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Elsevier

Libros en Datos

  • Ethics of Blockchain by Design

    • 1 Edición
    • Muthu Ramachandran
    • Inglés
    The increasing integration of blockchain into critical sectors such as finance, healthcare, and governance has prompted a pressing need to address ethical considerations from the ground up. While blockchain offers revolutionary potential for transparency, decentralization, and trust, it also introduces profound challenges around accountability, privacy, inclusion, and governance. Current literature often focuses on the technological and economic dimensions of blockchain but overlooks its ethical implications, leaving a critical gap in both research and practice. Ethics of Blockchain by Design addresses this gap by offering a comprehensive, design-centred framework that integrates ethical principles into every stage of blockchain system development. Rooted in systems engineering, the book systematically explores how ethical requirements can be specified, implemented, and validated within decentralized technologies. It goes beyond theoretical discussions by providing actionable methodologies, case studies, governance models, and compliance tools, making it essential reading for blockchain architects, developers, students, and policy makers. This book positions itself at the intersection of engineering, ethics, and decentralized innovation, and builds on the author’s extensive contributions to AI ethics, secure software systems, and blockchain in healthcare. As the demand for responsible digital infrastructure grows, Ethics of Blockchain by Design offers the first structured guide to ensuring blockchain technologies are not only technically sound but also ethically robust and socially accountable.

  • Next-Generation VAPT

    Combining OSINT and AI-ML for Enhanced Vulnerability Assessment and Penetration Testing
    • 1 Edición
    • Ravi Sheth + 4 más
    • Inglés
    Next-Generation VAPT: Combining OSINT and AI/ML for Enhanced Vulnerability Assessment and Penetration Testing empowers information security threat intelligence with the fusion of Open-Source Intelligence Tools (OSINT) and Artificial Intelligence and Machine Learning (AI/ML) to identify and detect vulnerabilities more efficiently and effectively. The book highlights the concept of OSINT for gathering actionable threat intelligence and AI/ML for automating threat detection, which ultimately enhances VAPT processes. The authors explain concepts for assessing the security of networks, wireless networks, performing VAPT on mobile devices, and conducting red teaming, which helps end users deal with upcoming threats at an early stage.It focuses on using machine intelligence for developing and validating exploits and AI-driven exploitation testing, which helps field experts discover and exploit vulnerabilities more quickly and accurately. Readers will find essential ethical and legal considerations in AI/ML and OSINT-based VAPT, ensuring responsible and compliant testing practices, accountability, and transparency. Industry Case Studies with solutions and safeguards are an integral part of the books applied approach.

  • Cyber Threat Intelligence for Cloud Systems

    A Comprehensive Guide to Becoming an Analyst
    • 1 Edición
    • Gianni D'Angelo
    • Inglés
    Cyber Threat Intelligence for Cloud Systems: A Comprehensive Guide to Becoming an Analyst begins with the foundational study of asset vulnerabilities, laying the groundwork for understanding the weak points within cloud environments. It then introduces tools and methodologies for the automated collection and supervision of data from assets, culminating in the exploration of advanced techniques, such as Artificial Intelligence and Machine Learning for data analysis and automated threat response. Alongside theoretical concepts, the book includes practical examples, hands-on exercises in Python, existing tools, and real-world case studies to help readers solidify their understanding and apply what they’ve learned.Cybersecurit... is one of the fastest-growing fields in today’s technological landscape, and with the increasing complexity and pervasiveness of cyber threats, especially in cloud environments, the need for qualified professionals such as Cyber Threat Analysts has never been more critical. Here, author Gianni D’Angelo provides the technical knowledge required to analyze and defend digital infrastructures, covering both traditional and modern approaches, highlighting how CTI supports decision-making and proactive defense strategies.

  • Data Compression for Data Mining Algorithms

    • 1 Edición
    • Xiaochun Wang
    • Inglés
    Data Compression for Data Mining Algorithms tackles the important problems in the design of more efficient data mining algorithms by way of data compression techniques and provides the first systematic and comprehensive description of the relationships between data compression mechanisms and the computations involved in data mining algorithms. Data mining algorithms are powerful analytical techniques used across various disciplines, including business, engineering, and science. However, in the big data era, tasks such as association rule mining and classification often require multiple scans of databases, while clustering and outlier detection methods typically depend on Euclidean distance for similarity measures, leading to high computational costs.Data Compression for Data Mining Algorithms addresses these challenges by focusing on the scalarization of data mining algorithms, leveraging data compression techniques to reduce dataset sizes and applying information theory principles to minimize computations involved in tasks such as feature selection and similarity computation. The book features the latest developments in both lossless and lossy data compression methods and provides a comprehensive exposition of data compression methods for data mining algorithm design from multiple points of view.Key discussions include Huffman coding, scalar and vector quantization, transforms, subbands, wavelet-based compression for scalable algorithms, and the role of neural networks, particularly deep learning, in feature selection and dimensionality reduction. The book’s contents are well-balanced for both theoretical analysis and real-world applications, and the chapters are well organized to compose a solid overview of the data compression techniques for data mining. To provide the reader with a more complete understanding of the material, projects and problems solved with Python are interspersed throughout the text.

