A friendly illustrated guide to designing and implementing your first database. Grokking Relational Database Design makes the principles of designing relational databases approachable and engaging. Everything in this book is reinforced by hands-on exercises and examples. In Grokking Relational Database Design, you’ll learn how to: Query and create databases using Structured Query Language (SQL) Design databases from scratch Implement and optimize database designs Take advantage of generative AI when designing databases A well-constructed database is easy to understand, query, manage, and scale when your app needs to grow. In Grokking Relational Database Design you’ll learn the basics of relational database design including how to name fields and tables, which data to store where, how to eliminate repetition, good practices for data collection and hygiene, and much more. You won’t need a computer science degree or in-depth knowledge of programming—the book’s practical examples and down-to-earth definitions are beginner-friendly. About the Technology Almost every business uses a relational database system. Whether you’re a software developer, an analyst creating reports and dashboards, or a business user just trying to pull the latest numbers, it pays to understand how a relational database operates. This friendly, easy-to-follow book guides you from square one through the basics of relational database design. About the Book Grokking Relational Database Design introduces the core skills you need to assemble and query tables using SQL. The clear explanations, intuitive illustrations, and hands-on projects make database theory come to life, even if you can’t tell a primary key from an inner join. As you go, you’ll design, implement, and optimize a database for an e-commerce application and explore how generative AI simplifies the mundane tasks of database designs. What's Inside Define entities and their relationships Minimize anomalies and redundancy Use SQL to implement your designs Security, scalability, and performance About the Reader For self-taught programmers, software engineers, data scientists, and business data users. No previous experience with relational databases assumed. About the Authors Dr. Qiang Hao and Dr. Michail Tsikerdekis are both professors of Computer Science at Western Washington University. Quotes If anyone is looking to improve their database design skills, they can’t go wrong with this book. - Ben Brumm, DatabaseStar Goes beyond SQL syntax and explores the core principles. An invaluable resource! - William Jamir Silva, Adjust Relational database design is best done right the first time. This book is a great help to achieve that! - Maxim Volgin, KLM Provides necessary notions to design and build databases that can stand the data challenges we face. - Orlando Méndez, Experian

Dive into the power of Apache Spark as a tool for handling and processing big data required for machine learning. With this book, you will explore how to configure, execute, and deploy machine learning algorithms using Spark's scalable architecture and learn best practices for implementing real-world big data solutions. What this Book will help me do Understand the integration of Apache Spark with large-scale infrastructures for machine learning applications. Employ data processing techniques for preprocessing and feature engineering efficiently with Spark. Master the implementation of advanced supervised and unsupervised learning algorithms using Spark. Learn to deploy machine learning models within Spark ecosystems for optimized performance. Discover methods for analyzing big data trends and machine learning model tuning for improved accuracy. Author(s) The author, Deepak Gowda, is an experienced data scientist with over ten years of expertise in machine learning and big data. His career spans industries such as supply chain, cybersecurity, and more where he has utilized Apache Spark extensively. Deepak's teaching style is marked by clarity and practicality, making complex concepts approachable. Who is it for? Apache Spark for Machine Learning is tailored for data engineers, machine learning practitioners, and computer science students looking to advance their ability to process, analyze, and model using large datasets. If you're already familiar with basic machine learning and want to scale your solutions using Spark, this book is ideal for your studies and professional growth.

Concepts of Database Management System is designed to meet the syllabi requirements of undergraduate students of computer applications and computer science. It describes the concepts in an easy-to-understand language with sufficient number of examples. The overview of emerging trends in databases is thoroughly explained. A brief introduction to PL/SQL, MS-Access and Oracle is discussed to help students get a flavor of different types of database management systems.

