This book discusses questions of numerical solutions of applied problems on parallel computing systems. Nowadays, engineering and scientific computations are carried out on parallel computing systems, which provide parallel data processing on a few computing nodes. In the development of up-to-date applied software, this feature of computers must be taken into account for the maximum efficient usage of their resources. In constructing computational algorithms, we should separate relatively independent subproblems in order to solve them on a single computing node.

Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and Computation Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book's collection of projects, comprehensive sample solutions, and follow-up exercises encompass practical topics pertaining to data processing, including: Non-standard, complex data formats, such as robot logs and email messages Text processing and regular expressions Newer technologies, such as Web scraping, Web services, Keyhole Markup Language (KML), and Google Earth Statistical methods, such as classification trees, k-nearest neighbors, and naive Bayes Visualization and exploratory data analysis Relational databases and Structured Query Language (SQL) Simulation Algorithm implementation Large data and efficiency Suitable for self-study or as supplementary reading in a statistical computing course, the book enables instructors to incorporate interesting problems into their courses so that students gain valuable experience and data science skills. Students learn how to acquire and work with unstructured or semistructured data as well as how to narrow down and carefully frame the questions of interest about the data. Blending computational details with statistical and data analysis concepts, this book provides readers with an understanding of how professional data scientists think about daily computational tasks. It will improve readers' computational reasoning of real-world data analyses.

Microservices Security in Action teaches readers how to secure their microservices applications code and infrastructure. After a straightforward introduction to the challenges of microservices security, the book covers fundamentals to secure both the application perimeter and service-to-service communication. Following a hands-on example, readers explore how to deploy and secure microservices behind an API gateway as well as how to access microservices accessed by a single-page application (SPA). Key Features Key microservices security fundamentals Securing service-to-service communication with mTLS and JWT Deploying and securing microservices with Docker Using Kubernetes security Securing event-driven microservices Using the Istio Service Mesh For developers well-versed in microservices design principles who have a basic familiarity with Java. About the technology As microservices continue to change enterprise application systems, developers and architects must learn to integrate security into their design and implementation. Because microservices are created as a system of independent components, each a possible point of failure, they can multiply the security risk. Prabath Siriwardena is the vice president of security architecture at WSO2, a company that produces open source software, and has more than 12 years of experience in the identity management and security domain. Nuwan Dias is the director of API architecture at WSO2 and has worked in the software industry for more than 7 years, most of which he spent focusing on the API management domain. Both have helped build security designs for Fortune 500 companies including Boeing, Verizon, Nissan, HP, and GE.

Studies of evolution at the molecular level have experienced phenomenal growth in the last few decades, due to rapid accumulation of genetic sequence data, improved computer hardware and software, and the development of sophisticated analytical methods. The flood of genomic data has generated an acute need for powerful statistical methods and efficient computational algorithms to enable their effective analysis and interpretation. Molecular Evolution: a statistical approach presents and explains modern statistical methods and computational algorithms for the comparative analysis of genetic sequence data in the fields of molecular evolution, molecular phylogenetics, statistical phylogeography, and comparative genomics. Written by an expert in the field, the book emphasizes conceptual understanding rather than mathematical proofs. The text is enlivened with numerous examples of real data analysis and numerical calculations to illustrate the theory, in addition to the working problems at the end of each chapter. The coverage of maximum likelihood and Bayesian methods are in particular up-to-date, comprehensive, and authoritative. This advanced textbook is aimed at graduate level students and professional researchers (both empiricists and theoreticians) in the fields of bioinformatics and computational biology, statistical genomics, evolutionary biology, molecular systematics, and population genetics. It will also be of relevance and use to a wider audience of applied statisticians, mathematicians, and computer scientists working in computational biology.

An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent TasksThe first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications. New to the Second EditionThis edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples. Enables Easy Mobility between the Two SystemsThrough the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book's website.

