Information architecture Books

Book SynopsisTrade Review"Among the metaverse of possible books on Text as Data that could have been published . . . I was pleased that my universe produced this one. I will assign this book as a critical part of my own course on content analysis for years to come, and it has already altered and improved the coherence of my own vocabulary and articulation for several critical choices underlying the process of turning text into data. . . . Highly recommend."---James Evans, Sociological Methods & Research

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Book SynopsisThis practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures.

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Book SynopsisThe Future of Enriched, Linked, Open and Filtered Metadata is a comprehensive and accessible guide to creating accurate, consistent, complete, user-centred and quality metadata that supports the user tasks of finding, identifying, selecting, obtaining and exploring information resources. Based on the author’s many years of academic research and work as a cataloguing and metadata librarian, it shows readers how they can configure, create, enhance and enrich their metadata for print and digital resources. The book applies examples using MARC21, RDA, FRBR, BIBFRAME, subject headings and name authorities. It also uses screenshots from cutting edge library management systems, discovery interfaces and metadata tools. Coverage includes: definitions, discussions, and comparisons among MARC, FRBR, LRM, RDA, Linked Data and BIBFRAME standards and models discussion of the underlying principles and protocols of Linked Data vis-à-vis library metadata practical metadata configuration, creation, management, and cases employing cutting edge LMS, discovery interfaces, formats and tools discussion around why metadata needs to be enriched, linked, open and filtered to ensure the information resources described are discoverable and user friendly consideration of metadata as a growing and continuously enhancing, customer-focused and user-driven practice where the aim is to support users to find and retrieve relevant resources for their research and learning. This practical book uses simple and accessible language to make sense of the many existing and emerging metadata standards, models and approaches. It will be a valuable resource for anyone involved in metadata creation, management and utilisation as well as a reference for LIS students, especially those undertaking information organisation, cataloguing and metadata modules.Trade Review"This work is a tour de force...Thoroughly recommended." -- Ian McCallum * Journal of the Australian Library and Information Association *Table of Contents Introduction to metadata Metadata strategies and quality indicators Metadata use cases Contemporary metadata principles Enriched and linked metadata Open metadata Filtered Metadata FRBR, LRM and the Notion of Work Resource Description and Access (RDA) BIBFRAME: a new metadata framework Crowdsourcing and user-generated metadata

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Book SynopsisIn this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications.

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Book SynopsisTable of Contents1. Domain 1: Security risk management 2. Domain 2: Asset security 3. Domain 3: Security engineering 4. Domain 4: Communication and network security 5. Domain 5: Identity and access management (controlling access and managing identity) 6. Domain 6: Security assessment and testing 7. Domain 7: Security operations 8. Domain 8: Software development security

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Book SynopsisTable of Contents1. An Introduction to Data Architecture2. The End-State Architecture - The "World Map"3. Transformations in the End-State Architecture4. A Brief History of Big Data5. The Siloed Application Environment6. Introduction to Data Vault 2.07. The Operational Environment: A Short History8. A Brief History of Data Architecture9. Repetitive Analytics: Some Basics10. Nonrepetitive Data11. Operational Analytics: Response Time12. Operational Analytics13. Personal Analytics14. Data Models Across the End-State Architecture15. The System of Record16. Business Value and the End-State Architecture17. Managing Text18. An Introduction to Data Visualizations

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Book SynopsisMachine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles.

