognitive science, a rapidly increasing amount of research is now capitalizing on large-scale and real-world digital data. Each piece of data is a trace of human behavior and offers us a potential clue to understanding basic cognitive principles. However, we have to be able to put the pieces together in a reasonable way, which necessitates both advances in our theoretical models and development of new methodological techniques. The primary goal of this volume is to present cutting-edge examples of mining large-scale and naturalistic data to discover important principles of cognition and evaluate theories that would not be possible without such a scale. This book also has a mission to stimulate cognitive scientists to consider new ways to harness big data in order to enhance our understanding of fundamental cognitive processes. Finally, this book aims to warn of the potential pitfalls of using, or being over-reliant on, big data and to show how big data can work alongside traditional, rigorously gathered experimental data rather than simply supersede it. In sum, this groundbreaking volume presents cognitive scientists and those in related fields with an exciting, detailed, stimulating, and realistic introduction to big data - and to show how it may greatly advance our understanding of the principles of human memory, perception, categorization, decision-making, language, problem-solving, and representation.

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.

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies. The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendation systems in social media, and evolving systems. It also covers emerging trends, such as big data analysis and social media evolution. Describing how to conduct proper analysis of the social and digital media markets, the book provides insights into processing, storing, and visualizing big social media data and social graphs. It includes coverage of graphs in social and digital media, graph and hyper-graph fundamentals, mathematical foundations coming from linear algebra, algebraic graph analysis, graph clustering, community detection, graph matching, web search based on ranking, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and dimensionality reduction, and much more. This book is an ideal reference for scientists and engineers working in social media and digital media production and distribution. It is also suitable for use as a textbook in undergraduate or graduate courses on digital media, social media, or social networks.

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.

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.

Intelligent Data Mining and Analysis in Power and Energy Systems A hands-on and current review of data mining and analysis and their applications to power and energy systems In Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems, the editors assemble a team of distinguished engineers to deliver a practical and incisive review of cutting-edge information on data mining and intelligent data analysis models as they relate to power and energy systems. You'll find accessible descriptions of state-of-the-art advances in intelligent data mining and analysis and see how they drive innovation and evolution in the development of new technologies. The book combines perspectives from authors distributed around the world with expertise gained in academia and industry. It facilitates review work and identification of critical points in the research and offers insightful commentary on likely future developments in the field. It also provides: A thorough introduction to data mining and analysis, including the foundations of data preparation and a review of various analysis models and methods In-depth explorations of clustering, classification, and forecasting Intensive discussions of machine learning applications in power and energy systems Perfect for power and energy systems designers, planners, operators, and consultants, Intelligent Data Mining and Analysis in Power and Energy Systems will also earn a place in the libraries of software developers, researchers, and students with an interest in data mining and analysis problems.

Get introduced to Azure Arc-enabled Data Services and the powerful capabilities to deploy and manage local, on-premises, and hybrid cloud data resources using the same centralized management and tooling you get from the Azure cloud. This book shows how you can deploy and manage databases running on SQL Server and Postgres in your corporate data center or any cloud as if they were part of the Azure platform. This second edition has been updated to the latest codebase, allowing you to use this book as your handbook to get started with Azure Arc-enabled Data Services today. Learn how to benefit from Azure's centralized management, the automated rollout of patches and updates, managed backups, and more. This book is the perfect choice for anyone looking for a hybrid or multi-vendor cloud strategy for their data estate. The authors walk you through the possibilities and requirements to get Azure SQL Managed Instance and PostgresSQL Hyperscale deployed outside of Azure, so the services are accessible to companies that cannot move to the cloud or do not want to use the Microsoft cloud exclusively. The technology described in this book will benefit those required to keep sensitive services, such as medical databases, away from the public cloud equally as those who can't move to a public cloud for other reasons such as infrastructure constraints but still want to benefit from the Azure cloud and the centralized management and tooling that it supports. What You Will Learn Understand the fundamentals and architecture of Azure Arc-enabled data services Build a multi-cloud strategy based on Azure Data Services Deploy Azure Arc-enabled data services on premises or in any cloud Deploy Azure Arc-enabled SQL Managed Instance on premises or in any cloud Deploy Azure Arc-enabled PostgreSQL Hyperscale on premises or in any cloud Backup and Restore your data that is managed by Azure Arc-enabled data services Manage Azure-enabled data services running outside of Azure Monitor Azure-enabled data services through Grafana and Kibana Monitor Azure-enabled data services running outside of Azure through Azure Monitor Who This Book Is For Database administrators and architects who want to manage on-premises or hybrid cloud data resources from the Microsoft Azure cloud. Especially for those wishing to take advantage of cloud technologies while keeping sensitive data on premises and under physical control.

