As digital technologies occupy a more central role in working and everyday human life, individual and social realities are increasingly constructed and communicated through digital objects, which are progressively replacing and representing physical objects. They are even shaping new forms of virtual reality. This growing digital transformation coupled with technological evolution and the development of computer computation is shaping a cyber society whose working mechanisms are grounded upon the production, deployment, and exploitation of big data. In the arts and humanities, however, the notion of big data is still in its embryonic stage, and only in the last few years, have arts and cultural organizations and institutions, artists, and humanists started to investigate, explore, and experiment with the deployment and exploitation of big data as well as understand the possible forms of collaborations based on it. Big Data in the Arts and Humanities: Theory and Practice explores the meaning, properties, and applications of big data. This book examines therelevance of big data to the arts and humanities, digital humanities, and management of big data with and for the arts and humanities. It explores the reasons and opportunities for the arts and humanities to embrace the big data revolution. The book also delineates managerial implications to successfully shape a mutually beneficial partnership between the arts and humanities and the big data- and computational digital-based sciences. Big data and arts and humanities can be likened to the rational and emotional aspects of the human mind. This book attempts to integrate these two aspects of human thought to advance decision-making and to enhance the expression of the best of human life.

This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques. This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and dates The difference between different data structures and how to create, add additional components to, and subset each data structure How to acquire and parse data from locations previously inaccessible How to develop functions and use loop control structures to reduce code redundancy How to use pipe operators to simplify code and make it more readable How to reshape the layout of data and manipulate, summarize, and join data sets

In recent years, investments by cloud companies in mega data centers and associated network infrastructure has created a very active and dynamic segment in the optical components and modules market. Optical interconnect technologies at high speed play a critical role for the growth of mega data centers, which flood the networks with unprecedented amount of data traffic. Datacenter Connectivity Technologies: Principles and Practice provides a comprehensive and in-depth look at the development of various optical connectivity technologies which are making an impact on the building of data centers. The technologies span from short range connectivity, as low as 100 meters with multi-mode fiber (MMF) links inside data centers, to long distances of hundreds of kilometers with single-mode fiber (SMF) links between data centers. This book is the first of its kind to address various advanced technologies connecting data centers. It represents a collection of achievements and the latest developments from well-known industry experts and academic researchers active in this field. Technical topics covered in this book include: - Mega data center requirements - High volume VCSELs - Directly modulated lasers - Electro-absorption modulated lasers - Pulse amplitude modulation (PAM) - Discrete Multi-Tone modulation (DMT) - Optical Duobinary Transmission - Optical fibers and connectors - Mach-zenhder modulators - Silicon photonics - Optical waveguide devices and packaging - Testing and measurements - Advanced modulation formats - Optical coherent networks - High-speed IC design & packaging

A new approach to distributed large-scale data mining, service-oriented knowledge discovery extracts useful knowledge from today's often unmanageable volumes of data by exploiting data mining and machine learning distributed models and techniques in service-oriented infrastructures. Service-Oriented Distributed Knowledge Discovery presents techniques, algorithms, and systems based on the service-oriented paradigm. Through detailed descriptions of real software systems, it shows how the techniques, models, and architectures can be implemented. The book covers key areas in data mining and service-oriented computing. It presents the concepts and principles of distributed knowledge discovery and service-oriented data mining. The authors illustrate how to design services for data analytics, describe real systems for implementing distributed knowledge discovery applications, and explore mobile data mining models. They also discuss the future role of service-oriented knowledge discovery in ubiquitous discovery processes and large-scale data analytics. Highlighting the latest achievements in the field, the book gives many examples of the state of the art in service-oriented knowledge discovery. Both novices and more seasoned researchers will learn useful concepts related to distributed data mining and service-oriented data analysis. Developers will also gain insight on how to successfully use service-oriented knowledge discovery in databases (KDD) frameworks.

This book offers a comprehensive guide to the world of metadata, from its origins in the ancient cities of the Middle East, to the Semantic Web of today. The author takes us on a journey through the centuries-old history of metadata up to the modern world of crowdsourcing and Google, showing how metadata works and what it is made of. The author explores how it has been used ideologically and how it can never be objective. He argues how central it is to human cultures and the way they develop. Metadata: Shaping Knowledge from Antiquity to the Semantic Web is for all readers with an interest in how we humans organize our knowledge and why this is important. It is suitable for those new to the subject as well as those know its basics. It also makes an excellent introduction for students of information science and librarianship.

