This book is Volume II of simple but powerful tools for performance improvement. It is written for managers, analysts, and consultants who realize the value that system dynamic modeling can bring to companies and organizations, and would like to have that capability without a degree in math or computer science. It features the iThink modeling program, which requires no extensive knowledge of math; instead, iThink uses a small set of symbols and rules to allow any keen observer of a system to create models graphically—the user literally draws a graphic of the system within the program and works from that. In Chapter 1, the author describes his own experiences with modeling, the growth and development of modeling software, and makes the case for its value. Chapter 2 is an overview of iThink symbols and rules, sufficient to enable the reader to interpret and understand iThink models; while the program has many advanced features, a great many models are based on the fundamentals in this chapter. Chapter 3 provides guidelines for converting workflow-mapping models into iThink dynamic models, and discusses approaches to building models from scratch. This approach to modeling is consistent with the author’s approach to workflow mapping and analysis, which uses a small symbol set and related discipline to map workflows in any company or organization, without the need for expensive software or extended training. That process is described in this volume of the series, and these maps are often the foundation for modeling the system as a dynamic entity.

Provides a practical and comprehensive introduction to the key aspects of model-based testing as taught in the ISTQB® Model-Based Tester—Foundation Level Certification Syllabus This book covers the essentials of Model-Based Testing (MBT) needed to pass the ISTQB® Foundation Level Model-Based Tester Certification. The text begins with an introduction to MBT, covering both the benefits and the limitations of MBT. The authors review the various approaches to model-based testing, explaining the fundamental processes in MBT, the different modeling languages used, common good modeling practices, and the typical mistakes and pitfalls. The book explains the specifics of MBT test implementation, the dependencies on modeling and test generation activities, and the steps required to automate the generated test cases. The text discusses the introduction of MBT in a company, presenting metrics to measure success and good practices to apply. Provides case studies illustrating different approaches to Model-Based Testing Includes in-text exercises to encourage readers to practice modeling and test generation activities Contains appendices with solutions to the in-text exercises, a short quiz to test readers, along with additional information Model-Based Testing Essentials – Guide to the ISTQB® Certified Model-Based Tester – Foundation Level is written primarily for participants of the ISTQB® Certification: software engineers, test engineers, software developers, and anybody else involved in software quality assurance. This book can also be used for anyone who wants a deeper understanding of software testing and of the use of models for test generation.

Everything you need to make the most of building information modeling If you're looking to get involved in the world of BIM, but don't quite know where to start, Building Information Modeling For Dummies is your one-stop guide to collaborative building using one coherent system of computer models rather than as separate sets of drawings. Inside, you'll find an easy-to-follow introduction to BIM and hands-on guidance for understanding drivers for change, the benefits of BIM, requirements you need to get started, and where BIM is headed. The future of BIM is bright—it provides the industry with an increased understanding of predictability, improved efficiency, integration and coordination, less waste, and better value and quality. Additionally, the use of BIM goes beyond the planning and design phase of the project, extending throughout the building life cycle and supporting processes, including cost management, construction management, project management, and facility operation. Now heavily adopted in the U.S., Hong Kong, India, Singapore, France, Canada, and countless other countries, BIM is set to become a mandatory practice in building work in the UK, and this friendly guide gives you everything you need to make sense of it—fast. Demonstrates how BIM saves time and waste on site Shows you how the information generated from BIM leads to fewer errors on site Explains how BIM is based on data sets that describe objects virtually, mimicking the way they'll be handled physically in the real world Helps you grasp how the integration of BIM allows every stage of the life cycle to work together without data or process conflict Written by a team of well-known experts, this friendly, hands-on guide gets you up and running with BIM fast.