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Book SynopsisSolution architecture is a relatively new specialism but is at the very heart of the relationship between business and IT. This book is an authoritative and practical introduction, suitable for new entrants to the field but also of benefit to experienced professionals wishing to consolidate their knowledge and skills. The tools and techniques of solution architecture are presented in the context of a framework and life cycle, taking a problem or idea through logical steps to design a holistic and evidence-based solution. There is a focus on collaboration with the business as well as other disciplines such as enterprise architecture, business analysis and cyber security.Trade ReviewThis book is an enjoyable and refreshing read, offering readers a comprehensive and contextual introduction to the discipline of solution architecture. As organisations are reimagining traditional business models, adopting agile ways of working and accelerating digital transformation agendas, this book highlights the importance of communication and collaboration throughout a solution architecture lifecycle including the voice of the customer and ongoing stakeholder interactions. This book provides a complementary framework for solution architecture that refreshingly re-enforces that ‘…unlike a strict methodology where activities are mandated, a framework is meant to be a guide, not a driver or constraint.' -- Chris Banks MBCS CITP, Director, Workplace Fusion LtdThis book provides a good overview of Solution Architecture Process and its alignment with Enterprise Architecture, -- Sachin Bansal, Enterprise / Lead Solution Architect, IBM Services, UK * *Note - Opinions expressed here are purely personal in individual capacity and do not reflect any endorsement by IBM or any other current/former employers *I recommend this book for those who want to grow their careers in Solution Architecture and for those considering related career roles. Complementing knowledge in specific technologies, this book can enable readers to grow from techniques and ways of thinking that can be applied for varying projects, delivery environments, and stakeholders. With a highly accessible style it will appeal to people with a range of backgrounds or career experiences. -- Mike Broomhead FBCS CITPAt last, a comprehensive study of a complex and often misunderstood subject. In addition to clarifying many aspects of Solution Architecture, the author has added personal insights based on his many years of experience. I particularly liked the sections where Solution Architecture is put into context with Business Architecture and other enterprise wide strategic domains. An excellent and enlightening book for all those interested in the topic of Solution Architecture. -- Paul Turner FBCS, BCS Author and Examiner'The book provides a good, clear, readable introduction to Solution Architecture. The topics are logically presented so that there is progressive style with concepts being illustrated through use of examples drawn from a realistic case study. There are also a good number of activities that the reader can work through. Reference is made to standards and methodologies without becoming fixated on them. It was very enjoyable and easy to read.' -- Dr Quentin Vaughan, Managing Client Partner, IBM Global Business ServicesTable of Contents Introduction to Solution Architecture Solution Architecture in the Context of Business and Enterprise Architecture A Framework for Solution Architecture Inputs to Solution Architecture Gap Analysis Stakeholder Interaction Solution Technology Definition Implementation

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Book SynopsisWhy does one smoker die of lung cancer but another live to 100? The answer is 'The Hidden Half' - those random, unknowable variables that mess up our attempts to comprehend the world.We humans are very clever creatures - but we're idiots about how clever we really are. In this entertaining and ingenious book, Blastland reveals how in our quest to make the world more understandable, we lose sight of how unexplainable it often is. The result - from GDP figures to medicine - is that experts know a lot less than they think. Filled with compelling stories from economics, genetics, business, and science, The Hidden Half is a warning that an explanation which works in one arena may not work in another. Entertaining and provocative, it will change how you view the world.Trade ReviewHighly original and challenging... Once you have read this book, you can't unread it. * Daniel Finkelstein, The Times *Fascinating and provocative. Blastland is one of the most original thinkers around. * Tim Harford - Financial Times columnist and author of The Undercover Economist *Elegantly written and mind-expanding, The Hidden Half will enthrall you with its storytelling while educating you with its scientific depth. * Daniel H. Pink - bestselling author of Drive *Brilliant. Blastland provides an explanation of the need for humility in the face of the inevitable limits to knowledge and our all-too-human temptation to tell stories about the world that go far beyond what the evidence will support. * Diane Coyle - Bennett Professor of Public Policy, Cambridge University *Fascinating... As John Wooden said, it's what you learn after you know it all that counts. * Andrew Gelman - author of Rich State Poor State Red State Blue State *Excellent. Blastland makes a compelling case that God is fond of playing dice with the cosmos-and the list of unpredictable things keeps growing, not shrinking. * Phillip Tetlock - bestselling author of Superforecasting *Beautifully written and often very funny. Anyone making decision that matter should enjoy this book and profit from its lessons. * Dame Frances Cairncross - Chair, Executive Committee of the Institute for Fiscal Studies *Thought-provoking. * UnHerd *Table of Contents0: Prologue 1: Bill is not Ben 2: I am not constant 3: Here is not there, now is not then 4: One path is not enough 5: The principle isn't practical 6: Big is not small 7: Big is not clear 8: The ignorant chicken 9: What to do 10: Postscript