Understanding Databases: Concepts and Practice is an accessible, highly visual introduction to database systems for undergraduate students across many majors. Designed for self-contained first courses in the subject, this interactive e-textbook covers fundamental database topics including conceptual design, the relational data model, relational algebra and calculus, Structured Query Language (SQL), database manipulation, transaction management, and database design theory. Visual components and self-assessment features provide a more engaging and immersive method of learning that enables students to develop a solid foundation in both database theory and practical application. Concise, easy-to-digest chapters offer ample opportunities for students to practice and master the material, and include a variety of solved real-world problems, self-check questions, and hands-on collaborative activities that task students to build a functioning database. This Enhanced eText also offers interactive multiple-choice questions with immediate feedback that allow students to self-assess as they proceed through the book. Case studies, illustrative examples, color summary figures and tables with annotations, and other pedagogical tools are integrated throughout the text to increase comprehension and retention of key concepts and help strengthen students' problem-solving skills.

A pragmatic approach to Big Data by taking the reader on a journey between Big Data (what it is) and the Smart Data (what it is for). Today s decision making can be reached via information (related to the data), knowledge (related to people and processes), and timing (the capacity to decide, act and react at the right time). The huge increase in volume of data traffic, and its format (unstructured data such as blogs, logs, and video) generated by the digitalization of our world modifies radically our relationship to the space (in motion) and time, dimension and by capillarity, the enterprise vision of performance monitoring and optimization.

Work with all aspects of batch processing in a modern Java environment using a selection of Spring frameworks. This book provides up-to-date examples using the latest configuration techniques based on Java configuration and Spring Boot. The Definitive Guide to Spring Batch takes you from the "Hello, World!" of batch processing to complex scenarios demonstrating cloud native techniques for developing batch applications to be run on modern platforms. Finally this book demonstrates how you can use areas of the Spring portfolio beyond just Spring Batch 4 to collaboratively develop mission-critical batch processes. You'll see how a new class of use cases and platforms has evolved to have an impact on batch-processing. Data science and big data have become prominent in modern IT and the use of batch processing to orchestrate workloads has become commonplace. The Definitive Guide to Spring Batch covers how running finite tasks on cloud infrastructure in a standardized way has changed where batch applications are run. Additionally, you'll discover how Spring Batch 4 takes advantage of Java 9, Spring Framework 5, and the new Spring Boot 2 micro-framework. After reading this book, you'll be able to use Spring Boot to simplify the development of your own Spring projects, as well as take advantage of Spring Cloud Task and Spring Cloud Data Flow for added cloud native functionality. Includes a foreword by Dave Syer, Spring Batch project founder. What You'll Learn Discover what is new in Spring Batch 4 Carry out finite batch processing in the cloud using the Spring Batch project Understand the newest configuration techniques based on Java configuration and Spring Boot using practical examples Master batch processing in complex scenarios including in the cloud Develop batch applications to be run on modern platforms Use areas of the Spring portfolio beyond Spring Batch to develop mission-critical batch processes Who This Book Is For Experienced Java and Spring coders new to the Spring Batch platform. This definitive book will be useful in allowing even experienced Spring Batch users and developers to maximize the Spring Batch tool.

This book focuses on understanding the analytics knowledge management process and its comprehensive application to various socioeconomic sectors. Using cases from Latin America and other emerging economies, it examines analytics knowledge applications where a solution has been achieved. Written for business students and professionals as well as researchers, the book is filled with practical insight into applying concepts and implementing processes and solutions. The eleven case studies presented in the book incorporate the whole analytics process and are useful reference examples for applying the analytics process for SME organizations in both developing and developed economies. The cases also identify multiple tacit factors to deal with during the implementation of analytics knowledge management processes. These factors, which include data cleaning, data gathering, and interpretation of results, are not always easily identified by analytics practitioners. This book promotes the understanding of analytics methods and techniques. It guides readers through numerous techniques and methods available to analytics practitioners by explaining the strengths and weaknesses of these methods and techniques.