DATA CONSCIENCE ALGORITHMIC S1EGE ON OUR HUM4N1TY EXPLORE HOW D4TA STRUCTURES C4N HELP OR H1NDER SOC1AL EQU1TY Data has enjoyed ‘bystander’ status as we’ve attempted to digitize responsibility and morality in tech. In fact, data’s importance should earn it a spot at the center of our thinking and strategy around building a better, more ethical world. It’s use—and misuse—lies at the heart of many of the racist, gendered, classist, and otherwise oppressive practices of modern tech. In Data Conscience: Algorithmic Siege on our Humanity, computer science and data inclusivity thought leader Dr. Brandeis Hill Marshall delivers a call to action for rebel tech leaders, who acknowledge and are prepared to address the current limitations of software development. In the book, Dr. Brandeis Hill Marshall discusses how the philosophy of “move fast and break things” is, itself, broken, and requires change. You’ll learn about the ways that discrimination rears its ugly head in the digital data space and how to address them with several known algorithms, including social network analysis, and linear regression A can’t-miss resource for junior-level to senior-level software developers who have gotten their hands dirty with at least a handful of significant software development projects, Data Conscience also provides readers with: Discussions of the importance of transparency Explorations of computational thinking in practice Strategies for encouraging accountability in tech Ways to avoid double-edged data visualization Schemes for governing data structures with law and algorithms

DESIGNING BIG DATA PLATFORMS Provides expert guidance and valuable insights on getting the most out of Big Data systems An array of tools are currently available for managing and processing data—some are ready-to-go solutions that can be immediately deployed, while others require complex and time-intensive setups. With such a vast range of options, choosing the right tool to build a solution can be complicated, as can determining which tools work well with each other. Designing Big Data Platforms provides clear and authoritative guidance on the critical decisions necessary for successfully deploying, operating, and maintaining Big Data systems. This highly practical guide helps readers understand how to process large amounts of data with well-known Linux tools and database solutions, use effective techniques to collect and manage data from multiple sources, transform data into meaningful business insights, and much more. Author Yusuf Aytas, a software engineer with a vast amount of big data experience, discusses the design of the ideal Big Data platform: one that meets the needs of data analysts, data engineers, data scientists, software engineers, and a spectrum of other stakeholders across an organization. Detailed yet accessible chapters cover key topics such as stream data processing, data analytics, data science, data discovery, and data security. This real-world manual for Big Data technologies: Provides up-to-date coverage of the tools currently used in Big Data processing and management Offers step-by-step guidance on building a data pipeline, from basic scripting to distributed systems Highlights and explains how data is processed at scale Includes an introduction to the foundation of a modern data platform Designing Big Data Platforms: How to Use, Deploy, and Maintain Big Data Systems is a must-have for all professionals working with Big Data, as well researchers and students in computer science and related fields.

Multi-Paradigm Modelling for Cyber-Physical Systems explores modeling and analysis as crucial activities in the development of Cyber-Physical Systems, which are inherently cross-disciplinary in nature and require distinct modeling techniques related to different disciplines, as well as a common background knowledge. This book will serve as a reference for anyone starting in the field of CPS who needs a solid foundation of modeling, including a comprehensive introduction to existing techniques and a clear explanation of their advantages and limitations. This book is aimed at both researchers and practitioners who are interested in various modeling paradigms across computer science and engineering. Identifies key problems and offers solution approaches as well as tools which have been developed or are necessary for modeling paradigms across cyber physical systems Explores basic theory and current research topics, related challenges, and research directions for multi-paradigm modeling Provides a complete, conceptual overview and framework of the research done by the MPM4CPS working groups and the different types of modeling paradigms developed

This volume describes frontiers in social-behavioral modeling for contexts as diverse as national security, health, and on-line social gaming. Recent scientific and technological advances have created exciting opportunities for such improvements. However, the book also identifies crucial scientific, ethical, and cultural challenges to be met if social-behavioral modeling is to achieve its potential. Doing so will require new methods, data sources, and technology. The volume discusses these, including those needed to achieve and maintain high standards of ethics and privacy. The result should be a new generation of modeling that will advance science and, separately, aid decision-making on major social and security-related subjects despite the myriad uncertainties and complexities of social phenomena. Intended to be relatively comprehensive in scope, the volume balances theory-driven, data-driven, and hybrid approaches. The latter may be rapidly iterative, as when artificial-intelligence methods are coupled with theory-driven insights to build models that are sound, comprehensible and usable in new situations. With the intent of being a milestone document that sketches a research agenda for the next decade, the volume draws on the wisdom, ideas and suggestions of many noted researchers who draw in turn from anthropology, communications, complexity science, computer science, defense planning, economics, engineering, health systems, medicine, neuroscience, physics, political science, psychology, public policy and sociology. In brief, the volume discusses: Cutting-edge challenges and opportunities in modeling for social and behavioral science Special requirements for achieving high standards of privacy and ethics New approaches for developing theory while exploiting both empirical and computational data Issues of reproducibility, communication, explanation, and validation Special requirements for models intended to inform decision making about complex social systems