This book invites the reader on a journey of discovery of service systems. From a Service-Dominant-Logic perspective, such systems are the building blocks of all economic activity, and innovation of new service systems holds the promise of a new industrial revolution. Users navigating web sites, customers interacting with intelligent mobile retail applications, patients interpreting advice from health-care professionals and other sources, students interacting with teachers and learning materials, city dwellers invoking smart service applications for transportation routing, and the unlimited variations of smart service systems that will be enabled by the Internet of Things and other technologies provide ample evidence of the need for service innovation. This book presents an overview of the foundational constructs of service science and models of co-creative systems, with the aim of enabling the reader to be a service innovator. The value proposition of this book is the opportunity to fill each reader's knowledge gaps and offer a comprehensive, coherent, and introductory overview of service system modeling.

Leaders and Managers want quick answers, quick ways to reach solutions, ways and means to access knowledge that won’t eat into their precious time and quick ideas that deliver a big result. The Little Book of Big Decision Models cuts through all the noise and gives managers access to the very best decision-making models that they need to to keep things moving forward. Every model is quick and easy to read and delivers the essential information and know-how quickly, efficiently and memorably.

Key Management Development Models provides the crucial information you need to develop your skills as a manager. Divided into two parts (Part 1: Developing Yourself & Part 2: Working with Others), each tool, model or idea will ensure you: · understand yourself better · understand how others perceive you · develop your credibility at work · make better choices in your management of others · become a more rounded professional, able to adapt your style to get the best out of yourself and others

This book explores a wide range of topics in exposure-response modeling, from traditional PKPD modeling to other areas in drug development and beyond. It incorporates numerous examples and software programs for implementing novel methods. The book emphasizes dose adjustment and treatment adaptation based on dynamic exposure-response models, illustrates how to apply causal inference to exposure-response modeling in pharmacometrics and epidemiology, and links exposure-response modeling to clinical decision making through model-based decision analysis.

Design Databases with Oracle SQL Developer Data Modeler In this practical guide, Oracle ACE Director Heli Helskyaho explains the process of database design using Oracle SQL Developer Data Modeler—the powerful, free tool that flawlessly supports Oracle and other database environments, including Microsoft SQL Server and IBM DB2. Oracle SQL Developer Data Modeler for Database Design Mastery covers requirement analysis, conceptual, logical, and physical design, data warehousing, reporting, and more. Create and deploy high-performance enterprise databases on any platform using the expert tips and best practices in this Oracle Press book. Configure Oracle SQL Developer Data Modeler Perform requirement analysis Translate requirements into a formal conceptual data model and process models Transform the conceptual (logical) model into a relational model Manage physical database design Generate data definition language (DDL) scripts to create database objects Design a data warehouse database Use subversion for version control and to enable a multiuser environment Document an existing database Use the reporting tools in Oracle SQL Developer Data Modeler Compare designs and the database

This innovative book recognizes the need within the object-oriented community for a book that goes beyond the tools and techniques of the typical methodology book. In Analysis Patterns: Reusable Object Models, Martin Fowler focuses on the end result of object-oriented analysis and design—the models themselves. He shares with you his wealth of object modeling experience and his keen eye for identifying repeating problems and transforming them into reusable models. Analysis Patterns provides a catalogue of patterns that have emerged in a wide range of domains including trading, measurement, accounting and organizational relationships. Recognizing that conceptual patterns cannot exist in isolation, the author also presents a series of "support patterns" that discuss how to turn conceptual models into software that in turn fits into an architecture for a large information system. Included in each pattern is the reasoning behind their design, rules for when they should and should not be used, and tips for implementation. The examples presented in this book comprise a cookbook of useful models and insight into the skill of reuse that will improve analysis, modeling and implementation.

Computational modeling is an important tool for understanding and improving food processing and manufacturing. It is used for many different purposes, including process design and process optimization. However, modeling goes beyond the process and can include applications to understand and optimize food storage and the food supply chain, and to perform a life cycle analysis. Modeling Food Processing Operations provides a comprehensive overview of the various applications of modeling in conventional food processing. The needs of industry, current practices, and state-of-the-art technologies are examined, and case studies are provided. Part One provides an introduction to the topic, with a particular focus on modeling and simulation strategies in food processing operations. Part Two reviews the modeling of various food processes involving heating and cooling. These processes include: thermal inactivation; sterilization and pasteurization; drying; baking; frying; and chilled and frozen food processing, storage and display. Part Three examines the modeling of multiphase unit operations such as membrane separation, extrusion processes and food digestion, and reviews models used to optimize food distribution. Comprehensively reviews the various applications of modeling in conventional food processing Examines the modeling of multiphase unit operations and various food processes involving heating and cooling Analyzes the models used to optimize food distribution