  • Digital Twins for Sustainable Development

    • 1 Edición
    • Valentina Emilia Balas + 4 más
    • Inglés
    Digital Twins for Sustainable Development covers digital twins for sustainability as a virtual representation of a physical system or environment, such as a building, city, or natural ecosystem and how they are used to support sustainable development and management practices. The book demonstrates how data from a variety of sources, such as sensors, satellite imagery, and other monitoring tools can be used for advanced analytics and modeling techniques to simulate the system's behavior over time. This allows researchers and professionals in computer science to manage complex systems and promote sustainable development and resource management practices.

  • Foundations of Cloud Computing

    • 1 Edición
    • Robert Shimonski
    • Inglés
    Foundations of Cloud Computing provides readers with a guidebook to navigating the field of Cloud Computing, including the guiding principles, key concepts, history, terminology, state-of-the-art in research, and a roadmap to where the field intersects and interacts with related fields of research and development. In this age of total connectivity, researchers need to be able to communicate and collaborate with a wide range of colleagues across multiple disciplines. This book helps researchers from all fields understand what Cloud Computing is, how it works, and how to speak the language of collaboration with developers and researchers who specialize in the field. With a complete and in-depth foundation in the key concepts of the field, and a roadmap to where and how Cloud Computing intersects across the domains of scientific research and application development, this book gives readers everything they need to navigate and apply this important, ubiquitous technology.

  • Fundamentals of Statistics for Researchers and Data Analysts

    • 1 Edición
    • Shashi A. Chiplonkar + 1 más
    • Inglés
    Fundamentals of Statistics for Researchers and Data Analysts explains statistical methods, and the assumptions and prerequisites for applying various analytical tools from an statistical point of view. Statistical analysis has become indispensable in almost all fields of science, business, industry and medicine, for evidence-based decision making and forecasting. However, due to lack of fundamental understanding of statistics, results of data analysis often remain inconclusive or erroneous. In addition, data analysts or even statistical advisers may not be familiar with the subject area of the data, leading to inaccurate application of statistical tools and interpretation of results. To address these issues, this book breaks down the concepts of statistics into accessible, practical explanations with real-world examples. The book is organized by first explaining what statistical thinking is and how one should proceed with formulating their question in terms of a statistical hypothesis. Then step by step, topics are explained in detail, including data generation by choice of proper study design, data collection methods, identifying outliers, methods of data analysis, and finally interpretation of results to help make the required decision. Essential statistical methods such as classification techniques, correlation analysis, regression models, probability distributions, model building and statistical tests of significance are explained with live datasets using Excel and SPSS. Fundamentals of Statistics for Researchers and Data Analysts instructs readers on the precise methodology of analyzing data and interpretation of statistical results to arrive at a valid conclusion. Readers can use the same methodology from the case studies given in the book for their own applications and research by replacing the variables in the examples with the variables from their own datasets. The book ensures that readers are well-prepared for data-driven roles in various sectors.

  • Quantum Theory, Decision Making and Social Dynamics

    • 1 Edición
    • Tofigh Allahviranloo + 3 más
    • Inglés
    Quantum Theory, Decision Making, and Social Dynamics is a detailed exploration of the connection between quantum theory, decision-making, and social networks. As quantum theory expands into various fields, there is an increasing demand for accessible resources that clarify its principles and uses. This book aims to address that need by explaining the complex relationship between quantum theory and social dynamics, especially in decision-making contexts. It discusses the challenges of understanding and applying quantum theory in social settings and provides readers with the knowledge to leverage its potential in decision-making processes. The book is divided into eleven chapters, each focusing on a specific aspect of quantum theory and its applications. Chapter 1 introduces quantum theory, fuzzy logic, and social network analysis, highlighting key concepts like superposition, entanglement, and fuzzy influence within networks. Chapter 2 examines fuzzy sets, membership functions, and inference systems, with applications in devices, traffic management, and healthcare. Chapter 3 covers the mathematical framework of quantum mechanics and its philosophical paradoxes, connecting them to fuzzy logic models of uncertainty. Chapter 4 links social networks to quantum graphs, defining their topology, centrality, and entangled edges. Chapter 5 models social identity as a fuzzy quantum superposition, exploring identity collapse and coherence within networks. Chapter 6 relates quantum entanglement to social ties, proposing fuzzy–quantum graph models for interconnected systems. Chapter 7 analyses measures of irregularity in quantum graphs and applies these to financial networks. Chapter 8 integrates quantum cognition with fuzzy MCDM, employing various probability evaluation methods. Chapter 9 features case studies of fuzzy systems and their integration with quantum fuzzy graphs. Chapter 10 develops a quantum graph-based link prediction model for dynamic social networks. Chapter 11 concludes with a summary of the quantum–fuzzy framework, discussing its contributions, limitations, and future directions.