Instead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers. This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. It not only shows how to use R's powerful graphic tools to construct the types of plots most useful in scientific and engineering work, but also: Explains how to statistically analyze and fit data to linear and nonlinear models Explores numerical differentiation, integration, and optimization Describes how to find eigenvalues and eigenfunctions Discusses interpolation and curve fitting Considers the analysis of time series Using R for Numerical Analysis in Science and Engineering provides a solid introduction to the most useful numerical methods for scientific and engineering data analysis using R.

A guide to Microsoft Project that focuses on developing a successful project management strategy across the organization to drive better decisions

"Making Effective Business Decisions Using Microsoft Project" goes far beyond the basics of managing projects with Microsoft Project and how to set up and use the software. This unique guide is an indispensable resource for anyone who operates within a Project Management Operation (PMO) or is affected by the adoption of project management within an organization. Its focus is to provide practical and transitional information for those who are charged with making decisions and supporting corporate and strategic objectives, and who face cost and resource constraints.

Because more and more companies are aligning project management with their business strategies, the book not only provides guidance on using Microsoft Project and teaching project management skills, but also includes important information on measuring results and communicating with the executive branch. It also provides valuable guidance in using SharePoint Server for social networking and working within a team.

Clearly written and presented, the book: Covers work management using Microsoft Project at multiple levels within an organizationFocuses on using Microsoft Project 2010 to integrate and support overall organizational strategiesIncludes hundreds of graphics, screen shots, and annotations that make it the most accessible and usable guide available on the subject

"Making Effective Business Decisions Using Microsoft Projec"t is a valuable reference for project managers at all levels, and it sets a new standard for training manuals used by businesses that teach courses on project management using Microsoft Project.

Looking to get SAP S/4HANA Sales up and running? This book has all the expert guidance you need! Start with the organizational structure and master data, including customer-vendor integration. Then follow click-by-click instructions to configure your key SD processes: pricing, sales order management, ATP and supply protection, shipping, billing, and more. Including SAP Fiori reports and KPIs, this is your all-in-one sales resource!In this book, you'll learn about:a. Master Data See how to configure new business partners in your system, and perform customer-vendor integration (CVI) to manage customer data. Then learn about the material master data that's integral to your sales processes. b. ConfigurationWalk through step-by-step instructions to configure your sales and distribution processes in SAP S/4HANA, from managing sales orders and condition contracts to shipping, delivery, billing, and invoicing. c. ReportingGet the most out of your sales data! Explore operational reports and due lists with SAP Fiori and SAP GUI. Analyze the KPIs that mean the most to your business. Highlights include: 1) Organizational structure 2) Business partners 3) Customer-vendor integration 4) Material master 5) Pricing 6) Sales contract and agreement management 7) Sales order management 8) Available-to-promise (ATP) 9) Supply protection10) Shipping and delivery 11) Billing and invoicing12) Reporting

Das Buch gibt eine Einfuhrung in die Geschaftsprozessanalyse mit den beiden Schwerpunkten "Ereignisgesteuerte Prozessketten" (EPK) und "objektorientierte Geschaftsprozessanalyse." Es thematisiert Grundlagen, Chancen und Risiken Betriebswirtschaftlicher Standardsoftware (ERP-Software) und gibt eine umfassende Einfuhrung in ereignisgesteuerte Prozessketten. Die Darstellung wird durch zahlreiche Beispiele aus der Praxis abgerundet. Anschliessend wird das Gesamtkonzept der Unternehmensmodellierung fur SAP R/3 beschrieben und aufgezeigt, welche zentrale Rolle ereignisgesteuerte Prozessketten dabei spielen. Das Buch stellt Objektorientierung vor und uberpruft die objektorientierte Geschaftsprozessmodellierung auf ihre Tauglichkeit fur eine effiziente Prozessanalyse. In der Neuauflage wurde der Schwerpunkt auf die Modellierungsmethoden verlagert. Das Kapitel zur objektorientierten Geschaftsprozessmodellierung wurde stark ausgebaut und aktualisiert (UML 2.0) und das EPK-Kapitel um weitere Beispiele erweitert."