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Book SynopsisGain practical insights by exploiting data in your business to build advanced predictive modeling applications Key Features A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Master open source Python tools to build sophisticated predictive models Book Description Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: 1. Learning Predictive Analytics with Python 2. Mastering Predictive Analytics with Python What you will learn Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover advanced methods to analyze structured and unstructured data Visualize the performance of models and the insights they produce Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis Who this book is for This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move on from a conceptual understanding of advanced analytics and become an expert in designing and building advanced analytics solutions using Python. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about predictive analytics algorithms, this book will also help you.Table of ContentsTable of Contents Module 1 Module 2

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Book SynopsisStreamline your workflow, transform raw data into actionable insights, and use Alteryx Designer to shape, design, and visualize data Key Features Acquire the skills necessary to perform analytics operations like an expert Discover hidden trends and insights in your data from various sources to make accurate predictions Reduce the time and effort required to derive insights from your data Purchase of the print or Kindle book includes a free eBook in the PDF format Book DescriptionAlteryx allows you to create data manipulation and analytic workflows with a simple, easy-to-use, code-free UI, and perform fast-executing workflows, offering multiple ways to achieve the same results. The Alteryx Designer Cookbook is a comprehensive guide to maximizing your Alteryx skills and determining the best ways to perform data operations. This book's recipes will guide you through an analyst's complete journey, covering all aspects of the data life cycle. The first set of chapters will teach you how to read data from various sources to obtain reports and pass it through the required adjustment operations for analysis. After an explanation of the Alteryx platform components with a particular focus on Alteryx Designer, you’ll be taken on a tour of what and how you can accomplish by using this tool. Along the way, you’ll learn best practices and design patterns. The book also covers real-world examples to help you apply your understanding of the features in Alteryx to practical scenarios. By the end of this book, you’ll have enhanced your proficiency with Alteryx Designer and an improved ability to execute tasks within the tool efficiently.What you will learn Speed up the cleansing, data preparing, and shaping process Perform operations and transformations on the data to suit your needs Blend different types of data sources for analysis Pivot and un-pivot the data for easy manipulation Perform aggregations and calculations on the data Encapsulate reusable logic into macros Develop high-quality, data-driven reports to improve consistency Who this book is forThis book is for data analysts, data professionals, and business intelligence professionals seeking to harness the full potential of the tool. A basic understanding of Alteryx Designer and Alteryx terminology, including macros, apps, and workflows, is all you need to get started with this book.Table of ContentsTable of Contents Input data from files Working with databases Data Preparation Data Transformations Data Parsing Grouping Data Blending and Merging data Aggregations Dynamic Operations/ Tools Macros and Apps Downloads, APIs & Web Services Developer options Reporting with Alteryx Outputting Data

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Book SynopsisThis is a book about Microsoft 365 Excel, or Excel 365. With a new formula calculations engine and many new built-in functions, creating formula solutions and business models in Excel 365 is dramatically easier than at any time in the history of spreadsheets. In addition, with the new data tools like Power Query, Power Pivot, and Power BI, performing data analysis to make data driven decisions can be easily done on data with different structures, with different sources, and on small and big data alike. With this exciting new Excel 365 version, we will learn three types for formulas: Worksheet, M Code, and DAX, and we will learn three types of Reporting/Dashboarding tools: Standard PivotTables, Data Model PivotTables, and Power BI Visualizations. This means that the New Excel 365 is the only app that matters in our age of analytics and data driven decisions. Who is this book/class for? Everyone. The book starts at the beginning and moves to an advanced level by telling a logical story about how to use Excel to solve calculation-based problems and answer crucial questions.