  • Essentials of Big Data Analytics

    Applications in R and Python
    • 1 Edición
    • Pallavi Chavan + 2 más
    • Inglés
    Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concepts with hands-on practices using the Python and R programming languages and MapReduce framework. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of big data. Essentials of Big Data Analytics is designed to provide a comprehensive resource for readers looking to deepen their understanding of Big Data analytics, particularly within a computer science, engineering, and data science context. By bridging theoretical concepts with practical applications, the book emphasizes hands-on learning through exercises and tutorials, specifically utilizing R and Python. Given the growing role of Big Data in industry and scientific research, this book serves as a timely resource to equip professionals with the skills needed to thrive in data-driven environments.

  • Multimodal Learning Using Heterogeneous Data

    • 1 Edición
    • Saeid Eslamian + 3 más
    • Inglés
    Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text, images, and audio within a unified framework. The book delves into the challenges and opportunities presented by multimodal data and offers insights into the foundations, techniques, and applications of this interdisciplinary approach. It is intended for researchers and practitioners interested in learning more about multimodal learning and is a valuable resource for those working on projects involving data analysis from multiple modalities.The book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data.Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution.

  • Mathematical Modeling for Big Data Analytics

    • 1 Edición
    • Passent El-Kafrawy + 1 más
    • Inglés
    Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of mathematical models and algorithms for analyzing large and complex datasets. The book covers a range of topics, including statistical modeling, machine learning, optimization techniques, and data visualization, and provides practical examples and case studies to demonstrate their applications in real-world scenarios. Users will find a clear and accessible resource to enhance their skills in mathematical modeling and data analysis for big data analytics. Real-world examples and case studies demonstrate how to approach and solve complex data analysis problems using mathematical modeling techniques.This book will help readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. Coverage of the theoretical foundations of big data analytics, including qualitative and quantitative analytics techniques, digital twins, machine learning, deep learning, optimization, and visualization techniques make this a must have resource.

  • Data Science for Teams

    20 Lessons from the Fieldwork
    • 1 Edición
    • Harris V. Georgiou
    • Inglés
    Managing human resources, time allocation, and risk management in R&D projects, particularly in Artificial Intelligence/Machine Learning/Data Analysis, poses unique challenges. Key areas such as model design, experimental planning, system integration, and evaluation protocols require specialized attention. In most cases, the research tends to focus primarily on one of the two main aspects: either the technical aspect of AI/ML/DA or the teams’ effort, or the typical management aspect and team members’ roles in such a project. Both are equally import for successful real-world R&D, but they are rarely examined together and tightly correlated. Data Science for Teams: 20 Lessons from the Fieldwork addresses the issue of how to deal with all these aspects within the context of real-world R&D projects, which are a distinct class of their own. The book shows the everyday effort within the team, and the adhesive substance in between that makes everything work. The core material in this book is organized over four main Parts with five Lessons each. Author Harris Georgiou goes into the difficulties progressively and dives into the challenges one step at a time, using a typical timeline profile of an R&D project as a loose template. From the formation of a team to the delivery of final results, whether it is a feasibility study or an integrated system, the content of each Lesson revisits hints, ideas and events from real-world projects in these fields, ranging from medical diagnostics and big data analytics to air traffic control and industrial process optimization. The scope of DA and ML is the underlying context for all, but most importantly the main focus is the team: how its work is organized, executed, adjusted, and optimized. Data Science for Teams presents a parallel narrative journey, with an imaginary team and project assignment as an example, running an R&D project from day one to its finish line. Every Lesson is explained and demonstrated within the team narrative, including personal hints and paradigms from real-world projects.

  • Data-Driven Insights and Analytics for Measurable Sustainable Development Goals

    • 1 Edición
    • Tilottama Goswami + 2 más
    • Inglés
    Data-Driven Insights and Analytics for Measurable Sustainable Development Goals discusses the growing imperative to understand, measure, and guide actions using data-driven insights. The SDGs encompass a broad spectrum of global challenges, from eradicating poverty and hunger to preserving the environment and fostering peace. To address these issues, one should be able to measure and analyze progress. This book bridges the gap between qualitative and quantitative assessments, recognizing that goals are not solely about numbers but also encompass complex social, environmental, and economic dynamics. By merging data science with qualitative analysis, readers can explore how SDGs intersect and influence each other.The book provides readers with an understanding of how to effectively leverage data science models and algorithms using descriptive analytics, allowing us to assess the current state of SDG performance and offering valuable insights into where we stand on these critical goals. Prescriptive analytics guides actions by offering actionable recommendations, while predictive analytics anticipates future trends and challenges, helping us navigate our path toward the SDGs effectively.