Ziel der Autoren ist es, Mechanismen und Prinzipien des Social Webs im Tourismus aufzuzeigen. Neben den theoretischen Grundlagen erl utern sie die praktischen Anwendungen und illustrieren das Thema anhand vieler Best-Practice-Beispiele. Leser erfahren, welche Bedeutung das Social Web und seine grundlegenden Mechanismen haben, so dass Internet-Aktivit ten besser geplant und an neue Entwicklungen angepasst werden k nnen. F r Akteure in der Tourismusbranche, aber auch Wissenschaftler, die einen Einstieg in die touristische Praxis des Social Web suchen.

Mathematical Models of Biological Systems provides a practical introduction to basic mathematical modelling methodology and analysis. It covers a variety of biological applications and uses these topics in turn to highlight key components in the art of modelling. Its primary aim is to give students the tools to translate simple, real-world biological problems into rigorous mathematical models. A secondary aim is to teach the reader how to critically assess the modelling components in the primary life science literature.
The book covers deterministic as well as stochastic dynamics, continuous-time as well as discrete-time dynamics, partial differential equations, dimensional analysis, and curve fitting/parameter estimation. It contains numerous case studies, graded from elementary examples to more complicated problems, as well as a general treatment of good modelling practice. Although the book assumes a basic background in mathematics, specifically beginning calculus and elementary statistics, all requisite material is included in a series of appendices.

Mathematical Models of Biological Systems provides a practical introduction to basic mathematical modelling methodology and analysis. It covers a variety of biological applications and uses these topics in turn to highlight key components in the art of modelling. Its primary aim is to give students the tools to translate simple, real-world biological problems into rigorous mathematical models. A secondary aim is to teach the reader how to critically assess the modelling components in the primary life science literature. The book covers deterministic as well as stochastic dynamics, continuous-time as well as discrete-time dynamics, partial differential equations, dimensional analysis, and curve fitting/parameter estimation. It contains numerous case studies, graded from elementary examples to more complicated problems, as well as a general treatment of good modelling practice. Although the book assumes a basic background in mathematics, specifically beginning calculus and elementary statistics, all requisite material is included in a series of appendices.

Manufacturing Execution Systeme (MES) sind das Werkzeug, mit dem die Fertigungsprozesse transparent gemacht werden und mit dem die Ablaufe in Realtime unter Berucksichtigung von Zielvorgaben geregelt werden konnen.

Das Buch soll helfen, ein MES zielorientiert im Unternehmen einzufuhren. Hierzu werden nicht nur Ratschlage zur Konzeption gegeben, sondern es wird auch bei der "internen Vermarktung" des MES-Vorhabens in Form von Ratschlagen und Wirtschaftlichkeitsbetrachtungen unterstutzt. Im Anschluss daran werden Hinweise zur Erstellung eines Pflichtenhefts sowie zur Ausschreibung und Anbieterauswahl gegeben. Neben Tipps vom Projektstart bis zum Produktivstart des Systems werden Themen wie Mitarbeiterqualifizierung und Support angesprochen. Ferner wird aufgezeigt, wie der Einfuhrungsprozess durch externe MES-Berater unterstutzt werden kann.

Zwei Fallbeispiele zeigen, wie die Einfuhrung in der Praxis verlief und welcher Nutzen durch das MES erzielt werden konnte.

Zur Besserung Nutzung des Systems werden noch organisatorische Massnahmen beschrieben, wie die Mitarbeitereinbindung mit Zielvereinbarungen und Pramienentlohnung, die auch neue Tarifmodelle, wie z.B. ERA (Entgeltrahmenabkommen) vorsehen.