Understand how and why the separation between layers and tiers in service-oriented architectures holds software developers back from being truly productive, and how you can remedy that problem. Strong processes and development tools can help developers write more complex software, but large amounts of code can still be directly deduced from the underlying database model, hampering developer productivity. In a world with a shortage of developers, this is bad news. More code also increases maintenance costs and the risk of bugs, meaning less time is spent improving the quality of systems. You will learn that by making relationships first-class citizens within an item/relationship model, you can develop an extremely compact query language, inspired by natural language. You will also learn how this model can serve as both a database schema and an object model upon which to build business logic. Implicit services free you from writing code for standard read/write operations, while still supporting fine-grained authorization. Vertically Integrated Architectures explains how functional schema mappings can solve database migrations and service versioning at the same time, and how all this can support any client, from free-format to fully vertically integrated types. Unleash the potential and use VIA to drastically increase developer productivity and quality. What You'll Learn See how the separation between application server and database in a SOA-based architecture might be justifiable from a historical perspective, but can also hold us back Examine how the vertical integration of application logic and database functionality can drastically increase developer productivity and quality Review why application developers only need to write pure business logic if an architecture takes care of basic read/write client-server communication and data persistence Understand why a set-oriented and persistence-aware programming language would not only make it easier to build applications, but would also enable the fully optimized execution of incoming service requests Who This Book Is For Software architects, senior software developers, computer science professionals and students, and the open source community.

Aimed at readers with a knowledge of geographic information systems (GIS) but no formal training in computer science, this book provides a clear and accessible introduction to how GIS store and process spatial data. This updated edition includes two new chapters on databases and heuristics, substantial additional material on indexing and raster imagery, and revisions throughout that incorporate up-to-date applications such as GPS on mobile devices and Internet-based services.

Abstract This IBM® Redbooks® publication is based on the Presentations Guide of the course Essentials of Cloud Application Development on IBM Bluemix that was developed by the IBM Redbooks team in partnership with IBM Skills Academy Program. This course is designed to teach university students the basic skills that are required to develop, deploy, and test cloud-based applications that use the IBM Bluemix® cloud services. The primary target audience for this course is university students in undergraduate computer science and computer engineer programs with no previous experience working in cloud environments. However, anyone new to cloud computing can also benefit from this course. After completing this course, you should be able to accomplish the following tasks: Define cloud computing Describe the factors that lead to the adoption of cloud computing Describe the choices that developers have when creating cloud applications Describe infrastructure as a service, platform as a service, and software as a service Describe IBM Bluemix and its architecture Identify the runtimes and services that IBM Bluemix offers Describe IBM Bluemix infrastructure types Create an application in IBM Bluemix Describe the IBM Bluemix dashboard, catalog, and documentation features Explain how the application route is used to test an application from the browser Create services in IBM Bluemix Describe how to bind services to an application in IBM Bluemix Describe the environment variables that are used with IBM Bluemix services Explain what are IBM Bluemix organizations, domains, spaces, and users Describe how to create an IBM SDK for Node.js application that runs on IBM Bluemix Explain how to manage your IBM Bluemix account with the Cloud Foundry CLI Describe how to set up and use the IBM Bluemix plug-in for Eclipse Describe the role of Node.js for server-side scripting Describe IBM Bluemix DevOps Services and the capabilities of IBM DevOps Services Identify the Web IDE features in IBM Bluemix DevOps Describe how to connect a Git repository client to Bluemix DevOps Services project Explain the pipeline build and deploy processes that IBM Bluemix DevOps Services use Describe how IBM Bluemix DevOps Services integrate with the IBM Bluemix cloud Describe the agile planning tools in IBM Bluemix Describe the characteristics of REST APIs Explain the advantages of the JSON data format Describe an example of REST APIs using Watson Describe the main types of data services in IBM Bluemix Describe the benefits of IBM Cloudant® Explain how Cloudant databases and documents are accessed from IBM Bluemix Describe how to use REST APIs to interact with Cloudant database Describe Bluemix mobile backend as a service (MBaaS) and the MBaaS architecture Describe the Push Notifications service Describe the App ID service Describe the Kinetise service Describe how to create Bluemix Mobile applications by using MobileFirst Services Starter Boilerplate The workshop materials were created in June 2017. Therefore, all IBM Bluemix features that are described in this Presentations Guide and IBM Bluemix user interfaces that are used in the examples are current as of June 2017.

