Data warehousing is one of the hottest business topics, and there's more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with "Data Warehousing For Dummies, 2nd Edition."

Data is probably your company's most important asset, so your data warehouse should serve "your" needs. The fully updated Second Edition of "Data Warehousing For Dummies" helps you understand, develop, implement, and use data warehouses, and offers a sneak peek into their future. You'll learn to: Analyze top-down and bottom-up data warehouse designsUnderstand the structure and technologies of data warehouses, operational data stores, and data martsChoose your project team and apply best development practices to your data warehousing projectsImplement a data warehouse, step by step, and involve end-users in the processReview and upgrade existing data storage to make it serve your needsComprehend OLAP, column-wise databases, hardware assisted databases, and middlewareUse data mining intelligently and find what you needMake informed choices about consultants and data warehousing products

"Data Warehousing For Dummies, 2nd Edition" also shows you how to involve users in the testing process and gain valuable feedback, what it takes to successfully manage a data warehouse project, and how to tell if your project is on track. You'll find it's the most useful source of data on the topic

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

One of the key methods of reducing and dealing with criminal activity is to accurately gauge and then analyse the geographical distribution of crime (from small scale to large scale areas). Once the police and government know what areas suffer most from criminal activity they can assess why this is the case and then deal with it in the most effective way. Crime mapping and the spatial analysis of crime data have become recognised as powerful tools for the study and control of crime. Much of the emerging demand for more information and detailed crime pattern analysis have been driven by legislative changes, such as the UK's new Crime and Disorder Act which has placed a joint statutory duty on Police Forces and Local Authorities to produce crime and disorder audits for their areas. The book sets out methods used in the fields of Geographical Information Systems and highlights areas of best practice, examines the types of problems to which spatial crime analysis can be applied, reviews the capabilities and limitations of existing techniques, and explores the future directions of spatial crime analysis and the need for training. It centres on a series of case studies highlighting the experiences of academics and practitioners in agencies centrally involved in the partnership approach to crime prevention. Practitioners and academics not only in the UK but also worldwide should be interested in the book as an up-to-date information resource and a practical guide.

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

The goal of this book is to provide a comprehensive and systematic introduction to the important and highly applicable method of data refinement and the simulation methods used for proving its correctness. The authors concentrate in the first part on the general principles needed to prove data refinement correct. They begin with an explanation of the fundamental notions, showing that data refinement proofs reduce to proving simulation. The topics of Hoare Logic and the Refinement Calculus are introduced and a general theory of simulations is developed and related to them. Accessibility and comprehension are emphasized in order to guide newcomers to the area. The book's second part contains a detailed survey of important methods in this field, such as VDM, and the methods due to Abadi & Lamport, Hehner, Lynch and Reynolds, Back's refinement calculus and Z. All these methods are carefully analysed, and shown to be either imcomplete, with counterexamples to their application, or to be always applicable whenever data refinement holds. This is shown by proving, for the first time, that all these methods can be described and analyzed in terms of two simple notions: forward and backward simulation. The book is self-contained, going from advanced undergraduate level and taking the reader to the state of the art in methods for proving simulation.

Ambient Diagnostics addresses innovative methods for discovering patterns from affordable devices, such as mobile phones, watches, cameras, and game interfaces, to interpret multimedia data for personal health monitoring and diagnosis. This is the first comprehensive textbook on multidisciplinary innovations in affordable healthcare-from sensory fusion, pattern detection, to classification. Connecting the Dots The material in this book combines sensing, pattern recognition, and visual design, and is divided into four parts, which cover fundamentals, multimedia intelligence, pervasive sensors, and crowdsourcing. The author describes basic pattern discovery models, sound, color, motion and video analytics, and pattern discovery from games and social networks. Each chapter contains the material's main concepts, as well as case studies, and extensive study questions. Contains overviews about diagnostic sensors on mobile phones Reflects the rapidly growing platforms for remote sensing, gaming, and social networking Incorporates cognitive tests such as fatigue detection Includes pseudo code and sample code Provides vision algorithms and multimedia analytics Covers Multimedia Intelligence Extensively Ambient Diagnostics includes concepts for ambient technologies such as point-and-search, the pill camera, active sensing with Kinect, digital human labs, negative and relative feature spaces, and semantic representations. The book also introduces methods for collective intelligence from online video games and social media.