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Book SynopsisThis book adopts a holistic interpretation of information architecture, to offer libraries and information professionals a variety of methods, tools, and techniques that may be used when designing websites and information systems that support workflows and what people require when “managing information”. The editors argue that information architecture for libraries has largely been the study of content architecture and that, on the other hand, library assessment literature has dealt with performance measurement and change management strategies. There is a gap in the middle for information services, with little on the ways of looking at the process architecture of a library and information service and on methods for business process analysis. Information Systems: Process and practice aims to fill that gap with a combination of theory and supporting case studies written by an international line-up of contributors, including Sally Burford, Fernando Loizides, Catherine Burns and Adam Euerby. Case studies cover a wide variety of settings, from discrete resource discovery projects for academic and cultural institutions, through design for large organizational websites, the research evidence about user experience for semi-structured document design on websites, to the health sector with examples including patient support websites and clinical document management. This book: takes a holistic view and interpretation of Information architecture in the context of libraries across the sector, globally discusses research and methods that help libraries and information services work from strategic business objectives through the organisation of processes that support the information services offered, and information management functions supported opens a new area of research/investigation on the link between information behaviour research and information systems and architecture, supported by case studies and projects includes contributions from an international range of experts from diverse backgrounds uses introductory sections and chapter commentary from the editors to draw the discussions together. This will be essential reading for researchers in information science specifically in the areas of digital libraries, information architecture and information systems. It will also be useful for practitioners and students in these areas who want to know the different research issues and challenges and learn how they have been handled in course of various research projects in these areas.Trade Review'This book is not a simple ‘how to’ guide but really a set of pointers to launch the reader towards deeper research. The large number of references included with each chapter help to facilitate this, as even though research will inevitably move on, the citations will give good starting points into the literature for some years to come. Recommended for libraries that support LIS research and independent LIS researchers that wish to broaden the scope and application of their work.' -- Jon Knight * Journal of Librarianship and Information Science *Table of ContentsSeries editor’s foreword – Gobinda Chowdhury 1. Introduction – Christine Urquhart 2. Approaches to information architecture – Faten Hamad 3. ​Taxonomy testing for information architecture – Christine Urquhart 4. The enterprise website and its information structures – Sally Burford 5. Analysing activities, roles and processes – Christine Urquhart and Dina Tbaishat 6. Libraries and organization of library processes – history of operational research, and use of process modelling – Dina Tbaishat 7. Using RIVA process modelling to study book acquisition in academic libraries – Dina Tbaishat 8. Workflow analysis and process mapping in US academic libraries – Christine Urquhart 9. A Theoretical framework for designing and evaluating semi-structured document triage interfaces – Fernando Loizides and Aekaterini Mavri 10. Resource discovery case studies – Karen Colbron and Christine Urquhart 11. Increasing social connection through a Community of Practice inspired design – Catherine M. Burns and Adam Euerby 12. Methods for studying information provision, networking and communication in patient support groups – Cristina Vasilica and Paula Ormandy 13. Health information systems: clinical data capture and document architecture – Faten Hamad 14. Producing systematic reviews and getting evidence to the clinician – Faten Hamad

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Trade Review"Most executives are familiar with big data, business intelligence (BI), analytics, business performance management, business process management, and fact-based decision-making, but they are uncertain about how to best deploy them to create business value. In this book you will find both content and features that will help you plan and execute BI strategically. Worthy of special mention is the treatment of big data and analytics. The hype around both topics is especially high, and it is easy to think that they are so new and different that they need to be treated in special ways. This book provides a clear understanding of the ways that big data and analytics both differ from the past but also the many ways that they are just a logical extension of what has come before. When viewed in this context, the strategic planning for BI and analytics in the world of big data is very similar to planning for BI in general. If you need to think about BI (and all the related topics) strategically in your company, I’m confident that you will find this book to be very helpful." --Hugh Watson, Professor and C. Herman and Mary Virginia Terry Chair of Business Administration, Terry College of Business, University of Georgia and Senior Editor, Business Intelligence JournalTable of Contents1. The Personal Face of Business Intelligence 2. Business Intelligence in the Era of Big Data and Cognitive Business 3. The Strategic Importance of Business Intelligence 4. BI Opportunity Analysis 5. Prioritizing BI Opportunities (BIOs) 6. Leveraging BI for Performance Management, Process Improvement, and Decision Support 7. Meeting the Challenges of Enterprise BI 8. General Management Perspectives on Technical Topics