Modeling and Analysis of Compositional Data presents a practical and comprehensive introduction to the analysis of compositional data along with numerous examples to illustrate both theory and application of each method. Based upon short courses delivered by the authors, it provides a complete and current compendium of fundamental to advanced methodologies along with exercises at the end of each chapter to improve understanding, as well as data and a solutions manual which is available on an accompanying website. Complementing Pawlowsky-Glahn's earlier collective text that provides an overview of the state-of-the-art in this field, Modeling and Analysis of Compositional Data fills a gap in the literature for a much-needed manual for teaching, self learning or consulting.

A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linear statistical models. The book presents a broad, in-depth overview of the most commonly used statistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations of Linear and Generalized Linear Models also features:

An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises

An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

This best selling management book is a true classic. If you want to be a model manager, keep this new, even better 3rd edition close at hand. Key Management Models has the winning combination of brevity and clarity, giving you short, practical overviews of the top classic and cutting edge management models in an easy-to-use, ready reference format. Whether you want to remind yourself about models you’ve already come across, or want to find new ones, you’ll find yourself referring back to it again and again. It's the essential guide to all the management models you’ll ever need to know about. Includes the classic and essential management models from the previous editions. Thoroughly updated to include cutting edge new models. Two-colour illustrations and case studies throughout. The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you will receive via email the code and instructions on how to access this product. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.

Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant. Starting with a background section of practical lessons and warnings for beginner data scientists for software engineering, this edited volume proceeds to identify critical questions of contemporary software engineering related to data and models. Learn how to adapt data from other organizations to local problems, mine privatized data, prune spurious information, simplify complex results, how to update models for new platforms, and more. Chapters share largely applicable experimental results discussed with the blend of practitioner focused domain expertise, with commentary that highlights the methods that are most useful, and applicable to the widest range of projects. Each chapter is written by a prominent expert and offers a state-of-the-art solution to an identified problem facing data scientists in software engineering. Throughout, the editors share best practices collected from their experience training software engineering students and practitioners to master data science, and highlight the methods that are most useful, and applicable to the widest range of projects. Shares the specific experience of leading researchers and techniques developed to handle data problems in the realm of software engineering Explains how to start a project of data science for software engineering as well as how to identify and avoid likely pitfalls Provides a wide range of useful qualitative and quantitative principles ranging from very simple to cutting edge research Addresses current challenges with software engineering data such as lack of local data, access issues due to data privacy, increasing data quality via cleaning of spurious chunks in data

Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling: Theory, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. The book gets you up to speed on the latest sparsity-related developments and will motivate you to continue learning about the field. The authors first present motivating examples and a high-level survey of key recent developments in sparse modeling. The book then describes optimization problems involving commonly used sparsity-enforcing tools, presents essential theoretical results, and discusses several state-of-the-art algorithms for finding sparse solutions. The authors go on to address a variety of sparse recovery problems that extend the basic formulation to more sophisticated forms of structured sparsity and to different loss functions. They also examine a particular class of sparse graphical models and cover dictionary learning and sparse matrix factorizations.