  • Advanced Topics in Inverse Data Envelopment Analysis

    Approaches for Handling Ratio Data
    • 1 Edición
    • Mehdi Soltanifar + 3 más
    • Inglés
    Advanced Topics in Inverse Data Envelopment Analysis: Approaches for Handling Ratio Data explores and tackles the most significant challenges encountered by researchers and practitioners in decision analysis and performance evaluation. This book delves into the sophisticated realm of Ratio Data Envelopment Analysis (DEA-R), offering a thorough examination of advanced methodologies, practical examples, and insights into managing complex problems involving both non-negative and negative data. Filling crucial gaps in existing literature, this comprehensive resource focuses on the emerging field of Inverse DEA-R, equipping readers with the necessary tools and knowledge to address a wide range of challenging data types. This book serves as an essential guide for making informed and efficient decisions, guiding researchers and graduate students in computer science, applied mathematics, industrial engineering, and finance, navigating the complexities of decision analysis in today's data-driven world.

  • Advanced Machine Learning for Cyber-Attack Detection in IoT Networks

    • 1 Edición
    • Dinh Thai Hoang + 3 más
    • Inglés
    Advanced Machine Learning for Cyber-Attack Detection in IoT Networks analyzes diverse machine learning techniques, including supervised, unsupervised, reinforcement, and deep learning, along with their applications in detecting and preventing cyberattacks in future IoT systems. Chapters investigate the key challenges and vulnerabilities found in IoT security, how to handle challenges in data collection and pre-processing specific to IoT environments, as well as what metrics to consider for evaluating the performance of machine learning models. Other sections look at the training, validation, and evaluation of supervised learning models and present case studies and examples that demonstrate the application of supervised learning in IoT security.

  • Applied Graph Data Science

    Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases
    • 1 Edición
    • Pethuru Raj + 4 más
    • Inglés
    Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.

  • Computational Intelligence for Genomics Data

    • 1 Edición
    • Babita Pandey + 4 más
    • Inglés
    Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers.

  • Data Science in the Medical Field

    • 1 Edición
    • Seifedine Kadry + 1 más
    • Inglés
    Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage.

  • Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications

    • 1 Edición
    • Siddhartha Bhattacharyya + 3 más
    • Inglés
    Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on recent, up-to-date technologies, combining other intelligent tools with swarm intelligence techniques to yield robust and failsafe solutions to real world problems. This book aims to provide audiences with a platform to learn and gain insights into the latest developments in hybrid swarm intelligence. It will be useful to researchers, engineers, developers, practitioners, and graduate students working in the major and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing.With the advent of data-intensive applications, the elimination of redundancy in disseminated information has become a serious challenge for researchers who are on the lookout for evolving metaheuristic algorithms which can explore and exploit the information feature space to derive the optimal settings for specific applications. Swarm intelligence algorithms have developed as one of the most widely used metaheuristic techniques for addressing this challenge in an effective way. Inspired by the behavior of a swarm of bees, these swarm intelligence techniques emulate the corresponding natural instincts to derive optimal solutions for data-intensive applications.

  • Cognitive Science, Computational Intelligence, and Data Analytics

    Methods and Applications with Python
    • 1 Edición
    • Vikas Khare + 2 más
    • Inglés
    Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented.

  • Smart Spaces

    • 1 Edición
    • Zhihan Lyu
    • Inglés
    Smart Spaces covers the latest concepts and technologies surrounding smart spaces, providing technical personnel engaged in smart space related research and industries a more in-depth understanding of smart spaces. This book can be used as a reference for practicing this emerging discipline, but it will also be useful for researchers, scientists, developers, practitioners, and graduate students working in the fields of smart spaces and artificial intelligence. It combines the study of working or living spaces with computing, information equipment, and multimodal sensing devices, and with natural and convenient interactive interfaces to support how people can easily obtain services from computer systems.People's work and life in smart spaces use computer systems; it is a process of uninterrupted interaction between people and the computer system. In this process, the computer is no longer just an information processing tool that passively executes explicit human operation commands but a collaborator with people to complete tasks – a partner to human beings. International research on smart spaces is quite extensive, which shows the important role of smart spaces in ubiquitous computing research.

  • Object-Oriented Analysis and Design for Information Systems

    Modeling with BPMN, OCL, IFML, and Python
    • 2 Edición
    • Raul Sidnei Wazlawick
    • Inglés
    Object-Oriented Analysis and Design for Information Systems, Second Edition clearly explains real object-oriented programming in practice. Expert author Raul Sidnei Wazlawick explains concepts such as object responsibility, visibility, and the real need for delegation in detail. The object-oriented code generated by using these concepts in a systematic way is concise, organized and reusable.The patterns and solutions presented in this book are based in research and industrial applications. You will come away with clarity regarding processes and use cases and a clear understanding of how to expand a use case. Wazlawick clearly explains how to build meaningful sequence diagrams. Object-Oriented Analysis and Design for Information Systems illustrates how and why building a class model is not just placing classes into a diagram. You will learn the necessary organizational patterns so that your software architecture will be maintainable. The Second Edition includes all new content shifting the focus of the book to agile software development, including Scrum software project management, BPMN diagrams, user stories, and Python code examples.