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Book SynopsisText in French: Si vous êtes á la tête d'une organisation ou si vous devez communiquer avec des dirigeants au sujet de la gestion des données, Sorienter dans le labyrinthe est votre guide. Les organisations qui veulent tirer de la valeur de leurs données doivent bien les gérer. Mais pour la plupart des cadres, la gestion des données semble obscure, compliquée et très technique. Vous n'avez pas le temps d'apprendre tous les détails ou d'aller au-delá du battage médiatique. S'orienter dans le labyrinthe vous aide á y parvenir. Basé sur les meilleures pratiques du Data Management Body of Knowledge (DMBOK2) de DAMA, il explique les principes fondamentaux et explique pourquoi ils sont importants. Il attire votre attention sur ce que vous devez savoir pour aider votre organisation á faire confiance á ses données et á en tirer de la valeur.

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Book SynopsisLeverage BigQuery to understand and prepare your data to ensure that it's accurate, reliable, and ready for analysis and modeling Key Features Use mock datasets to explore data with the BigQuery web UI, bq CLI, and BigQuery API in the Cloud console Master optimization techniques for storage and query performance in BigQuery Engage with case studies on data exploration and preparation for advertising, transportation, and customer support data Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData professionals encounter a multitude of challenges such as handling large volumes of data, dealing with data silos, and the lack of appropriate tools. Datasets often arrive in different conditions and formats, demanding considerable time from analysts, engineers, and scientists to process and uncover insights. The complexity of the data life cycle often hinders teams and organizations from extracting the desired value from their data assets. Data Exploration and Preparation with BigQuery offers a holistic solution to these challenges. The book begins with the basics of BigQuery while covering the fundamentals of data exploration and preparation. It then progresses to demonstrate how to use BigQuery for these tasks and explores the array of big data tools at your disposal within the Google Cloud ecosystem. The book doesn’t merely offer theoretical insights; it’s a hands-on companion that walks you through properly structuring your tables for query efficiency and ensures adherence to data preparation best practices. You’ll also learn when to use Dataflow, BigQuery, and Dataprep for ETL and ELT workflows. The book will skillfully guide you through various case studies, demonstrating how BigQuery can be used to solve real-world data problems. By the end of this book, you’ll have mastered the use of SQL to explore and prepare datasets in BigQuery, unlocking deeper insights from data.What you will learn Assess the quality of a dataset and learn best practices for data cleansing Prepare data for analysis, visualization, and machine learning Explore approaches to data visualization in BigQuery Apply acquired knowledge to real-life scenarios and design patterns Set up and organize BigQuery resources Use SQL and other tools to navigate datasets Implement best practices to query BigQuery datasets Gain proficiency in using data preparation tools, techniques, and strategies Who this book is forThis book is for data analysts seeking to enhance their data exploration and preparation skills using BigQuery. It guides anyone using BigQuery as a data warehouse to extract business insights from large datasets. A basic understanding of SQL, reporting, data modeling, and transformations will assist with understanding the topics covered in this book.Table of ContentsTable of Contents Introducing BigQuery and Its Components BigQuery Organization and Design Exploring Data in BigQuery Loading and Transforming Data Querying BigQuery Data Exploring Data with Notebooks Further Exploring and Visualizing Data An Overview of Data Preparation Tools Cleansing and Transforming Data Best Practices for Data Preparation, Optimization, and Cost Control Hands-On Exercise – Analyzing Advertising Data Hands-On Exercise Analyzing Transportation Data Hands-On Exercise – Analyzing Customer Support Data Summary and Future Directions