Abstract This IBM® Redbooks® publication is based on the Presentations Guide of the course "Essentials of Cloud Application Development on IBM Bluemix" that was developed by the IBM Redbooks team in partnership with IBM Middle East and Africa (MEA) University Program. This course is designed to teach university students the basic skills that are required to develop, deploy, and test cloud-based applications that use the IBM Bluemix® cloud services. The primary target audience for this course is university students in undergraduate computer science and computer engineer programs with no previous experience working in cloud environments. However, anyone new to cloud computing can benefit from this course. After completing this course, you should be able to accomplish these tasks: Describe the factors that lead to the adoption of cloud computing. Describe infrastructure as a service, platform as a service, and software as a service. Define cloud computing. Describe IBM Bluemix. Describe the architecture of IBM Bluemix. Identify the runtimes and services that Bluemix offers. Explain how to get started with Bluemix. Describe Bluemix organizations, domains, spaces, and users. Create Bluemix applications. Use services in a Bluemix application. Set environmental variables that are used with Bluemix services. Deploy and run Bluemix applications. Describe how to create an IBM SDK for Node.js application that runs on Bluemix. Explain how to manage a Bluemix account with the Cloud Foundry CLI.[ ]Describe how to integrate workstation development platforms with Bluemix. Manage application code and assets with IBM Bluemix DevOps services. Work with the Git repository that is used by DevOps services. Describe the characteristics of REST APIs. Describe the use of JSON as the preferred data format for REST APIs. dentify the data services that are available on Bluemix. Describe the features in Bluemix for developing mobile applications. Create a MobileFirst Services Starter application on Bluemix. Send push notifications from Bluemix and receive them on the mobile device emulator. The workshop materials were created in August 2016. Thus, all IBM Bluemix features discussed in this Presentations Guide and Bluemix user interfaces used in the examples are current as of August 2016. Note: This IBM Redbooks publication references exercises that are NOT included with this book. The exercises are only available to students attending the course.

This title includes a number of Open Access chapters. Data structure and software engineering is an integral part of computer science. This volume presents new approaches and methods to knowledge sharing, brain mapping, data integration, and data storage. The author describes how to manage an organization’s business process and domain data and presents new software and hardware testing methods. The book introduces a game development framework used as a learning aid in a software engineering at the university level. It also features a review of social software engineering metrics and methods for processing business information. It explains how to use Pegasys to create and manage sequence analysis workflows.

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.

This handbook provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from statistics and computer science experts in industry and academia, the text instills a working understanding of key statistical and computing ideas that can be readily applied in research and practice. Offering balanced coverage of methodology, theory, and applications, the text describes modern, scalable approaches for analyzing large datasets. It details advances in statistics and machine learning, as well as defines the underlying concepts of the available analytical tools and techniques.

Advanced data management has always been at the core of efficient database and information systems. Recent trends like big data and cloud computing have aggravated the need for sophisticated and flexible data storage and processing solutions. This book provides a comprehensive coverage of the principles of data management developed in the last decades with a focus on data structures and query languages. It treats a wealth of different data models and surveys the foundations of structuring, processing, storing and querying data according these models. Starting off with the topic of database design, it further discusses weaknesses of the relational data model, and then proceeds to convey the basics of graph data, tree-structured XML data, key-value pairs and nested, semi-structured JSON data, columnar and record-oriented data as well as object-oriented data. The final chapters round the book off with an analysis of fragmentation, replication and consistency strategies for data management in distributed databases as well as recommendations for handling polyglot persistence in multi-model databases and multi-database architectures. While primarily geared towards students of Master-level courses in Computer Science and related areas, this book may also be of benefit to practitioners looking for a reference book on data modeling and query processing. It provides both theoretical depth and a concise treatment of open source technologies currently on the market.