  • Synthetic Data and Generative AI

    • 1 Edición
    • Vincent Granville
    • Inglés
    Synthetic Data and Generative AI covers the foundations of machine learning with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.

  • Mathematical Methods in Data Science

    • 1 Edición
    • Jingli Ren + 1 más
    • Inglés
    Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science.

  • Data Analytics for Social Microblogging Platforms

    • 1 Edición
    • Soumi Dutta + 3 más
    • Inglés
    Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.

  • Health Systems Science Education: Development and Implementation

    • 1 Edición
    • Volumen 4
    • Rosalyn Maben-Feaster + 5 más
    • Inglés
    **Selected for 2026 Doody's Core Titles in Medical Education**Now taught in a majority of medical schools nationwide, health systems science (HSS) prepares learners for the health systems of the future—an essential topic in modern health care. Health Systems Science Education, part of the American Medical Association’s MedEd Innovation Series, is a first-of-its-kind, instructor-focused field book that that equips educators to not just teach health systems science, but to know how to integrate and implement HSS comprehensively and effectively across the curriculum. This change management-oriented volume.

  • Meeting the Challenges of Data Quality Management

    • 1 Edición
    • Laura Sebastian-Coleman
    • Inglés
    Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly. The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage. This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses.

  • Executing Data Quality Projects

    Ten Steps to Quality Data and Trusted Information (TM)
    • 2 Edición
    • Danette McGilvray
    • Inglés
    Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today’s data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization’s standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before.

  • Intelligent Data Security Solutions for e-Health Applications

    • 1 Edición
    • Amit Kumar Singh + 1 más
    • Inglés
    E-health applications such as tele-medicine, tele-radiology, tele-ophthalmology, and tele-diagnosis are very promising and have immense potential to improve global healthcare. They can improve access, equity, and quality through the connection of healthcare facilities and healthcare professionals, diminishing geographical and physical barriers. One critical issue, however, is related to the security of data transmission and access to the technologies of medical information. Currently, medical-related identity theft costs billions of dollars each year and altered medical information can put a person’s health at risk through misdiagnosis, delayed treatment or incorrect prescriptions. Yet, the use of hand-held devices for storing, accessing, and transmitting medical information is outpacing the privacy and security protections on those devices. Researchers are starting to develop some imperceptible marks to ensure the tamper-proofing, cost effective, and guaranteed originality of the medical records. However, the robustness, security and efficient image archiving and retrieval of medical data information against these cyberattacks is a challenging area for researchers in the field of e-health applications. Intelligent Data Security Solutions for e-Health Applications focuses on cutting-edge academic and industry-related research in this field, with particular emphasis on interdisciplinary approaches and novel techniques to provide security solutions for smart applications. The book provides an overview of cutting-edge security techniques and ideas to help graduate students, researchers, as well as IT professionals who want to understand the opportunities and challenges of using emerging techniques and algorithms for designing and developing more secure systems and methods for e-health applications.

  • Digital Media Steganography

    Principles, Algorithms, and Advances
    • 1 Edición
    • Mahmoud Hassaballah
    • Inglés
    The common use of the Internet and cloud services in transmission of large amounts of data over open networks and insecure channels, exposes that private and secret data to serious situations. Ensuring the information transmission over the Internet is safe and secure has become crucial, consequently information security has become one of the most important issues of human communities because of increased data transmission over social networks. Digital Media Steganography: Principles, Algorithms, and Advances covers fundamental theories and algorithms for practical design, while providing a comprehensive overview of the most advanced methodologies and modern techniques in the field of steganography. The topics covered present a collection of high-quality research works written in a simple manner by world-renowned leaders in the field dealing with specific research problems. It presents the state-of-the-art as well as the most recent trends in digital media steganography.

  • Building Big Data Applications

    • 1 Edición
    • Krish Krishnan
    • Inglés
    Building Big Data Applications helps data managers and their organizations make the most of unstructured data with an existing data warehouse. It provides readers with what they need to know to make sense of how Big Data fits into the world of Data Warehousing. Readers will learn about infrastructure options and integration and come away with a solid understanding on how to leverage various architectures for integration. The book includes a wide range of use cases that will help data managers visualize reference architectures in the context of specific industries (healthcare, big oil, transportation, software, etc.).

  • Model Management and Analytics for Large Scale Systems

    • 1 Edición
    • Bedir Tekinerdogan + 4 más
    • Inglés
    Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management.

