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.

Among the features that make Noiseless Steganography: The Key to Covert Communications a first of its kind: The first to comprehensively cover Linguistic Steganography The first to comprehensively cover Graph Steganography The first to comprehensively cover Game Steganography Although the goal of steganography is to prevent adversaries from suspecting the existence of covert communications, most books on the subject present outdated steganography approaches that are detectable by human and/or machine examinations. These approaches often fail because they camouflage data as a detectable noise by altering digital images, audio files, text, etc. However, such alteration raises suspicion and makes the message discernible by detecting its noise. Addressing such shortcomings, Noiseless Steganography: The Key to Covert Communications introduces a novel Noiseless Steganography Paradigm (Nostega). Rather than hiding data in noise or producing noise, Nostega camouflages messages as well as their transmission in the form of unquestionable data in the generated steganographic cover. The book explains how to use Nostega to determine suitable domains capable of generating unsuspicious steganographic cover in which messages are embedded in the form of innocent data that is compatible with the chosen domain. It presents a number of Nostega-based methodologies, including but not limited to: A novel cover type that enables data to be hidden in plotted graphs A novel methodology that pursues popular games such as chess, checkers, crosswords, and dominoes to conceal messages Comprehensive coverage of linguistic steganography Several novel linguistic steganography methodologies based on Natural Language Processing and Computational Linguistic techniques such as: Education-Centric-Based, Summarization-Based, Natural Language Generation Based, Random-Series-Based, Email Headers Based, Automatic Joke Generation Based, List-Based, and Automatic Notes Generation Based The first book to provide comprehensive coverage of Linguistic Steganography, Graph Steganography, and Game Steganography, it discusses the implementation and steganalysis validation of ten Nostega-based methodologies. It describes how to establish covert channels by employing the selected domain to serve as justification for the interaction and delivery of the cover among the communicating parties. Instead of using contemporary steganography approaches to camouflage your data as noise that is assumed to look innocent, the text provides you with the tools to prevent your adversaries from suspecting the existence of covert communications altogether.

Corpus Annotation gives an up-to-date picture of this fascinating new area of research, and will provide essential reading for newcomers to the field as well as those already involved in corpus annotation. Early chapters introduce the different levels and techniques of corpus annotation. Later chapters deal with software developments, applications, and the development of standards for the evaluation of corpus annotation. While the book takes detailed account of research world-wide, its focus is particularly on the work of the UCREL (University Centre for Computer Corpus Research on Language) team at Lancaster University, which has been at the forefront of developments in the field of corpus annotation since its beginnings in the 1970s.

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 helps SAP architects and SAP Basis administrators deploy and operate SAP S/4HANA systems on the most common public cloud platforms. Market-leading cloud offerings are covered, including Amazon Web Services, Microsoft Azure, and Google Cloud. You will gain an end-to-end understanding of the initial implementation of SAP S/4HANA systems on those platforms. You will learn how to move away from the big monolithic SAP ERP systems and arrive at an environment with a central SAP S/4HANA system as the digital core surrounded by cloud-native services. The book begins by introducing the core concepts of Hyperscaler cloud platforms that are relevant to SAP. You will learn about the architecture of SAP S/4HANA systems on public cloud platforms, with specific content provided for each of the major platforms. The book simplifies the deployment of SAP S/4HANA systems in public clouds by providing step-by-step instructions and helping you deal with the complexity of such a deployment. Content in the book is based on best practices, industry lessons learned, and architectural blueprints, helping you develop deep insights into the operations of SAP S/4HANA systems on public cloud platforms. Reading this book enables you to build and operate your own SAP S/4HANA system in the public cloud with a minimum of effort. What You Will Learn Choose the right Hyperscaler platform for your future SAP S/4HANA workloads Start deploying your first SAP S/4HANA system in the public cloud Avoid typical pitfalls during your implementation Apply and leverage cloud-native services for your SAP S/4HANA system Save costs by choosing the right architecture and build a robust architecture for your most critical SAP systems Meet your business' criteria for availability and performance by having the right sizing in place Identify further use cases when operating SAP S/4HANA in the public cloud Who This Book Is For SAP architects looking for an answer on how to move SAP S/4HANA systems from on-premises into the cloud; those planning to deploy to one of the three major platforms from Amazon Web Services, Microsoft Azure, and Google Cloud Platform; and SAP Basis administrators seeking a detailed and realistic description of how to get started on a migration to the cloud and how to drive that cloud implementation to completion

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

This volume gathers selected peer-reviewed papers presented at the XXVI International Joint Conference on Industrial Engineering and Operations Management (IJCIEOM), held on July 8-11, 2020 in Rio de Janeiro, Brazil. The respective chapters address a range of timely topics in industrial engineering, including operations and process management, global operations, managerial economics, data science and stochastic optimization, logistics and supply chain management, quality management, product development, strategy and organizational engineering, knowledge and information management, work and human factors, sustainability, production engineering education, healthcare operations management, disaster management, and more. These topics broadly involve fields like operations, manufacturing, industrial and production engineering, and management. Given its scope, the book offers a valuable resource for those engaged in optimization research, operations research, and practitioners alike.

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.

"Covers all areas of computer-based data acquisition--from basic concepts to the most recent technical developments--without the burden of long theoretical derivations and proofs. Offers practical, solution-oriented design examples and real-life case studies in each chapter and furnishes valuable selection guides for specific types of hardware."