Experiment and Evaluation in Information Retrieval Models explores different algorithms for the application of evolutionary computation to the field of information retrieval (IR). As well as examining existing approaches to resolving some of the problems in this field, results obtained by researchers are critically evaluated in order to give readers a clear view of the topic. In addition, this book covers Algorithmic Solutions to the Problems in Advanced IR Concepts, including Feature Selection for Document Ranking, web page classification and recommendation, Facet Generation for Document Retrieval, Duplication Detection and seeker satisfaction in question answering community Portals. Written with students and researchers in the field on information retrieval in mind, this book is also a useful tool for researchers in the natural and social sciences interested in the latest developments in the fast-moving subject area. Key features: Focusing on recent topics in Information Retrieval research, Experiment and Evaluation in Information Retrieval Models explores the following topics in detail: Searching in social media Using semantic annotations Ranking documents based on Facets Evaluating IR systems offline and online The role of evolutionary computation in IR Document and term clustering, Image retrieval Design of user profiles for IR Web page classification and recommendation Relevance feedback approach for Document and image retrieval

Big Data: A Tutorial-Based Approach explores the tools and techniques used to bring about the marriage of structured and unstructured data. It focuses on Hadoop Distributed Storage and MapReduce Processing by implementing (i) Tools and Techniques of Hadoop Eco System, (ii) Hadoop Distributed File System Infrastructure, and (iii) efficient MapReduce processing. The book includes Use Cases and Tutorials to provide an integrated approach that answers the 'What', 'How', and 'Why' of Big Data. Features Identifies the primary drivers of Big Data Walks readers through the theory, methods and technology of Big Data Explains how to handle the 4 V's of Big Data in order to extract value for better business decision making Shows how and why data connectors are critical and necessary for Agile text analytics Includes in-depth tutorials to perform necessary set-ups, installation, configuration and execution of important tasks Explains the command line as well as GUI interface to a powerful data exchange tool between Hadoop and legacy r-dbms databases

This book provides a snapshot of the implementation in various countries around the world of the international cataloging standard RDA: resource description and access. All stages of implementing a new standard are covered, from initial assessment and impact analysis through translation, staff training, and data migration, to implementation and user orientation. Contributions include the results of detailed research into awareness of the standard in professional groups, differences between catalog metadata produced using RDA and current local standards, and the effect of RDA on the presentation of catalog displays to the end user. The contributions cover aspects of RDA implementation in Canada, China, German-speaking countries, Iran, Israel, Mexico, the Philippines, Singapore, and Turkey, and French, German and Spanish translation activity. The information contained will be relevant for many years to come, for those who are intending to implement RDA, review the quality of legacy data, measure the impact of the globalization of cataloguing data, or prepare for education and orientation in international bibliographic standards. This book was published as a special double issue of Cataloging and Classification Quarterly.

Recent technological advancements and other related factors and trends are contributing to the production of an astoundingly large and rapidly accelerating collection of data, or 'Big Data'. This data now allows us to examine urban and regional phenomena in ways that were previously not possible. Despite the tremendous potential of big data for regional science, its use and application in this context is fraught with issues and challenges. This book brings together leading contributors to present an interdisciplinary, agenda-setting and action-oriented platform for research and practice in the urban and regional community. This book provides a comprehensive, multidisciplinary and cutting-edge perspective on big data for regional science. Chapters contain a collection of research notes contributed by experts from all over the world with a wide array of disciplinary backgrounds. The content is organized along four themes: sources of big data; integration, processing and management of big data; analytics for big data; and, higher level policy and programmatic considerations. As well as concisely and comprehensively synthesising work done to date, the book also considers future challenges and prospects for the use of big data in regional science. Big Data for Regional Science provides a seminal contribution to the field of regional science and will appeal to a broad audience, including those at all levels of academia, industry, and government.

With the advancements of semantic web, ontology has become the crucial mechanism for representing concepts in various domains. For research and dispersal of customized healthcare services, a major challenge is to efficiently retrieve and analyze individual patient data from a large volume of heterogeneous data over a long time span. This requirement demands effective ontology-based information retrieval approaches for clinical information systems so that the pertinent information can be mined from large amount of distributed data. This unique and groundbreaking book highlights the key advances in ontology-based information retrieval techniques being applied in the healthcare domain and covers the following areas: Semantic data integration in e-health care systems Keyword-based medical information retrieval Ontology-based query retrieval support for e-health implementation Ontologies as a database management system technology for medical information retrieval Information integration using contextual knowledge and ontology merging Collaborative ontology-based information indexing and retrieval in health informatics An ontology-based text mining framework for vulnerability assessment in health and social care An ontology-based multi-agent system for matchmaking patient healthcare monitoring A multi-agent system for querying heterogeneous data sources with ontologies for reducing cost of customized healthcare systems A methodology for ontology based multi agent systems development Ontology based systems for clinical systems: validity, ethics and regulation