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Book SynopsisEverything you need to know to apply data contracts and build a truly data-driven organization that harnesses quality data to deliver tangible business value Purchase of the print or Kindle book includes a free PDF eBook Key Features Understand data contracts and their power to resolving the problems in contemporary data platforms Learn how to design and implement a cutting-edge data platform powered by data contracts Access practical guidance from the pioneer of data contracts to get expert insights on effective utilization Book DescriptionDespite the passage of time and the evolution of technology and architecture, the challenges we face in building data platforms persist. Our data often remains unreliable, lacks trust, and fails to deliver the promised value. With Driving Data Quality with Data Contracts, you’ll discover the potential of data contracts to transform how you build your data platforms, finally overcoming these enduring problems. You’ll learn how establishing contracts as the interface allows you to explicitly assign responsibility and accountability of the data to those who know it best—the data generators—and give them the autonomy to generate and manage data as required. The book will show you how data contracts ensure that consumers get quality data with clearly defined expectations, enabling them to build on that data with confidence to deliver valuable analytics, performant ML models, and trusted data-driven products. By the end of this book, you’ll have gained a comprehensive understanding of how data contracts can revolutionize your organization’s data culture and provide a competitive advantage by unlocking the real value within your data.What you will learn Gain insights into the intricacies and shortcomings of today’s data architectures Understand exactly how data contracts can solve prevalent data challenges Drive a fundamental transformation of your data culture by implementing data contracts Discover what goes into a data contract and why it’s important Design a modern data architecture that leverages the power of data contracts Explore sample implementations to get practical knowledge of using data contracts Embrace best practices for the successful deployment of data contracts Who this book is forIf you’re a data engineer, data leader, architect, or practitioner thinking about your data architecture and looking to design one that enables your organization to get the most value from your data, this book is for you. Additionally, staff engineers, product managers, and software engineering leaders and executives will also find valuable insights.Table of ContentsTable of Contents A Brief History of Data Platforms Introducing Data Contracts How to Get Adoption in Your Organization Bringing Data Consumers and Generators Closer Together Embedding Data Governance What Makes Up a Data Contract A Contract-Driven Data Architecture A Sample Implementation Implementing Data Contracts In Your Organization Data Contracts in Practice

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Book SynopsisIn Silico Chemistry and Biology: Current and Future Prospects provides a compact overview on recent advances in this highly dynamic branch of chemistry. Various methods of protein modelling and computer-assisted drug design are presented, including fragment- and ligand-based approaches. Many successful practical applications of these techniques are demonstrated. The authors also look to the future and describe the main challenges of the field. Table of ContentsFrom the Content: - Different Methods for in silico Drug Discovery - Force Fields: Use and Applications - Protein Modelling - Molecular Docking and MD: Mimicking the Real Biological Process - Ligand-based Drug Design: Recent Advancements - Fragment-based Methods for Drug Discovery - Successful Applications of in silico Drug Design Processes

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Book SynopsisPhotonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously facilitated the realization of recurrent neural networks in analogue hardware. This concept exploits the properties of complex nonlinear dynamical systems, giving rise to photonic reservoirs implemented by semiconductor lasers, telecommunication modulators and integrated photonic chips.