Social media has put mass communication in the hands of normal people on an unprecedented scale, and has also given social scientists the tools necessary to listen to the voices of everyday people around the world. This book gives social scientists the skills necessary to leverage that opportunity, and transform social media's vast stream of information into social science data. The book combines the big data techniques of computer science with social science methodology. Intended as a text for advanced undergraduates, graduate students, and researchers in the social sciences, this book provides a methodological pathway for scholars who want to make use of this new and evolving source of data. It provides a framework for building one's own data collection and analysis infrastructure, a toolkit of content analysis, geographic analysis, and network analysis, and meditations on the ethical implications of social media data.

GENSTAT is a general purpose statistical computing system with a flexible command language operating on a variety of data structures. It may be used on a number of computer ranges, either interactively for exploratory data analysis, or in batch mode for standard data analysis.
The great flexibility of GENSTAT is demonstrated in this handbook by analysing the wide range of examples discussed in Applied Statistics - Principles and Examples (Cox and Snell, 1981). GENSTAT programs are listed for each of the examples. Most of the data sets are small but often it is these seemingly small problems which involve the most tricky statistical and computational procedures. This handbook is self-contained although for a full description of the analysis and interpretation it should be used in parallel with Applied Statistics - Principles and Examples.

First Published in 2004. Learning how to analyze qualitative data by computer can be fun. That is one assumption underpinning this introduction to qualitative analysis, which takes account of how computing techniques have enhanced and transformed the field. The author provides a practical discussion of the main procedures for analyzing qualitative data by computer, with most of its examples taken from humour or everyday life. He examines ways in which computers can contribute to greater rigour and creativity, as well as greater efficiency in analysis. He discusses some of the pitfalls and paradoxes as well as the practicalities of computer-based qualitative analysis. The perspective of "Qualitative Data Analysis" is pragmatic rather than prescriptive, introducing different possibilities without advocating one particular approach. The result is a largely discipline-neutral text, which is suitable for arts and social science students and first-time qualitative analysts.

Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event Mining: Algorithms and Applications presents state-of-the-art event mining approaches and applications with a focus on computing system management. The book first explains how to transform log data in disparate formats and contents into a canonical form as well as how to optimize system monitoring. It then shows how to extract useful knowledge from data. It describes intelligent and efficient methods and algorithms to perform data-driven pattern discovery and problem determination for managing complex systems. The book also discusses data-driven approaches for the detailed diagnosis of a system issue and addresses the application of event summarization in Twitter messages (tweets). Understanding the interdisciplinary field of event mining can be challenging as it requires familiarity with several research areas and the relevant literature is scattered in diverse publications. This book makes it easier to explore the field by providing both a good starting point for readers not familiar with the topics and a comprehensive reference for those already working in this area.

Digital rights management (DRM) is a type of server software developed to enable secure distribution - and perhaps more importantly, to disable illegal distribution - of paid content over the Web. DRM technologies are being developed as a means of protection against the online piracy of commercially marketed material, which has proliferated through the widespread use of Napster and other peer-to-peer file exchange programs. With the flourish of these file exchange programs, content owners, creators and producers need to have a plan to distribute their content digitally and protect it at the same time-a seemingly impossible task. There are numerous books dealing with copyright, eBusiness, the Internet, privacy, security, content management, and related technical subjects. Additionally, there are several research papers, and almost daily newspaper and magazine articles dealing with digital piracy. However, there are only a few books and documents that bring these together as a basis for profitable exchange of digital content. Digital Rights Management can help content providers make money by unifying the confusing array of concepts that swirl around current presentations of DRM in newspapers and business publications.