Ein Kapitel mit Checklisten, Literaturtipps und Weblinks schliesst dieses Buch ab."

This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.

Examine the latest developments in online business with cutting-edge coverage, real examples, actual business cases, and hands-on applications found in the market-leading ELECTRONIC COMMERCE, 12E. With comprehensive coverage of emerging strategies and today's most important technologies, this popular book equips you with a solid understanding of the dynamics of this fast-paced industry. The new edition offers thorough discussions of e-commerce growth in the rapidly-developing economies of China, India, and Brazil. You also examine key topics, such as social media and online marketing strategies, technology-enabled outsourcing, and online payment processing systems. New intriguing "Learning From Failure" segments help you draw important lessons from the experiences of actual companies as you review real-world e-commerce practices in action.

Discover what needs to happen in enterprise-architecture practice-and not just its outcomes, but also the activities from which those outcomes would arise. This book reveals how business and enterprise architects can deliver fast solutions to an always-on-the-go business world. To begin, you'll review a new technique called "context-space mapping," which provides a structured method for sense-making across the entire context of an enterprise. Throughout the book, you'll concentrate on the routine practices that underpin each of the architecture disciplines. Working step-by-step through a real 10-day architecture project, this book explores the activities that underpin the strategy, structures and solutions in the real-time turmoil of an enterprise architect's everyday work. You'll explore how and why and when the various documents, artefacts and items of 'theory-stuff' come into the practice - all those mainstream methods, frameworks, models, metamodels and other information sources. In the end, Everyday Enterprise Architecture will help you develop the skills, judgment, and awareness to keep enhancing the value of your architectural projects. What You'll Learn Work on architectures at "business-speed" Adapt architectures for different tasks Gather, use, and manage architectural information Who This Book Is For Enterprise and business architects.

DESCRIPTION R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data. R in Action, Second Edition is language tutorial focused on practical problems. Written by a research methodologist, it takes a direct and modular approach to quickly give readers the information they need to produce useful results. Focusing on realistic data analyses and a comprehensive integration of graphics, it follows the steps that real data analysts use to acquire their data, get it into shape, analyze it, and produce meaningful results that they can provide to clients. RETAIL SELLING POINTS Step-by-step guide to practical data analysis Gives quick access to the R language Written by seasoned researcher Uses over 130 graphics AUDIENCE This book is for social scientists, medical and pharmaceutical researchers, professors and students in quantitative fields, data analysts, data mining specialists, and statistical programmers who have a sense of what they want to do, but not how to do it in R. ABOUT THE TECHNOLOGY R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and

This proceedings volume features top contributions in modern statistical methods from Statistics 2021 Canada, the 6th Annual Canadian Conference in Applied Statistics, held virtually on July 15-18, 2021. Papers are contributed from established and emerging scholars, covering cutting-edge and contemporary innovative techniques in statistics and data science. Major areas of contribution include Bayesian statistics; computational statistics; data science; semi-parametric regression; and stochastic methods in biology, crop science, ecology and engineering. It will be a valuable edited collection for graduate students, researchers, and practitioners in a wide array of applied statistical and data science methods.

Take control of your personal finances with this popular, easy guide

Quicken is the country's most popular and highly recommended personal finance software, and with "Quicken For Dummies," you can harness the power of this practical financial tool to manage bills, reconcile bank accounts, track investments, and much more

Personal finance expert and CPA Stephen L. Nelson lends his expertise to this accessible, easy-to-read guide, which has been fully updated to reflect the new features of Quicken available in its newest version. You can quickly learn how to use Quicken to record credit card transactions, track mortgages and other liabilities, minimize taxes, maximize savings, maintain detailed records, and build a solid foundation for creating better financial plans.Covers updated features in the newest version of the increasingly popular Quicken softwareEmpowers readers to fight the bad habits and decisions that lead to large debts and minimal or nonexistent savingsDemystifies the software's features for tracking everyday finances, managing investments, and evaluating the tax implications of financial decisionsOffers invaluable advice and expertise from author Stephen L. Nelson, a recognized authority on personal finance

Every step toward better finances is a step in the right direction. Let "Quicken For Dummies" start your journey toward increased savings, lowered debt, and a more secure financial future.