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Book SynopsisThis book deals with the autoregressive method for digital processing of random oscillations. The method is based on a one-to-one transformation of the numeric factors of the Yule series model to linear elastic system characteristics. This parametric approach allowed to develop a formal processing procedure from the experimental data to obtain estimates of logarithmic decrement and natural frequency of random oscillations. A straightforward mathematical description of the procedure makes it possible to optimize a discretization of oscillation realizations providing efficient estimates. The derived analytical expressions for confidence intervals of estimates enable a priori evaluation of their accuracy. Experimental validation of the method is also provided. Statistical applications for the analysis of mechanical systems arise from the fact that the loads experienced by machineries and various structures often cannot be described by deterministic vibration theory. Therefore, a sufficient description of real oscillatory processes (vibrations) calls for the use of random functions. In engineering practice, the linear vibration theory (modeling phenomena by common linear differential equations) is generally used. This theory’s fundamental concepts such as natural frequency, oscillation decrement, resonance, etc. are credited for its wide use in different technical tasks. In technical applications two types of research tasks exist: direct and inverse. The former allows to determine stochastic characteristics of the system output X(t) resulting from a random process E(t) when the object model is considered known. The direct task enables to evaluate the effect of an operational environment on the designed object and to predict its operation under various loads. The inverse task is aimed at evaluating the object model on known processes E(t) and X(t), i.e. finding model (equations) factors. This task is usually met at the tests of prototypes to identify (or verify) its model experimentally. To characterize random processes a notion of "shaping dynamic system" is commonly used. This concept allows to consider the observing process as the output of a hypothetical system with the input being stationary Gauss-distributed ("white") noise. Therefore, the process may be exhaustively described in terms of parameters of that system. In the case of random oscillations, the "shaping system" is an elastic system described by the common differential equation of the second order: X ̈(t)+2hX ̇(t)+ ω_0^2 X(t)=E(t), where ω0 = 2π/Т0 is the natural frequency, T0 is the oscillation period, and h is a damping factor. As a result, the process X(t) can be characterized in terms of the system parameters – natural frequency and logarithmic oscillations decrement δ = hT0 as well as the process variance. Evaluation of these parameters is subjected to experimental data processing based on frequency or time-domain representations of oscillations. It must be noted that a concept of these parameters evaluation did not change much during the last century. For instance, in case of the spectral density utilization, evaluation of the decrement values is linked with bandwidth measurements at the points of half-power of the observed oscillations. For a time-domain presentation, evaluation of the decrement requires measuring covariance values delayed by a time interval divisible by T0. Both estimation procedures are derived from a continuous description of research phenomena, so the accuracy of estimates is linked directly to the adequacy of discrete representation of random oscillations. This approach is similar a concept of transforming differential equations to difference ones with derivative approximation by corresponding finite differences. The resulting discrete model, being an approximation, features a methodical error which can be decreased but never eliminated. To render such a presentation more accurate it is imperative to decrease the discretization interval and to increase realization size growing requirements for computing power. The spectral density and covariance function estimates comprise a non-parametric (non-formal) approach. In principle, any non-formal approach is a kind of art i.e. the results depend on the performer’s skills. Due to interference of subjective factors in spectral or covariance estimates of random signals, accuracy of results cannot be properly determined or justified. To avoid the abovementioned difficulties, the application of linear time-series models with well-developed procedures for parameter estimates is more advantageous. A method for the analysis of random oscillations using a parametric model corresponding discretely (no approximation error) with a linear elastic system is developed and presented in this book. As a result, a one-to-one transformation of the model’s numerical factors to logarithmic decrement and natural frequency of random oscillations is established. It allowed to develop a formal processing procedure from experimental data to obtain the estimates of δ and ω0. The proposed approach allows researchers to replace traditional subjective techniques by a formal processing procedure providing efficient estimates with analytically defined statistical uncertainties.