  • Mosby's Dental Dictionary

    • 4 Edición
    • Elsevier Inc
    • Inglés
    **Selected for Doody’s Core Titles® 2024 with "Essential Purchase" designation in Dictionaries/Termino... essential dental resource that goes beyond education! Mosby's Dental Dictionary, 4th Edition is the must-have, pocket-sized reference covering all areas of dentistry that’s designed for both students and practitioners. This new edition defines over 10,000 terms on dynamic areas of dentistry, including materials, imaging, surgery, orthodontics, pain control, and more.　Throughout the text, over 300 illustrations address new innovations, research, technology, and products in the field, and extensive appendices provide quick access to the information you will use every day. Plus, a free companion website contains more than 5,000 audio pronunciations, 500 additional images, videos, and animations to help illustrate key concepts.

  • Meta-Analytics

    Consensus Approaches and System Patterns for Data Analysis
    • 1 Edición
    • Steven Simske
    • Inglés
    Meta-Analytics: Consensus Approaches and System Patterns for Data Analysis presents an exhaustive set of patterns for data science to use on any machine learning based data analysis task. The book virtually ensures that at least one pattern will lead to better overall system behavior than the use of traditional analytics approaches. The book is ‘meta’ to analytics, covering general analytics in sufficient detail for readers to engage with, and understand, hybrid or meta- approaches. The book has relevance to machine translation, robotics, biological and social sciences, medical and healthcare informatics, economics, business and finance. Inn addition, the analytics within can be applied to predictive algorithms for everyone from police departments to sports analysts.

  • Rodak's Hematology

    Clinical Principles and Applications
    • 6 Edición
    • Elaine M. Keohane + 2 más
    • Inglés
    **Selected for Doody’s Core Titles® 2024 in Laboratory Technology**Make sure you are thoroughly prepared to work in a clinical lab. Rodak’s Hematology: Clinical Principles and Applications, 6th Edition uses hundreds of full-color photomicrographs to help you understand the essentials of hematology. This new edition shows how to accurately identify cells, simplifies hemostasis and thrombosis concepts, and covers normal hematopoiesis through diseases of erythroid, myeloid, lymphoid, and megakaryocytic origins. Easy to follow and understand, this book also covers key topics including: working in a hematology lab; complementary testing areas such as flow cytometry, cytogenetics, and molecular diagnostics; the parts and functions of the cell; and laboratory testing of blood cells and body fluid cells.

  • Data Science

    Concepts and Practice
    • 2 Edición
    • Vijay Kotu + 1 más
    • Inglés
    Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more...

  • Principles and Practice of Big Data

    Preparing, Sharing, and Analyzing Complex Information
    • 2 Edición
    • Jules J. Berman
    • Inglés
    Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book can be implemented in a few lines of code using just about any popular programming language (Python snippets are provided). Through the use of new multiple examples, this edition demonstrates that if we understand our data, and if we know how to ask the right questions, we can learn a great deal from large and complex data collections. The book will assist students and professionals from all scientific backgrounds who are interested in stepping outside the traditional boundaries of their chosen academic disciplines.

  • Introduction to Data Compression

    • 5 Edición
    • Khalid Sayood
    • Inglés
    Introduction to Data Compression, Fifth Edition, builds on the success of what is widely considered the best introduction and reference text on the art and science of data compression. Data compression techniques and technology are ever-evolving with new applications in image, speech, text, audio and video. This new edition includes all the latest developments in the field. Khalid Sayood provides an extensive introduction to the theory underlying today’s compression techniques, with detailed instruction for their applications using several examples to explain the concepts. Encompassing the entire field of data compression, the book includes lossless and lossy compression, Huffman coding, arithmetic coding, dictionary techniques, context based compression, and scalar and vector quantization. The book provides a comprehensive working knowledge of data compression, giving the reader the tools to develop a complete and concise compression package.

  • Misch's Avoiding Complications in Oral Implantology

    • 1 Edición
    • Carl E. Misch + 1 más
    • Inglés
    See how to effectively manage ALL dental implant complications throughout ALL phases of treatment! Avoiding Complications in Oral Implantology provides evidence-based management protocols for a wide range of implant problems such as placement complications, malpositioning, bleeding, infection, and nerve injuries. Hundreds of high-quality, full-color photos and illustrations clearly demonstrate the complications and their resolution. Edited by Carl Misch and Randolph Resnik — both well-known names in dental implantology and prosthodontics — and with a team of expert contributors, this authoritative guide helps you handle the implant-related complications that can occur as more and more patients choose dental implants.

  • Big Data and Smart Service Systems

    • 1 Edición
    • Xiwei Liu + 4 más
    • Inglés
    Big Data and Smart Service Systems presents the theories and applications regarding Big Data and smart service systems, data acquisition, smart cities, business decision-making support, and smart service design. The rapid development of computer and Internet technologies has led the world to the era of Big Data. Big Data technologies are widely used, which has brought unprecedented impacts on traditional industries and lifestyle. More and more governments, business sectors, and institutions begin to realize data is becoming the most valuable asset and its analysis is becoming the core competitiveness.