The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. The most desirable property of consistency of an estimator and its large sample distribution, with suitable normalization, are discussed, the focus being on the consistent and asymptotically normal (CAN) estimators. It is shown that for the probability models belonging to an exponential family and a Cramer family, the maximum likelihood estimators of the indexing parameters are CAN. The book describes some large sample test procedures, in particular, the most frequently used likelihood ratio test procedure. Various applications of the likelihood ratio test procedure are addressed, when the underlying probability model is a multinomial distribution. These include tests for the goodness of fit and tests for contingency tables. The book also discusses a score test and Wald's test, their relationship with the likelihood ratio test and Karl Pearson's chi-square test. An important finding is that, while testing any hypothesis about the parameters of a multinomial distribution, a score test statistic and Karl Pearson's chi-square test statistic are identical. Numerous illustrative examples of differing difficulty level are incorporated to clarify the concepts. For better assimilation of the notions, various exercises are included in each chapter. Solutions to almost all the exercises are given in the last chapter, to motivate students towards solving these exercises and to enable digestion of the underlying concepts. The concepts from asymptotic inference are crucial in modern statistics, but are difficult to grasp in view of their abstract nature. To overcome this difficulty, keeping up with the recent trend of using R software for statistical computations, the book uses it extensively, for illustrating the concepts, verifying the properties of estimators and carrying out various test procedures. The last section of the chapters presents R codes to reveal and visually demonstrate the hidden aspects of different concepts and procedures. Augmenting the theory with R software is a novel and a unique feature of the book. The book is designed primarily to serve as a text book for a one semester introductory course in asymptotic statistical inference, in a post-graduate program, such as Statistics, Bio-statistics or Econometrics. It will also provide sufficient background information for studying inference in stochastic processes. The book will cater to the need of a concise but clear and student-friendly book introducing, conceptually and computationally, basics of asymptotic inference.

This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience.

The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics.

This introductory textbook presents research methods and data analysis tools in non-technical language. It explains the research process and the basics of qualitative and quantitative data analysis, including procedures and methods, analysis, interpretation, and applications using hands-on data examples in QDA Miner Lite and IBM SPSS Statistics software. The book is divided into four parts that address study and research design; data collection, qualitative methods and surveys; statistical methods, including hypothesis testing, regression, cluster and factor analysis; and reporting. The intended audience is business and social science students learning scientific research methods, however, given its business context, the book will be equally useful for decision-makers in businesses and organizations.

This book presents a general method for deriving higher-order statistics of multivariate distributions with simple algorithms that allow for actual calculations. Multivariate nonlinear statistical models require the study of higher-order moments and cumulants. The main tool used for the definitions is the tensor derivative, leading to several useful expressions concerning Hermite polynomials, moments, cumulants, skewness, and kurtosis. A general test of multivariate skewness and kurtosis is obtained from this treatment. Exercises are provided for each chapter to help the readers understand the methods. Lastly, the book includes a comprehensive list of references, equipping readers to explore further on their own.

Das Buch behandelt praxisbezogene Methoden zur Analyse und Steuerung operationaler IT-Risiken entsprechend der Anforderungen im Gesetz zur Kontrolle und Transparenz im Unternehmensbereich (KonTraG). Prasentiert werden zudem Konzepte fur den Aufbau und den Ablauf einer Notfall- und Krisenorganisation in Unternehmen sowie Verfahren zur Pravention und Durchfuhrung von Ubungen im Not- oder Katastrophenfall. Zu den Praxisbeispielen werden Checklisten zur Verfugung gestellt, die als Referenz bei der Erstellung eigener Dokumente dienen."