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Book SynopsisThis open access book answers two central questions: firstly, is it at all possible to verify electronic equipment procured from untrusted vendors? Secondly, can I build trust into my products in such a way that I support verification by untrusting customers? In separate chapters the book takes readers through the state of the art in fields of computer science that can shed light on these questions. In a concluding chapter it discusses realistic ways forward. In discussions on cyber security, there is a tacit assumption that the manufacturer of equipment will collaborate with the user of the equipment to stop third-party wrongdoers. The Snowden files and recent deliberations on the use of Chinese equipment in the critical infrastructures of western countries have changed this. The discourse in both cases revolves around what malevolent manufacturers can do to harm their own customers, and the importance of the matter is on par with questions of national security.This book is of great interest to ICT and security professionals who need a clear understanding of the two questions posed in the subtitle, and to decision-makers in industry, national bodies and nation states. Table of Contents1 Introduction: 1.1 A New Situation.- 1.2 What are we Afraid of?.- 1.3 Huawei and ZTE.- 1.4 Trust in Vendors.- 1.5 Points of Attack.- 1.6 Trust in Vendors is Different from Computer Security.- 1.7 Why the Problem is Important.- 1.8 Advice for Readers.- 2 Trust: 2.1 Prisoner's Dilemma.- 2.2 Trust and Game Theory.- 2.3 Trust and Freedom of Choice.- 2.4 Trust, Consequence and Situation.- 2.5 Trust and Security.- 2.6 Trusted Computing Base---Trust between Components.- 2.7 Discussion.- 3 What is an ICT-System?: 3.1 Transistors and Integrated Circuits.- 3.2 Memory and Communication.- 3.3 Processors and Instruction Sets.- 3.4 Firmware.- 3.5 Operating Systems, Device Drivers, Hardware Adaptation Layers and Hypervisors.- 3.6 Bytecode Interpreters.- 3.7 The Application on Top.- 3.8 Infrastructures and Distributed Systems.- 3.9 Discussion.- 4 Development of ICT Systems: 4.1 Software Development.- 4.2 Hardware Development .- 4.3 Security Updates and Maintenance.- 4.4 Discussion.- 5 Theoretical Foundation: 5.1 Gödel and the Liar's Paradox.- 5.2 Turing and the Halting Problem.- 5.3 Decidability of Malicious Behaviour.- 5.4 Is there Still Hope?.- 5.5 Where does this Lead Us?.- 6 Reverse Engineering of Code: 6.1 Application of Reversing in ICT.- 6.2 Static Code Analysis.- 6.3 Disassemblers.- 6.4 Decompilers.- 6.5 Debuggers.- 6.6 Antireversing.- 6.7 Hardware.- 6.8 Discussion.- 7 Static Detection of Malware: 7.1 Classes of Malware.- 7.2 Signatures, and Static Code Analysis.- 7.3 Encrypted and Oligomorphic Malware.- 7.4 Obfuscation Techniques.- 7.5 Polymorphic and Metamorphic Malware.- 7.6 Heuristic Approaches.- 7.7 Malicious Hardware.- 7.8 Specification Based Techniques.- 7.9 Discussion.- 8 Dynamic Detection Methods: 8.1 Dynamic Properties.- 8.2 Unrestricted Execution.- 8.3 Emulator Based Analysis.- 8.4 Virtual Machines.- 8.5 Evasion Techniques.- 8.6 Analysis.- 8.7 Hardware.- 8.8 Discussion.- 9 Formal Methods: 9.1 Formal Methods Overview.- 9.2 Specification.- 9.3 Programming Languages.- 9.4 Hybrid Programming and Specification Languages.- 9.5 Semantic Translation.- 9.6 Logics.- 9.7 Theorem Proving and Model Checking.- 9.8 Proofcarrying Code.- 9.9 Conclusion.- 10 Software Quality and Quality Management: 10.1 What is Software Quality Management?.- 10.2 Software Development Process.- 10.3 Software Quality Models.- 10.4 Software Quality Management.- 10.5 Software Quality Metrics.- 10.6 Standards.- 10.7 Common Criteria (ISO/IEC-15408).- 10.8 Software Testing.- 10.9 Verification through Formal Methods.- 10.10 Code Review.- 10.11 Discussion.- 11 Containment of Untrusted Modules: 11.1 Overview.- 11.2 Partial Failures and Fault Models.- 11.3 Erlang---a Programming Language Supporting Containment.- 11.4 Microservices---an Architecture Model Supporting Containment.- 11.5 Hardware Containment.- 11.6 Discussion.- 12 Summary and Way Forward: 12.1 Summary of Findings.- 12.2 Way Forward.- 12.3 Concluding Remarks.

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