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.

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.

Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the text instills a working understanding of key statistical and computing ideas that can be readily applied in research and practice. Offering balanced coverage of methodology, theory, and applications, this handbook: Describes modern, scalable approaches for analyzing increasingly large datasets Defines the underlying concepts of the available analytical tools and techniques Details intercommunity advances in computational statistics and machine learning Handbook of Big Data also identifies areas in need of further development, encouraging greater communication and collaboration between researchers in big data sub-specialties such as genomics, computational biology, and finance.

This is the first comprehensive overview of the 'science of science,' an emerging interdisciplinary field that relies on big data to unveil the reproducible patterns that govern individual scientific careers and the workings of science. It explores the roots of scientific impact, the role of productivity and creativity, when and what kind of collaborations are effective, the impact of failure and success in a scientific career, and what metrics can tell us about the fundamental workings of science. The book relies on data to draw actionable insights, which can be applied by individuals to further their career or decision makers to enhance the role of science in society. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists and graduate students, policymakers, and administrators with an interest in the wider scientific enterprise.

Artificial intelligence is changing the world of work. How can HR professionals understand the variety of opportunities AI has created for the HR function and how best to implement these in their organization? This book provides the answers. From using natural language processing to ensure job adverts are free from bias and gendered language to implementing chatbots to enhance the employee experience, artificial intelligence can add value throughout the work of HR professionals. Artificial Intelligence for HR demonstrates how to leverage this potential and use AI to improve efficiency and develop a talented and productive workforce. Outlining the current technology landscape as well as the latest AI developments, this book ensures that HR professionals fully understand what AI is and what it means for HR in practice. Alongside coverage of employee engagement and recruitment, this second edition features new material on applications of AI for virtual work, reskilling and data integrity. Packed with practical advice, research and new and updated case studies from global organizations including Uber, IBM and Unilever, the second edition of Artificial Intelligence for HR will equip HR professionals with the knowledge they need to improve people operational efficiencies, and allow AI solutions to become enhancements for driving business success.

This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website.

Malicious hackers utilize the World Wide Web to share knowledge. Analyzing the online communication of these threat actors can help reduce the risk of attacks. This book shifts attention from the defender environment to the attacker environment, offering a new security paradigm of 'proactive cyber threat intelligence' that allows defenders of computer networks to gain a better understanding of their adversaries by analyzing assets, capabilities, and interest of malicious hackers. The authors propose models, techniques, and frameworks based on threat intelligence mined from the heart of the underground cyber world: the malicious hacker communities. They provide insights into the hackers themselves and the groups they form dynamically in the act of exchanging ideas and techniques, buying or selling malware, and exploits. The book covers both methodology - a hybridization of machine learning, artificial intelligence, and social network analysis methods - and the resulting conclusions, detailing how a deep understanding of malicious hacker communities can be the key to designing better attack prediction systems.

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;

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

Image and video signals require large transmission bandwidth and storage, leading to high costs. The data must be compressed without a loss or with a small loss of quality. Thus, efficient image and video compression algorithms play a significant role in the storage and transmission of data. Image and Video Compression: Fundamentals, Techniques, and Applications explains the major techniques for image and video compression and demonstrates their practical implementation using MATLAB (R) programs. Designed for students, researchers, and practicing engineers, the book presents both basic principles and real practical applications. In an accessible way, the book covers basic schemes for image and video compression, including lossless techniques and wavelet- and vector quantization-based image compression and digital video compression. The MATLAB programs enable readers to gain hands-on experience with the techniques. The authors provide quality metrics used to evaluate the performance of the compression algorithms. They also introduce the modern technique of compressed sensing, which retains the most important part of the signal while it is being sensed.

A practical guide to making good decisions in a world of missing data In the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but nonetheless present, the universe of information is full of dark data that we overlook at our peril. In Dark Data, data expert David Hand takes us on a fascinating and enlightening journey into the world of the data we don't see. Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them. In doing so, he teaches us not only to be alert to the problems presented by the things we don't know, but also shows how dark data can be used to our advantage, leading to greater understanding and better decisions. Today, we all make decisions using data. Dark Data shows us all how to reduce the risk of making bad ones.