The ability to create and understand financial models that assess the valuation of a company, the projects it undertakes, and its future earnings/profit projections is one of the most valued skills in corporate finance. However, while many business professionals are familiar with financial statements and accounting reports, few are truly proficient at building an accurate and effective financial model from the ground up. In his short book, The Basics of Financial Modeling—an abridgement of his Handbook of Financial Modeling—Jack Avon equips financial professionals with a quick overview of the tools they need to monitor a company's assets and project its future performance and prospects for specific initiatives. Based on the author's extensive experience building models in business and finance—and teaching others to do the same— The Basics of Financial Modeling takes readers step by step in a quick-read format through the financial modeling process, starting with a general overview of the history and evolution of financial modeling. It then moves on to more technical topics, such as the principles of financial modeling and the proper way to approach a financial modeling assignment, before covering key application areas for modeling in Microsoft Excel. Designed for beginning to intermediate modelers who wish to expand and enhance their knowledge of using Excel to build financial models, The Basics of Financial Modeling covers: The accounting and finance concepts that underpin working financial models How to approach financial issues and solutions from a modeler's perspective The importance of thinking about end users when developing a financial model How to plan, design, and build a financial model A nuts-to-bolts guide to solving common financial problems with spreadsheets, The Basics of Financial Modeling is a one-stop resource for anyone who needs to build or analyze financial models.

The golden standard evaluation reference text Now in its second edition, Evaluation Theory, Models, and Applications is the vital text on evaluation models, perfect for classroom use as a textbook, and as a professional evaluation reference. The book begins with an overview of the evaluation field and program evaluation standards, and proceeds to cover the most widely used evaluation approaches. With new evaluation designs and the inclusion of the latest literature from the field, this Second Edition is an essential update for professionals and students who want to stay current. Understanding and choosing evaluation approaches is critical to many professions, and Evaluation Theory, Models, and Applications, Second Edition is the benchmark evaluation guide. Authors Daniel L. Stufflebeam and Chris L. S. Coryn, widely considered experts in the evaluation field, introduce and describe 23 program evaluation approaches, including, new to this edition, transformative evaluation, participatory evaluation, consumer feedback, and meta-analysis. Evaluation Theory, Models, and Applications, Second Edition facilitates the process of planning, conducting, and assessing program evaluations. The highlighted evaluation approaches include: Experimental and quasi-experimental design evaluations Daniel L. Stufflebeam's CIPP Model Michael Scriven's Consumer-Oriented Evaluation Michael Patton's Utilization-Focused Evaluation Robert Stake's Responsive/Stakeholder-Centered Evaluation Case Study Evaluation Key readings listed at the end of each chapter direct readers to the most important references for each topic. Learning objectives, review questions, student exercises, and instructor support materials complete the collection of tools. Choosing from evaluation approaches can be an overwhelming process, but Evaluation Theory, Models, and Applications, Second Edition updates the core evaluation concepts with the latest research, making this complex field accessible in just one book.

This work is on biometric data indexing for large-scale identification systems with a focus on different biometrics data indexing methods. It provides state-of-the-art coverage including different biometric traits, together with the pros and cons for each. Discussion of different multimodal fusion strategies are also included.

Many empirical researchers yearn for an econometric

model that better explains their data. Yet these researchers

rarely pursue this objective for fear of the

statistical complexities involved in specifying that

model. This book is intended to alleviate those anxieties

by providing a practical methodology that anyone

familiar with regression analysis can employ—a

methodology that will yield a model that is both more

informative and is a better representation of the data.

This book outlines simple, practical procedures

that can be used to specify a model that better explains

the data. Such procedures employ the use of

purely statistical techniques performed upon a publicly

available data set, which allows readers to follow

along at every stage of the procedure. Using the

econometric software Stata (though most other statistical

software packages can be used as well), this

book demonstrates how to test for model misspecification

and how to respecify these models in a practical

way that not only enhances the inference drawn

from the results, but adds a level of robustness that

can increase the researcher’s confidence in the output

generated. By following this procedure, researchers

will be led to a better, more finely tuned empirical

model that yields better results.

Process Modeling Style focuses on other aspects of process modeling beyond notation that are very important to practitioners. Many people who model processes focus on the specific notation used to create their drawings. While that is important, there are many other aspects to modeling, such as naming, creating identifiers, descriptions, interfaces, patterns, and creating useful process documentation. Experience author John Long focuses on those non-notational aspects of modeling, which practitioners will find invaluable. Gives solid advice for creating roles, work products, and processes Instucts on how to organize and structure the parts of a process Gives examples of documents you should use to define a set of processes