  • Network Storage

    Tools and Technologies for Storing Your Company’s Data
    • 1 Edición
    • James O'Reilly
    • Inglés
    Network Storage: Tools and Technologies for Storing Your Company’s Data explains the changes occurring in storage, what they mean, and how to negotiate the minefields of conflicting technologies that litter the storage arena, all in an effort to help IT managers create a solid foundation for coming decades. The book begins with an overview of the current state of storage and its evolution from the network perspective, looking closely at the different protocols and connection schemes and how they differentiate in use case and operational behavior. The book explores the software changes that are motivating this evolution, ranging from data management, to in-stream processing and storage in virtual systems, and changes in the decades-old OS stack. It explores Software-Defined Storage as a way to construct storage networks, the impact of Big Data, high-performance computing, and the cloud on storage networking. As networks and data integrity are intertwined, the book looks at how data is split up and moved to the various appliances holding that dataset and its impact. Because data security is often neglected, users will find a comprehensive discussion on security issues that offers remedies that can be applied. The book concludes with a look at technologies on the horizon that will impact storage and its networks, such as NVDIMMs, The Hybrid Memory Cube, VSANs, and NAND Killers.

  • Data Hiding Techniques in Windows OS

    A Practical Approach to Investigation and Defense
    • 1 Edición
    • Nihad Ahmad Hassan + 1 más
    • Inglés
    "This unique book delves down into the capabilities of hiding and obscuring data object within the Windows Operating System. However, one of the most noticeable and credible features of this publication is, it takes the reader from the very basics and background of data hiding techniques, and run’s on the reading-road to arrive at some of the more complex methodologies employed for concealing data object from the human eye and/or the investigation. As a practitioner in the Digital Age, I can see this book siting on the shelves of Cyber Security Professionals, and those working in the world of Digital Forensics – it is a recommended read, and is in my opinion a very valuable asset to those who are interested in the landscape of unknown unknowns. This is a book which may well help to discover more about that which is not in immediate view of the onlooker, and open up the mind to expand its imagination beyond its accepted limitations of known knowns." - John Walker, CSIRT/SOC/Cyber Threat Intelligence Specialist Featured in Digital Forensics Magazine, February 2017 In the digital world, the need to protect online communications increase as the technology behind it evolves. There are many techniques currently available to encrypt and secure our communication channels. Data hiding techniques can take data confidentiality to a new level as we can hide our secret messages in ordinary, honest-looking data files. Steganography is the science of hiding data. It has several categorizations, and each type has its own techniques in hiding. Steganography has played a vital role in secret communication during wars since the dawn of history. In recent days, few computer users successfully manage to exploit their Windows® machine to conceal their private data. Businesses also have deep concerns about misusing data hiding techniques. Many employers are amazed at how easily their valuable information can get out of their company walls. In many legal cases a disgruntled employee would successfully steal company private data despite all security measures implemented using simple digital hiding techniques. Human right activists who live in countries controlled by oppressive regimes need ways to smuggle their online communications without attracting surveillance monitoring systems, continuously scan in/out internet traffic for interesting keywords and other artifacts. The same applies to journalists and whistleblowers all over the world. Computer forensic investigators, law enforcements officers, intelligence services and IT security professionals need a guide to tell them where criminals can conceal their data in Windows® OS & multimedia files and how they can discover concealed data quickly and retrieve it in a forensic way. Data Hiding Techniques in Windows OS is a response to all these concerns. Data hiding topics are usually approached in most books using an academic method, with long math equations about how each hiding technique algorithm works behind the scene, and are usually targeted at people who work in the academic arenas. This book teaches professionals and end users alike how they can hide their data and discover the hidden ones using a variety of ways under the most commonly used operating system on earth, Windows®. This is your hands-on guide to understand, detect and use today’s most popular techniques in hiding and exploring hidden data under Windows® machines, covering all Windows® versions from XP till Windows® 10. Starting with the Roman Emperor, Julius Caesar, and his simple cipher method to the surveillance programs deployed by NSA, to monitor communication and online traffic, this book will teach you everything you need to know to protect your digital data using steganographic & anonymity cryptographic techniques. Written in a simple style and requiring only basic knowledge of main Windows® functions, techniques are presented in a way to easily implement them directly on your computer.

  • Big Data

    Principles and Paradigms
    • 1 Edición
    • Rajkumar Buyya + 2 más
    • Inglés
    Big Data: Principles and Paradigms captures the state-of-the-art research on the architectural aspects, technologies, and applications of Big Data. The book identifies potential future directions and technologies that facilitate insight into numerous scientific, business, and consumer applications. To help realize Big Data’s full potential, the book addresses numerous challenges, offering the conceptual and technological solutions for tackling them. These challenges include life-cycle data management, large-scale storage, flexible processing infrastructure, data modeling, scalable machine learning, data analysis algorithms, sampling techniques, and privacy and ethical issues.