This book constitutes the proceedings of the 1st International Conference on Systems and Information Sciences (ICCIS), held in Manta, Ecuador, from July 27 to 29, 2020, and was jointly organized by Universidad Laica Eloy Alfaro de Manabi "ULEAM", in collaboration with GDEON. ICCIS aims to bring together systems and information sciences researchers and developers from academia and industry around the world to discuss cutting-edge research. The book covers the following topics: AI, Expert Systems and Big Data Analytics Cloud, IoT and Distributed Computing Communications Database System and Application Financial Technologies (FinTech), Economics and Business Engineering m-Learning and e-Learning Security Software Engineering Web Information Systems and Applications General Track

Computer System and Network Security provides the reader with a basic understanding of the issues involved in the security of computer systems and networks. Introductory in nature, this important new book covers all aspects related to the growing field of computer security. Such complete coverage in a single text has previously been unavailable, and college professors and students, as well as professionals responsible for system security, will find this unique book a valuable source of information, either as a textbook or as a general reference.
Computer System and Network Security discusses existing and potential threats to computer systems and networks and outlines the basic actions that are generally taken to protect them. The first two chapters of the text introduce the reader to the field of computer security, covering fundamental issues and objectives. The next several chapters describe security models, authentication issues, access control, intrusion detection, and damage control. Later chapters address network and database security and systems/networks connected to wide-area networks and internetworks. Other topics include firewalls, cryptography, malicious software, and security standards. The book includes case studies with information about incidents involving computer security, illustrating the problems and potential damage that can be caused when security fails.
This unique reference/textbook covers all aspects of computer and network security, filling an obvious gap in the existing literature.

This book is based on the best papers accepted for presentation during the International Conference on Mathematics and its Applications in New Computer Systems (MANCS-2021), Russia. The book includes research materials on modern mathematical problems, solutions in the field of cryptography, data analysis and modular computing, as well as scientific computing. The scope of numerical methods in scientific computing presents original research, including mathematical models and software implementations, related to the following topics: numerical methods in scientific computing; solving optimization problems; methods for approximating functions, etc. The studies in mathematical solutions to cryptography issues are devoted to secret sharing schemes, public key systems, private key systems, n-degree comparisons, modular arithmetic of simple, addition of points of an elliptic curve, Hasse theorem, homomorphic encryption and learning with error, and modifications of the RSA system. Furthermore, issues in data analysis and modular computing include contributions in the field of mathematical statistics, machine learning methods, deep learning, and neural networks. Finally, the book gives insights into the fundamental problems in mathematics education. The book intends for readership specializing in the field of cryptography, information security, parallel computing, computer technology, and mathematical education.

This book gathers papers on interactive and collaborative mobile learning environments, assessment, evaluation and research methods in mobile learning, mobile learning models, theory and pedagogy, open and distance mobile learning, life-long and informal learning using mobile devices, wearables and the Internet of Things, game-based learning, dynamic learning experiences, mobile systems and services for opening up education, mobile healthcare and training, case studies on mobile learning, and 5G network infrastructure. Today, interactive mobile technologies have become the core of many-if not all-fields of society. Not only do the younger generation of students expect a mobile working and learning environment, but also the new ideas, technologies and solutions introduced on a nearly daily basis also boost this trend. Discussing and assessing key trends in the mobile field were the primary aims of the 13th International Conference on Interactive Mobile Communication Technologies and Learning (IMCL2019), which was held in Thessaloniki, Greece, from 31 October to 01 November 2019. Since being founded in 2006, the conference has been devoted to new approaches in interactive mobile technologies, with a focus on learning. The IMCL conferences have since become a central forum of the exchange of new research results and relevant trends, as well as best practices. The book's intended readership includes policymakers, academics, educators, researchers in pedagogy and learning theory, schoolteachers, further education lecturers, practitioners in the learning industry, etc.

A quick and reliable way to build proven databases for core business functions
Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful First Edition, while adding a companion volume to take care of more specific requirements of different businesses. Each volume is accompanied by a CD-ROM, which is sold separately. Each CD-ROM provides powerful design templates discussed in the books in a ready-to-use electronic format, allowing companies and individuals to develop the databases they need at a fraction of the cost and a third of the time it would take to build them from scratch.
Updating the data models from the First Edition CD-ROM, this resource allows database developers to quickly load a core set of data models and customize them to support a wide range of business functions.

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.

DEEP LEARNING A concise and practical exploration of key topics and applications in data science In Deep Learning: From Big Data to Artificial Intelligence with R, expert researcher Dr. Stephane Tuffery delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on various software tools and deep learning methods relying on three major libraries: MXNet, PyTorch, and Keras-TensorFlow. In the book, numerous, up-to-date examples are combined with key topics relevant to modern data scientists, including processing optimization, neural network applications, natural language processing, and image recognition. This is a thoroughly revised and updated edition of a book originally released in French, with new examples and methods included throughout. Classroom-tested and intuitively organized, Deep Learning: From Big Data to Artificial Intelligence with R offers complimentary access to a companion website that provides R and Python source code for the examples offered in the book. Readers will also find: A thorough introduction to practical deep learning techniques with explanations and examples for various programming libraries Comprehensive explorations of a variety of applications for deep learning, including image recognition and natural language processing Discussions of the theory of deep learning, neural networks, and artificial intelligence linked to concrete techniques and strategies commonly used to solve real-world problems Perfect for graduate students studying data science, big data, deep learning, and artificial intelligence, Deep Learning: From Big Data to Artificial Intelligence with R will also earn a place in the libraries of data science researchers and practicing data scientists.

In engineering work and other practical situations, methods of a non-stop character are often needed. The computer intensive methods outlined in this book should show how to pass many obstacles that could not previously be overcome. Much emphasis in this book is placed on applications in science, economics, reliability, meteorology, medicine and transportation. In principle every area where data deserve statistical analyses there is a relevant application of these new methods. This book is aimed at classically educated statisticians as well as the younger generation.

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.

'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.