Private clouds allow for managing multiple databases under one roof, avoiding unnecessary resource management. Private cloud solutions can be applied in sectors such as healthcare, retail, and software. The Introduction to Private Cloud using Oracle Exadata and Oracle Database will explore the general architecture of private cloud databases with a focus on Oracle's Exadata database machine. The book describes the private cloud using fundamental-level Exadata and database. Exadata has been Oracle's pioneer product for almost a decade. In the last few years, Oracle has positioned Exadata for customers to consume as a cloud service. This book will provide a timely introduction to Exadata for current and potential Oracle customers and other IT professionals.

Fundamentals of Database Indexing and Searching presents well-known database searching and indexing techniques. It focuses on similarity search queries, showing how to use distance functions to measure the notion of dissimilarity. After defining database queries and similarity search queries, the book organizes the most common and representative index structures according to their characteristics. The author first describes low-dimensional index structures, memory-based index structures, and hierarchical disk-based index structures. He then outlines useful distance measures and index structures that use the distance information to efficiently solve similarity search queries. Focusing on the difficult dimensionality phenomenon, he also presents several indexing methods that specifically deal with high-dimensional spaces. In addition, the book covers data reduction techniques, including embedding, various data transforms, and histograms. Through numerous real-world examples, this book explores how to effectively index and search for information in large collections of data. Requiring only a basic computer science background, it is accessible to practitioners and advanced undergraduate students.

From their shadowy origins in Bitcoin to their use by multinational corporations, cryptocurrencies and blockchains are remaking the rules of digital media and society. Meanwhile, regulators, governments, and the public are trying to make sense of it all. In this accessible book, Quinn DuPont guides readers through the changing face of money to show how blockchain technology underpins new forms of value exchange and social coordination. He introduces cryptocurrency and blockchain technology to readers in terms of their developers and users, investment opportunities and risks, changes to politics and law, social and industrial applications - and what this all means for the new economy. The author argues throughout that, rather than being a technical innovation, cryptocurrencies and blockchains are social technologies enabling developers and users to engage in unprecedented experiments with social and political levers. Cryptocurrencies and Blockchains dispenses with hype and offers sober reflection on this crucial and timely topic. It is essential reading for students and scholars of culture, politics, media, and the economy, as well as anyone who wants to understand, take part in, or change the future of work and society.

Discover how blockchain will facilitate a new currency that will transcend space and time Largely inspired by The Denationalization of Money by Fredrich Hayek, Money Without Boundaries' ideological foundation is also inspired by economists and thought leaders like Milton Friedman and Irving Fisher, advancements in capital markets over the past 50 years, and the convergence of old and new technologies. Author Thomas J. Anderson explains how blockchain acts as the filter and the glue, making it all possible. Compared with other currencies, blockchain-managed money markets are more straightforward and transparent. It is easier to monitor, understand, and assess the quality of their "full-faith and credit." Money Without Boundaries shows how not only money, but also the process of borrowing and lending, will evolve to be conducted in a 100% trusted, secure, transparent, open architecture environment. Anderson begins with a history of money and discusses the rise of cryptocurrency, concluding with a comparison of decentralized money markets to all other alternatives. Money without Boundaries: - Demonstrates how blockchain technology allows full transparency - Explains how blockchain makes it possible for money to be fully commoditized - Explains how this fully market-based, decentralized, self-regulating system has vast implications throughout the global financial system - Shows how everyone will benefit when they have the opportunity to compete on "full faith in credit" If you are interested in cryptocurrency, money, monetary theory, or understanding how the applied uses of blockchain technology will change your everyday life, this is essential reading.