  • Data Simplification

    Taming Information With Open Source Tools
    • 1 Edición
    • Jules J. Berman
    • Inglés
    Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools. This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data. Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.

  • Mobile Data Loss

    Threats and Countermeasures
    • 1 Edición
    • Michael T. Raggo
    • Inglés
    The mobile threat landscape is evolving bringing about new forms of data loss. No longer can organizations rely on security policies designed during the PC era. Mobile is different and therefore requires a revised approach to countermeasures to mitigate data loss. Understanding these differences is fundamental to creating a new defense-in-depth strategy designed for mobile. Mobile Data Loss: Threats & Countermeasures reviews the mobile threat landscape using a hacker mind-set to outline risks and attack vectors that include malware, risky apps, operating system compromises, network attacks, and user behaviours. This provides the basis for then outlining countermeasures for defining a holistic mobile security methodology that encompasses proactive protections, response mechanisms, live monitoring, and incident response. Designing a comprehensive mobile security strategy is key. Mobile Data Loss: Threats & Countermeasures outlines the threats and strategies for protecting devices from a plethora of data loss vectors.

  • Data Mapping for Data Warehouse Design

    • 1 Edición
    • Qamar Shahbaz
    • Inglés
    Data mapping in a data warehouse is the process of creating a link between two distinct data models’ (source and target) tables/attributes. Data mapping is required at many stages of DW life-cycle to help save processor overhead; every stage has its own unique requirements and challenges. Therefore, many data warehouse professionals want to learn data mapping in order to move from an ETL (extract, transform, and load data between databases) developer to a data modeler role. Data Mapping for Data Warehouse Design provides basic and advanced knowledge about business intelligence and data warehouse concepts including real life scenarios that apply the standard techniques to projects across various domains. After reading this book, readers will understand the importance of data mapping across the data warehouse life cycle.

  • The Art and Science of Analyzing Software Data

    • 1 Edición
    • Christian Bird + 2 más
    • Inglés
    The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science. The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions.

  • Bent Functions

    Results and Applications to Cryptography
    • 1 Edición
    • Natalia Tokareva
    • Inglés
    Bent Functions: Results and Applications to Cryptography offers a unique survey of the objects of discrete mathematics known as Boolean bent functions. As these maximal, nonlinear Boolean functions and their generalizations have many theoretical and practical applications in combinatorics, coding theory, and cryptography, the text provides a detailed survey of their main results, presenting a systematic overview of their generalizations and applications, and considering open problems in classification and systematization of bent functions. The text is appropriate for novices and advanced researchers, discussing proofs of several results, including the automorphism group of bent functions, the lower bound for the number of bent functions, and more.

  • How Nursing has Changed with Technology, An Issue of Nursing Clinics

    • 1 Edición
    • Volumen 50-2
    • Maria Overstreet
    • Inglés
    How has technology changed the art and science of nursing practice?Many facets of nursing practice have stayed constant over the years such as the way we advocate for our patients and the art of caring for our patients. However, nursing practice has evolved over the years especially in the bedside delivery of state of the art nursing care. Technology at the bedside has forced nurse educators to change the ways in which we always have taught nursing students. Technology has also begun to change the methods used in the actual bedside nursing care. In this issue, you will hear from some of the nursing experts in areas of nursing care that has changed in either the delivery of care or method of assessing care of the patient. Nursing experts will describe some of the historical changes and intrigue you in the changes expected to come to the bedside. Why is this issue important? First, we gain insight from a review of where we have been and nurses tend to reminisce on our past as well as romance our historical roots. Second, technology is ever changing and it is good practice to keep abreast of what is happening in other areas of nursing so that we can apply others successes in our own specific areas of nursing. Third, informatics in nursing is a growing field and nursing must embrace technology and learn to adapt various methods of delivery so that we can appropriately care for and advocate for our patients.With the changes in our national healthcare system, we must encourage nurses to try out new methods of delivery as well as encourage their ideas of how nursing can change. The articles in this issue reflect these changes.

  • Repurposing Legacy Data

    Innovative Case Studies
    • 1 Edición
    • Jules J. Berman
    • Inglés
    Repurposing Legacy Data: Innovative Case Studies takes a look at how data scientists have re-purposed legacy data, whether their own, or legacy data that has been donated to the public domain. Most of the data stored worldwide is legacy data—data created some time in the past, for a particular purpose, and left in obsolete formats. As with keepsakes in an attic, we retain this information thinking it may have value in the future, though we have no current use for it. The case studies in this book, from such diverse fields as cosmology, quantum physics, high-energy physics, microbiology, psychiatry, medicine, and hospital administration, all serve to demonstrate how innovative people draw value from legacy data. By following the case examples, readers will learn how legacy data is restored, merged, and analyzed for purposes that were never imagined by the original data creators.