Since its first volume in 1960, Advances in Computers has presented detailed coverage of innovations in computer hardware, software, theory, design, and applications. It has also provided contributors with a medium in which they can explore their subjects in greater depth and breadth than journal articles usually allow. As a result, many articles have become standard references that continue to be of significant, lasting value in this rapidly expanding field. In-depth surveys and tutorials on new computer technology Well-known authors and researchers in the field Extensive bibliographies with most chapters Many of the volumes are devoted to single themes or subfields of computer science

Fundamentals of Database Indexing and Searching presents well-known database searching and indexing techniques. It focuses on similarity search queries, showing how to use distance functions to measure the notion of dissimilarity. After defining database queries and similarity search queries, the book organizes the most common and representative index structures according to their characteristics. The author first describes low-dimensional index structures, memory-based index structures, and hierarchical disk-based index structures. He then outlines useful distance measures and index structures that use the distance information to efficiently solve similarity search queries. Focusing on the difficult dimensionality phenomenon, he also presents several indexing methods that specifically deal with high-dimensional spaces. In addition, the book covers data reduction techniques, including embedding, various data transforms, and histograms. Through numerous real-world examples, this book explores how to effectively index and search for information in large collections of data. Requiring only a basic computer science background, it is accessible to practitioners and advanced undergraduate students.

Concepts of Database Management System is designed to meet the syllabi requirements of undergraduate students of computer applications and computer science. It describes the concepts in an easy-to-understand language with sufficient number of examples. The overview of emerging trends in databases is thoroughly explained. A brief introduction to PL/SQL, MS-Access and Oracle is discussed to help students get a flavor of different types of database management systems.

Introduces various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges facing society Handbook of Real-World Applications in Modeling and Simulation provides a thorough explanation of modeling and simulation in the most useful, current, and predominant applied areas of transportation, homeland security, medicine, operational research, military science, and business modeling. Offering a cutting-edge and accessible presentation, this book discusses how and why the presented domains have become leading applications of modeling and simulation techniques. Contributions from leading academics and researchers integrate modeling and simulation theories, methods, and data to analyze challenges that involve technological and social issues. The book begins with an introduction that explains why modeling and simulation is a reliable analysis assessment tool for complex systems problems. Subsequent chapters provide an orientation to various modeling and simulation methods and paradigms that are used to explain and solve the predominant challenges across real-world applied domains. Additionally, the handbook: Provides a practical one-stop reference on modeling and simulation and contains an accessible introduction to key concepts and techniques Introduces, trains, and prepares readers from statistics, mathematics, engineering, computer science, economics, and business to use modeling and simulation in their studies and research Features case studies that are representative of fundamental areas of multidisciplinary studies and provides a concise look at the key concepts of modeling and simulation Contains a collection of original ideas on modeling and simulation to help academics and practitioners develop a multifunctional perspective Self-contained chapters offer a comprehensive approach to explaining each respective domain and include sections that explore the related history, theory, modeling paradigms, and case studies. Key terms and techniques are clearly outlined, and exercise sets allow readers to test their comprehension of the presented material. Handbook of Real-World Applications in Modeling and Simulation is an essential reference for academics and practitioners in the areas of operations research, business, management science, engineering, statistics, mathematics, and computer science. The handbook is also a suitable supplement for courses on modeling and simulation at the graduate level.

The first complete, practical guide to XPages development - direct from members of the XPages development team at IBM Lotus Martin Donnelly, Mark Wallace, and Tony McGuckin have written the definitive programmer's guide to utilizing this breakthrough technology. Packed with tips, tricks, and best practices from IBM's own XPages developers, Mastering XPages brings together all the information developers need to become experts - whether you’re experienced with Notes/Domino development or not. The authors start from the very beginning, helping developers steadily build your expertise through practical code examples and clear, complete explanations. Readers will work through scores of real-world XPages examples, learning cutting-edge XPages and XSP language skills and gaining deep insight into the entire development process. Drawing on their own experience working directly with XPages users and customers, the authors illuminate both the technology and how it can be applied to solving real business problems. Martin Donnelly previously led a software startup that developed and distributed small business accounting software. Donnelly holds a Commerce degree from University College Cork and an M.S. in Computer Science from Boston University. Mark Wallace has worked at IBM for 15 years on many projects as a technical architect and application developer. Tony McGuckin participates in the Lotus OneUI Web Application and iWidget Adoption Workgroup. He holds a bachelor's degree in Software Engineering from the University of Ulster.