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

In just 24 lessons of one hour or less, Sams Teach Yourself SQL in 24 Hours helps you use SQL to build effective databases, efficiently retrieve data, and manage everything from performance to security. This Seventh Edition is thoroughly revised and reorganized for faster learning and a deeper understanding of modern SQL development. Based on standardized SQL throughout, it teaches using new sample code based on the free, easy-to-use Oracle Database Express (XE). You'll find more hands-on examples than ever, culminating in an all-new Bonus Exercises Workshop with even more real-world practice. This guide's straightforward, step-by-step approach shows you how to work with database structures, objects, queries, tables, and more. In just hours, you will be applying advanced techniques, from transactions and joins to complex data retrieval using views and subqueries. Step-by-step instructions carefully walk you through the most common SQL tasks. Practical, hands-on examples show you how to apply what you learn. Quizzes and exercises help you test your knowledge and stretch your skills. Notes and tips point out shortcuts and solutions. Learn how to... * Master core relational database concepts, SQL concepts, and language components * Clearly understand your data * Set up databases and plan efficient database designs * Define entities and relationships, establish data structures, and create database objects * "Normalize" raw databases into logically organized tables * Edit relational data and tables and manage transactions * Write effective, well-performing queries * Categorize, summarize, sort, group, and restructure data * Work with dates and times * Join tables in queries, use subqueries, and combine multiple queries * Optimize performance with indexes and other techniques * Administer databases and manage users * Secure databases and data

Business Process Management (BPM) has become one of the most widely used approaches for the design of modern organizational and information systems. The conscious treatment of business processes as significant corporate assets has facilitated substantial improvements in organizational performance but is also used to ensure the conformance of corporate activities. This Handbook presents in two volumes the contemporary body of knowledge as articulated by the world' s leading BPM thought leaders. This first volume focuses on arriving at a sound definition of BPM approaches and examines BPM methods and process-aware information systems. As such, it provides guidance for the integration of BPM into corporate methodologies and information systems. Each chapter has been contributed by leading international experts. Selected case studies complement their views and lead to a summary of BPM expertise that is unique in its coverage of the most critical success factors of BPM.

The second edition of this handbook has been significantly revised and extended. Each chapter has been updated to reflect the most current developments. This includes in particular new technologies such as in-memory data and process management, social media and networks. A further focus of this revised and extended edition is on the actual deployment of the proposed theoretical concepts. This volume includes a number of entire new chapters from some of the world's leading experts in the domain of BPM.

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Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.

Take a deep dive into the Azure SQL Database Hyperscale Service Tier and discover a new form of cloud architecture from Microsoft that supports massive databases. The new horizontally scalable architecture, formerly code-named Socrates, allows you to decouple compute nodes from storage layers. This radically different approach dramatically increases the scalability of the service. This book shows you how to leverage Hyperscale to provide next-level scalability, high throughput, and fast performance from large databases in your environment. The book begins by showing how Hyperscale helps you eliminate many of the problems of traditional high-availability and disaster recovery architecture. You'll learn how Hyperscale overcomes storage capacity limitations and issues with scale-up times and costs. With Hyperscale, your costs do not increase linearly with database size and you can manage more data than ever at a lower cost. The book teaches you how to deploy, configure, and monitor an Azure SQL Hyperscale database in a production environment. The book also covers migrating your current workloads from traditional architecture to Azure SQL Hyperscale. What You Will Learn Understand the advantages of Hyperscale over traditional architecture Deploy a Hyperscale database on the Azure cloud (interactively and with code) Configure the advanced features of the Hyperscale database tier Monitor and scale database performance to suit your needs Back up and restore your Azure SQL Hyperscale databases Implement disaster recovery and failover capability Compare performance of Hyperscale vs traditional architecture Migrate existing databases to the Hyperscale service tier Who This Book Is For SQL architects, data engineers, and DBAs who want the most efficient and cost-effective cloud technologies to run their critical data workloads, and those seeking rapid scalability and high performance and throughput while utilizing large databases

Business data integration is a complex problem that must be solved when organizations change or enhance their internal structures. The goal of this book is to present a simple yet thorough resource that describes the challenges of business data integration and the solutions to these challenges such as schema integration, illustrated through an Operational Data Store (ODS) case study. The book contains three sections spanning ten chapters. Section I, Foundational Concepts, will provide you with the necessary basic concepts and discuss schema integration. Section II, Preparation and Design, introduces the case study and we will reverse engineer each of the data sources to create a set of data dictionary reports which will provide us with the meta data we need to apply the schema integration process. Section III, Physical Implementation, will present scripts to populate each of the source databases and spreadsheets and use reports to create Extract, Transform, and Load (ETL) specifications.

The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms.