Your Search Results

      • Trusted Partner
        Computing & IT
        September 2021

        Artificial intelligence and the future of warfare

        The USA, China, and strategic stability

        by James Johnson

        This volume offers an innovative and counter-intuitive study of how and why artificial intelligence-infused weapon systems will affect the strategic stability between nuclear-armed states. Johnson demystifies the hype surrounding artificial intelligence (AI) in the context of nuclear weapons and, more broadly, future warfare. The book highlights the potential, multifaceted intersections of this and other disruptive technology - robotics and autonomy, cyber, drone swarming, big data analytics, and quantum communications - with nuclear stability. Anticipating and preparing for the consequences of the AI-empowered weapon systems are fast becoming a critical task for national security and statecraft. Johnson considers the impact of these trends on deterrence, military escalation, and strategic stability between nuclear-armed states - especially China and the United States. The book draws on a wealth of political and cognitive science, strategic studies, and technical analysis to shed light on the coalescence of developments in AI and other disruptive emerging technologies. Artificial intelligence and the future of warfare sketches a clear picture of the potential impact of AI on the digitized battlefield and broadens our understanding of critical questions for international affairs. AI will profoundly change how wars are fought, and how decision-makers think about nuclear deterrence, escalation management, and strategic stability - but not for the reasons you might think.

      • Trusted Partner
        Computing & IT
        September 2021

        Artificial intelligence and the future of warfare

        The USA, China, and strategic stability

        by James Johnson

        This volume offers an innovative and counter-intuitive study of how and why artificial intelligence-infused weapon systems will affect the strategic stability between nuclear-armed states. Johnson demystifies the hype surrounding artificial intelligence (AI) in the context of nuclear weapons and, more broadly, future warfare. The book highlights the potential, multifaceted intersections of this and other disruptive technology - robotics and autonomy, cyber, drone swarming, big data analytics, and quantum communications - with nuclear stability. Anticipating and preparing for the consequences of the AI-empowered weapon systems are fast becoming a critical task for national security and statecraft. Johnson considers the impact of these trends on deterrence, military escalation, and strategic stability between nuclear-armed states - especially China and the United States. The book draws on a wealth of political and cognitive science, strategic studies, and technical analysis to shed light on the coalescence of developments in AI and other disruptive emerging technologies. Artificial intelligence and the future of warfare sketches a clear picture of the potential impact of AI on the digitized battlefield and broadens our understanding of critical questions for international affairs. AI will profoundly change how wars are fought, and how decision-makers think about nuclear deterrence, escalation management, and strategic stability - but not for the reasons you might think.

      • Trusted Partner
        Medicine
        May 2021

        Embodiment and everyday cyborgs

        Technologies that alter subjectivity

        by Gill Haddow

        Your organs are failing and require replacement. If you had the choice, would you prefer organs from other humans or non-human animals, or would you choose a 'cybernetic' medical implant? Using a range of social science methods and drawing on the sociology of the body and embodiment, biomedicine and technology, this book asks what happens to who we are (our identity) when we change what we are (our bodies)? From surveying young adults about whether they would choose options such as 3-D bioprinting, living or deceased human donation, or non-human animal or implantable biomechanical devices, to interviewing those who live with an implantable cardiac defibrillator, Haddow invites us to think about what kind of relationship we have with our bodies. She concludes that the reliance on 'cybernetic' medical devices create 'everyday cyborgs' who can experience alienation and new forms of vulnerability at implantation and activation. Embodiment and everyday cyborgs invites readers to consider the relationship between personal identity and the body, between humans and non-human animals, and our increasing dependency on 'smart' implantable technology. The creation of new techno-organic hybrid bodies makes us acutely aware of our own bodies and how ambiguous the experience of embodiment actually is. It is only through understanding how modifications such as transplantation, amputation and implantation make our bodies a 'presence' to us, Haddow argues, that we realise our everyday experience of our bodies as an absence.

      • Trusted Partner
        Computing & IT
        September 2021

        Artificial intelligence and the future of warfare

        The USA, China, and strategic stability

        by James Johnson

        This volume offers an innovative and counter-intuitive study of how and why artificial intelligence-infused weapon systems will affect the strategic stability between nuclear-armed states. Johnson demystifies the hype surrounding artificial intelligence (AI) in the context of nuclear weapons and, more broadly, future warfare. The book highlights the potential, multifaceted intersections of this and other disruptive technology - robotics and autonomy, cyber, drone swarming, big data analytics, and quantum communications - with nuclear stability. Anticipating and preparing for the consequences of the AI-empowered weapon systems are fast becoming a critical task for national security and statecraft. Johnson considers the impact of these trends on deterrence, military escalation, and strategic stability between nuclear-armed states - especially China and the United States. The book draws on a wealth of political and cognitive science, strategic studies, and technical analysis to shed light on the coalescence of developments in AI and other disruptive emerging technologies. Artificial intelligence and the future of warfare sketches a clear picture of the potential impact of AI on the digitized battlefield and broadens our understanding of critical questions for international affairs. AI will profoundly change how wars are fought, and how decision-makers think about nuclear deterrence, escalation management, and strategic stability - but not for the reasons you might think.

      • Trusted Partner
        Humanities & Social Sciences
        May 2024

        Knowing COVID-19

        The pandemic and beyond

        by Des Fitzgerald, Fred Cooper

        Knowing COVID-19 demonstrates how researchers in the humanities shone a light on some of the many hidden problems of COVID-19, in the very depths of the pandemic crisis. Drawing on eight COVID-19 research projects, the volume shows how humanities researchers, alongside colleagues in the clinical and life sciences, addressed some of the major critical unknowns about this new infectious disease - from the effects of racism to the risks of deploying shame; from how to design an effective instructional leaflet to how to communicate effectively to bus passengers. Across eight novel case studies, the book showcases how humanities research during a pandemic is not only about interpreting the crisis when it has safely passed, but how it can play a vital, collaborative and instrumental role as events are still unfolding.

      • Computer science
        April 2009

        Data Modeling for the Business

        A Handbook for Aligning the Business With It Using High-level Data Models

        by Steve Hoberman, Donna Burbank, Chris Bradley

        Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach and hands-on exercises to help you practice topics on your own.   Hear one of the authors, Steve Hoberman, talk about this book.   In this book, we review data modeling basics and explain why the core concepts stored in a high-level data model can have significant business impact on an organization. We explain the technical notation used for a data model and walk through some simple examples of building a high-level data model.  We also describe how data models relate to other key initiatives you may have heard of or may be implementing in your organization.   This book contains best practices for implementing a high-level data model, along with some easy-to-use templates and guidelines for a step-by-step approach.  Each step will be illustrated using many examples based on actual projects we have worked on. Names have been changed to protect the innocent, but the pain points and lessons have been preserved. One example spans an entire chapter and will allow you to practice building a high-level data model from beginning to end, and then compare your results to ours. Building a high-level data model following the ten step approach you’ll read about is a great way to ensure you will retain the new skills you learn in this book.   As is the case in many disciplines, using the right tool for the right job is critical to the overall success of your high-level data model implementation.  To help you in your tool selection process, there are several chapters dedicated to discussing what to look for in a high-level data modeling tool and a framework for choosing a data modeling tool, in general.    This book concludes with a real-world case study that shows how an international energy company successfully used a high-level data model to streamline their information management practices and increase communication throughout the organization—between both businesspeople and IT.   One of the most critical systems issues is aligning business with IT and fulfilling business needs using data models. The authors of Data Modeling for the Business do a masterful job at simply and clearly describing the art of using data models to communicate with business representatives and meet business needs. The book provides many valuable tools, analogies, and step-by-step methods for effective data modeling and is an important contribution in bridging the much needed connection between data modeling and realizing business requirements. Len Silverston, author of The Data Model Resource Book series

      • Computer science
        October 2011

        Data Modeling Made Simple with PowerDesigner

        by Steve Hoberman, George McGeachie

        Data Modeling Made Simple with PowerDesigner will provide the business or IT professional with a practical working knowledge of data modeling concepts and best practices, and how to apply these principles with PowerDesigner.   Hear one of the authors, Steve Hoberman, talk about this book.   You'll build many PowerDesigner data models along the way, increasing your skills first with the fundamentals and later with more advanced feature of PowerDesigner. This book combines real-world experience and best practices to help you master the following ten objectives:   This book has ten key objectives for you, the reader: You will know when a data model is needed and which PowerDesigner models are the most appropriate for each situation You will be able to read a data model of any size and complexity with the same confidence as reading a book You will know when to apply and how to make use of all the key features of PowerDesigner You will be able to build, step-by-step in PowerDesigner, a pyramid of linked data models, including a conceptual data model, a fully normalized relational data model, a physical data model, and an easily navigable dimensional model You will be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design You will improve data governance and modeling consistency within your organization by leveraging features such as PowerDesigner’s reference models, Glossary, domains, and model comparison and model mapping techniques You will know how to utilize dependencies and traceability links to assess the impact of change You will know how to integrate your PowerDesigner models with externally-managed files, including the import and export of data using Excel and Requirements documents You will know where you can take advantage of the entire PowerDesigner model set, to increase the success rate of corporate-wide initiatives such as business intelligence and enterprise resource planning (ERP) You will understand the key differentiators between PowerDesigner and other data modeling tools you may have used before