Dealing with the volume, complexity, and diversity of data currently being generated by scientific experiments and simulations often causes scientists to waste productive time. Scientific Data Management: Challenges, Technology, and Deployment describes cutting-edge technologies and solutions for managing and analyzing vast amounts of data, helping scientists focus on their scientific goals. The book begins with coverage of efficient storage systems, discussing how to write and read large volumes of data without slowing the simulation, analysis, or visualization processes. It then focuses on the efficient data movement and management of storage spaces and explores emerging database systems for scientific data. The book also addresses how to best organize data for analysis purposes, how to effectively conduct searches over large datasets, how to successfully automate multistep scientific process workflows, and how to automatically collect metadata and lineage information. This book provides a comprehensive understanding of the latest techniques for managing data during scientific exploration processes, from data generation to data analysis. Enhanced by numerous detailed color images, it includes real-world examples of applications drawn from biology, ecology, geology, climatology, and more. Check out Dr. Shoshani discuss the book during an interview with International Science Grid This Week (iSGTW): http://www.isgtw.org/?pid=1002259

Big Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power. Further, capabilities of big data systems also vary with respect to type of problems. For instance, distributed memory systems are not recommended for iterative algorithms. Similarly, variations in big data systems also exist related to consistency and fault tolerance. The purpose of this book is to provide a detailed explanation of big data systems. The book covers various topics including Networking, Security, Privacy, Storage, Computation, Cloud Computing, NoSQL and NewSQL systems, High Performance Computing, and Deep Learning. An illustrative and practical approach has been adopted in which theoretical topics have been aided by well-explained programming and illustrative examples. Key Features: Introduces concepts and evolution of Big Data technology. Illustrates examples for thorough understanding. Contains programming examples for hands on development. Explains a variety of topics including NoSQL Systems, NewSQL systems, Security, Privacy, Networking, Cloud, High Performance Computing, and Deep Learning. Exemplifies widely used big data technologies such as Hadoop and Spark. Includes discussion on case studies and open issues. Provides end of chapter questions for enhanced learning.

Provides a one-stop resource for engineers learning biostatistics using MATLAB and WinBUGS Through its scope and depth of coverage, this book addresses the needs of the vibrant and rapidly growing bio-oriented engineering fields while implementing software packages that are familiar to engineers. The book is heavily oriented to computation and hands-on approaches so readers understand each step of the programming. Another dimension of this book is in parallel coverage of both Bayesian and frequentist approaches to statistical inference. It avoids taking sides on the classical vs. Bayesian approach, and many examples in this book are solved using both methods. The results are then compared and commented upon. Readers have the choice of MATLAB for classical data analysis and WinBUGS/OpenBUGS for Bayesian data analysis. Every chapter starts with a box highlighting what is covered in that chapter and ends with exercises, a list of software scripts, datasets, and references. Engineering Biostatistics: An Introduction using MATLAB and WinBUGS also includes: * parallel coverage of classical and Bayesian approaches, where appropriate * substantial coverage of Bayesian approaches to statistical inference * material classroom tested in an introductory statistics course in bioengineering over several years * exercises at the end of each chapter and an accompanying website with full solutions to even-numbered exercises, hints for odd-numbered exercises, and additional materials and examples Engineering Biostatistics: An Introduction using MATLAB and WinBUGS can serve as a textbook for introductory-to-intermediate applied statistics courses, as well as a useful reference for engineers interested in biostatistical approaches.

Knowledge representation is a key area of modern AI, underlying the development of semantic networks. Description logics are languages that represent knowledge in a structured and formally well-understood way: they are the cornerstone of the Semantic Web. This is the first textbook describing this importan new topic and will be suitable for courses aimed at advanced undergraduate and beginning graduate students, or for self-study. It assumes only a basic knowledge of computer science concepts. After generla introducitons motivating and overviewing the subject, the authors describe a simple DL and how it works and can be used, utilizing a running example that recurs through the book. Methods of reasoning and their implementation and complexity are examined, Finally, the authors provide a non-trivial DL knowledge base and use it to illsutrate featues that have been introduced: this base is available for free online access in a form usable by modern ontology editors.

This book focuses on the different aspects of handling big data in healthcare. It showcases the current state-of-the-art technology used for storing health records and health data models. It also focuses on the research challenges in big data acquisition, storage, management and analysis.

With our appetites for data on the rise, it has become more important than ever to use UML (Unified Modelling Language) to capture and precisely represent all of these data requirements. Learn how to construct UML data models by working through a series of exercises and self-assessment tests. Beginners can learn the UML directly. Experienced modellers can leverage their understanding of existing database notations, as the book extensively compares the UML to traditional data modelling (Information Engineering). Discover a new way of representing data requirements and communicating better with your business customers; Understand what UML constructs mean and how to properly use them; Learn subtleties of the UML. Become a power UML developer; Practice constructing data models with the exercises. The back of the book answers every exercise; Assess your mastery of the material. Each part has a multiple-choice test that can quantify your understanding; Improve your ability to abstract -- think about different ways of representation -- as you construct data models; Measure the quality of your data models; Be able to create database designs (DDL code) starting from a UML data model; Be able to write SQL database queries using a data model as a blueprint; Know the differences among operational models, data warehouse models, enterprise models, and master models. They are all aspects of data modelling. This book is concise and to the point. You will learn by induction through reading, practice, and feedback.

Due to the complexity of operational forestry problems, computing applications are becoming pervasive in all aspects of forest and natural resource management. This book provides a comprehensive introduction to computers and their applications in forest and natural resource management and is designed for both undergraduate and graduate students in forestry and natural resources. It introduces state-of-the-art applications for several of the most important computer technologies in terms of data acquisition, data manipulation, basic programming techniques, and other related computer and Internet concepts and applications. This book consists of six parts and 19 chapters.

Demystify architecting complex blockchain applications in enterprise environments Architecting Enterprise Blockchain Solutions helps engineers and IT administrators understand how to architect complex blockchain applications in enterprise environments. The book takes a deep dive into the intricacies of supporting and securing blockchain technology, creating and implementing decentralized applications, and incorporating blockchain into an existing enterprise IT infrastructure. Blockchain is a technology that is experiencing massive growth in many facets of business and the enterprise. Most books around blockchain primarily deal with how blockchains are related to cryptocurrency or focus on pure blockchain development. This book teaches what blockchain technology is and offers insights into its current and future uses in high performance networks and complex ecosystems. Provides a practical, hands-on approach Demonstrates the power and flexibility of enterprise blockchains such as Hyperledger and R3 Corda Explores how blockchain can be used to solve complex IT support and infrastructure problems Offers numerous hands-on examples and diagrams Get ready to learn how to harness the power and flexibility of enterprise blockchains!

Statistical Data Mining Using SAS Applications, Second Edition describes statistical data mining concepts and demonstrates the features of user-friendly data mining SAS tools. Integrating the statistical and graphical analysis tools available in SAS systems, the book provides complete statistical data mining solutions without writing SAS program codes or using the point-and-click approach. Each chapter emphasizes step-by-step instructions for using SAS macros and interpreting the results. Compiled data mining SAS macro files are available for download on the author's website. By following the step-by-step instructions and downloading the SAS macros, analysts can perform complete data mining analysis fast and effectively. New to the Second Edition-General Features Access to SAS macros directly from desktop Compatible with SAS version 9, SAS Enterprise Guide, and SAS Learning Edition Reorganization of all help files to an appendix Ability to create publication quality graphics Macro-call error check New Features in These SAS-Specific Macro Applications Converting PC data files to SAS data (EXLSAS2 macro) Randomly splitting data (RANSPLIT2) Frequency analysis (FREQ2) Univariate analysis (UNIVAR2) PCA and factor analysis (FACTOR2) Multiple linear regressions (REGDIAG2) Logistic regression (LOGIST2) CHAID analysis (CHAID2) Requiring no experience with SAS programming, this resource supplies instructions and tools for quickly performing exploratory statistical methods, regression analysis, logistic regression multivariate methods, and classification analysis. It presents an accessible, SAS macro-oriented approach while offering comprehensive data mining solutions.

Computing and database management has shifted from cottage industry-style methods - the small independent researcher keeping records for a particular project - to state-of-the-art file storage systems, presentation, and distribution over the Internet. New and emerging techniques for recognition, compilation, and data management have made managing data a discipline in its own right. Covering all aspects of this data management, Biodiversity Databases: Techniques, Politics, and Applications brings together input from social scientists, programmers, database designers, and information specialists to delineate the political setting and give institutions platforms for the dissemination of taxonomic information. A practical and logical guide to complex issues, the book explores the changes and challenges of the information age. It discusses projects developed to provide better access to all available biodiversity information. The chapters make the case for the need for representation of concepts in taxonomic databases. They explore issues involved in connecting databases with different user interfaces, the technical demands of linking databases that are not entirely uniform in structure, and the problems of user access and the control of data quality. The book highlights different approaches to addressing concerns associated with the taxonomic impediment and the low reproducibility of taxonomic data. It provides an in-depth examination of the challenge of making taxonomic information more widely available to users in the wider scientific community, in government, and the general population.

Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education. With a focus on computational tools, the book shows readers how to improve their awareness of what tasks can be achieved and describes the correct approach to perform these tasks. Practical examples demonstrate the most important pointsThe author first discusses how to write computer code using HTML as a concrete example. He then covers a variety of data storage topics, including different file formats, XML, and the structure and design issues of relational databases. After illustrating how to extract data from a relational database using SQL, the book presents tools and techniques for searching, sorting, tabulating, and manipulating data. It also introduces some very basic programming concepts as well as the R language for statistical computing. Each of these topics has supporting chapters that offer reference material on HTML, CSS, XML, DTD, SQL, R, and regular expressions. One-stop shop of introductory computing information Written by a member of the R Development Core Team, this resource shows readers how to apply data technologies to tasks within a research setting. Collecting material otherwise scattered across many books and the web, it explores how to publish information via the web, how to access information stored in different formats, and how to write small programs to automate simple, repetitive tasks.

This book provides readers with a practical guide to the principles of hybrid approaches to natural language processing (NLP) involving a combination of neural methods and knowledge graphs. To this end, it first introduces the main building blocks and then describes how they can be integrated to support the effective implementation of real-world NLP applications. To illustrate the ideas described, the book also includes a comprehensive set of experiments and exercises involving different algorithms over a selection of domains and corpora in various NLP tasks. Throughout, the authors show how to leverage complementary representations stemming from the analysis of unstructured text corpora as well as the entities and relations described explicitly in a knowledge graph, how to integrate such representations, and how to use the resulting features to effectively solve NLP tasks in a range of domains. In addition, the book offers access to executable code with examples, exercises and real-world applications in key domains, like disinformation analysis and machine reading comprehension of scientific literature. All the examples and exercises proposed in the book are available as executable Jupyter notebooks in a GitHub repository. They are all ready to be run on Google Colaboratory or, if preferred, in a local environment. A valuable resource for anyone interested in the interplay between neural and knowledge-based approaches to NLP, this book is a useful guide for readers with a background in structured knowledge representations as well as those whose main approach to AI is fundamentally based on logic. Further, it will appeal to those whose main background is in the areas of machine and deep learning who are looking for ways to leverage structured knowledge bases to optimize results along the NLP downstream.

This book provides a broad introduction to search engines by integrating five different perspectives on Web search and search engines that are usually dealt with separately: the technical perspective, the user perspective, the internet-based research perspective, the economic perspective, and the societal perspective. After a general introduction to the topic, two foundational chapters present how search tools can cover the Web's content and how search engines achieve this by crawling and processing the found documents. The next chapter on user behavior covers how people phrase their search queries and interact with search engines. This knowledge builds the foundation for describing how results are ranked and presented. The following three chapters then deal with the economic side of search engines, i.e., Google and the search engine market, search engine optimization (SEO), and the intermingling of organic and sponsored search results. Next, the chapter on search skills presents techniques for improving searches through advanced search interfaces and commands. Following that, the Deep Web and how its content can be accessed is explained. The two subsequent chapters cover ways to improve the quality of search results, while the next chapter describes how to access the Deep Web. Last but not least, the following chapter deals with the societal role of search engines before the final chapter concludes the book with an outlook on the future of Web search. With this book, students and professionals in disciplines like computer science, online marketing, or library and information science will learn how search engines work, what their main shortcomings are at present, and what prospects there are for their further development. The different views presented will help them to understand not only the basic technologies but also the implications the current implementations have concerning economic exploitation and societal impact.

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book. About the Author Dr. Jesus Rogel-Salazar is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.

Technological developments in recent years have been tremendous. This evolution is visible in companies through technological equipment, computerized procedures, and management practices associated with technologies. One of the management practices that is visible is related to business intelligence and analytics (BI&A). Concepts such as data warehousing, key performance indicators (KPIs), data mining, and dashboards are changing the business arena. This book aims to promote research related to these new trends that open up a new field of research in the small and medium enterprises (SMEs) area. Features Focuses on the more recent research findings occurring in the fields of BI&A Conveys how companies in the developed world are facing today's technological challenges Shares knowledge and insights on an international scale Provides different options and strategies to manage competitive organizations Addresses several dimensions of BI&A in favor of SMEs

Blockchain technology is defined as a decentralized system of distributed registers that are used to record data transactions on multiple computers. The reason this technology has gained popularity is that you can put any digital asset or transaction in the blocking chain, the industry does not matter. Blockchain technology has infiltrated all areas of our lives, from manufacturing to healthcare and beyond. Cybersecurity is an industry that has been significantly affected by this technology and may be more so in the future. Blockchain for Cybersecurity and Privacy: Architectures, Challenges, and Applications is an invaluable resource to discover the blockchain applications for cybersecurity and privacy. The purpose of this book is to improve the awareness of readers about blockchain technology applications for cybersecurity and privacy. This book focuses on the fundamentals, architectures, and challenges of adopting blockchain for cybersecurity. Readers will discover different applications of blockchain for cybersecurity in IoT and healthcare. The book also includes some case studies of the blockchain for e-commerce online payment, retention payment system, and digital forensics. The book offers comprehensive coverage of the most essential topics, including: Blockchain architectures and challenges Blockchain threats and vulnerabilities Blockchain security and potential future use cases Blockchain for securing Internet of Things Blockchain for cybersecurity in healthcare Blockchain in facilitating payment system security and privacy This book comprises a number of state-of-the-art contributions from both scientists and practitioners working in the fields of blockchain technology and cybersecurity. It aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this particular area or those interested in grasping its diverse facets and exploring the latest advances on the blockchain for cybersecurity and privacy.

This book provides a concise yet comprehensive overview of computer and Internet security, suitable for a one-term introductory course for junior/senior undergrad or first-year graduate students. It is also suitable for self-study by anyone seeking a solid footing in security - including software developers and computing professionals, technical managers and government staff. An overriding focus is on brevity, without sacrificing breadth of core topics or technical detail within them. The aim is to enable a broad understanding in roughly 350 pages. Further prioritization is supported by designating as optional selected content within this. Fundamental academic concepts are reinforced by specifics and examples, and related to applied problems and real-world incidents. The first chapter provides a gentle overview and 20 design principles for security. The ten chapters that follow provide a framework for understanding computer and Internet security. They regularly refer back to the principles, with supporting examples. These principles are the conceptual counterparts of security-related error patterns that have been recurring in software and system designs for over 50 years. The book is "elementary" in that it assumes no background in security, but unlike "soft" high-level texts it does not avoid low-level details, instead it selectively dives into fine points for exemplary topics to concretely illustrate concepts and principles. The book is rigorous in the sense of being technically sound, but avoids both mathematical proofs and lengthy source-code examples that typically make books inaccessible to general audiences. Knowledge of elementary operating system and networking concepts is helpful, but review sections summarize the essential background. For graduate students, inline exercises and supplemental references provided in per-chapter endnotes provide a bridge to further topics and a springboard to the research literature; for those in industry and government, pointers are provided to helpful surveys and relevant standards, e.g., documents from the Internet Engineering Task Force (IETF), and the U.S. National Institute of Standards and Technology.

"Data Science Foundations is most welcome and, indeed, a piece of literature that the field is very much in need of...quite different from most data analytics texts which largely ignore foundational concepts and simply present a cookbook of methods...a very useful text and I would certainly use it in my teaching." - Mark Girolami, Warwick University Data Science encompasses the traditional disciplines of mathematics, statistics, data analysis, machine learning, and pattern recognition. This book is designed to provide a new framework for Data Science, based on a solid foundation in mathematics and computational science. It is written in an accessible style, for readers who are engaged with the subject but not necessarily experts in all aspects. It includes a wide range of case studies from diverse fields, and seeks to inspire and motivate the reader with respect to data, associated information, and derived knowledge.

This book addresses the challenging topic of modeling adaptive networks, which often manifest inherently complex behavior. Networks by themselves can usually be modeled using a neat, declarative, and conceptually transparent Network-Oriented Modeling approach. In contrast, adaptive networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections in Social Networks change due to various social dynamics. For adaptive networks, separate procedural specifications are often added for the adaptation process. Accordingly, modelers have to deal with a less transparent, hybrid specification, part of which is often more at a programming level than at a modeling level. This book presents an overall Network-Oriented Modeling approach that makes designing adaptive network models much easier, because the adaptation process, too, is modeled in a neat, declarative, and conceptually transparent Network-Oriented Modeling manner, like the network itself. Thanks to this approach, no procedural, algorithmic, or programming skills are needed to design complex adaptive network models. A dedicated software environment is available to run these adaptive network models from their high-level specifications. Moreover, because adaptive networks are described in a network format as well, the approach can simply be applied iteratively, so that higher-order adaptive networks in which network adaptation itself is adaptive (second-order adaptation), too can be modeled just as easily. For example, this can be applied to model metaplasticity in cognitive neuroscience, or second-order adaptation in biological and social contexts. The book illustrates the usefulness of this approach via numerous examples of complex (higher-order) adaptive network models for a wide variety of biological, mental, and social processes. The book is suitable for multidisciplinary Master's and Ph.D. students without assuming much prior knowledge, although also some elementary mathematical analysis is involved. Given the detailed information provided, it can be used as an introduction to Network-Oriented Modeling for adaptive networks. The material is ideally suited for teaching undergraduate and graduate students with multidisciplinary backgrounds or interests. Lecturers will find additional material such as slides, assignments, and software.