      • Computer science
        July 2012

        The Analytical Puzzle

        by David Haertzen

        Do you enjoy completing puzzles? Perhaps one of the most challenging (yet rewarding) puzzles is delivering a successful data warehouse suitable for data mining and analytics. The Analytical Puzzle describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organization. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualization and mobile devices. Here are the main objectives for each of the book's 19 chapters: Chapter 1: Develop a foundational knowledge of data warehousing, business intelligence and analytics Chapter 2: Build the business case needed to sell your data warehousing project, and then produce a project plan that avoids common pitfalls Chapter 3: Elicit and organize business intelligence and data warehousing business requirements Chapter 4: Specify the technical architecture of the data warehousing system, including software and infrastructure components, technology stack, and non-functional requirements. Gain an understanding of cloud based data warehousing and data warehouse appliances Chapter 5: Learn about data attributes including metrics and key performance indicators (KPIs), the raw material of data warehousing and business intelligence Chapter 6: Learn about data modeling and how to apply design patterns for each part of the data warehouse Chapter 7: Speak the dimensional modeling language of measures, dimensions, facts, cubes, stars, and snowflakes Chapter 8: Organize a successful data governance program. Learn how to manage metadata for your data warehousing and business intelligence project Chapter 9: Identify useful data sources and implement a data quality program Chapter 10: Use database technology for your data warehousing project, and understand the impact of data warehouse appliances, big data, in memory databases, columnar databases and OnLine Analytical Processing (OLAP) Chapter 11: Apply data integration and understand the role data mapping, data cleansing, data transformation, and loading data play in a successful data warehouse Chapter 12: Use the business intelligence (BI) operations of slice, dice, drill down, roll up, and pivot to analyze and present data Chapter 13: Learn about descriptive and predictive statistics, and calculate mean, median, mode, variance and standard deviation Chapter 14: Harness analytical methods such as regression analysis, data mining, and statistics to make profitable decisions and anticipate the future Chapter 15: Appreciate the components and design patterns that compose a successful analytic application Chapter 16: Gain an understanding of the uses and benefits of scorecards and dashboards including support of mobile device users Chapter 17: Gain insight into applications of business intelligence that could profit your organization, including risk management, finance, marketing, government, healthcare, science and sports Chapter 18: Perform customer analytics to better understand and segment your customers Chapter 19: Test, roll out, and sustain the data warehouse David Haertzen is a seasoned data warehouse architect who has helped a diverse set of organizations from start-ups to multinationals to utilize data for their advantage. David teaches data modeling, data warehousing, data architecture, business intelligence and is also active as editor of the Infogoal.com Data Management Center. David is a graduate of the University of Minnesota and holds an MBA from the University of St. Thomas.

      • Computer science
        August 2012

        UML Requirements Modeling For Business Analysts

        by Norman Daoust

        Hear the author, Norman Daoust, talk about his book.   This book provides you with a collection of best practices, guidelines, and tips for using the Unified Modeling Language (UML) for business analysis. The contents have been assembled over the years based on experience and documented best practices. Over sixty easy to understand UML diagram examples will help you to apply these ideas immediately. If you use, expect to use, or think you should use the Unified Modeling Language (UML) or use cases in your business analysis activities, this book will help you: communicate more succinctly and effectively with your stakeholders including your software development team, increase the likelihood that your requirements will be reviewed and understood, reduce requirements analysis, documentation, and review time.   The first three chapters explain the reasons for utilizing the UML for business analysis, present a brief history of the UML and its diagram categories, and describe a set of general modeling guidelines and tips applicable to all of the UML diagram types. Each of the next thirteen chapters is dedicated to a different UML diagram type: Use Case Diagrams Activity Diagrams Interaction Overview Diagrams Class Diagrams Object Diagrams State Machine Diagrams Timing Diagrams Sequence Diagrams Communication Diagrams Composite Structure Diagrams Component Diagrams Deployment Diagrams Package Diagrams   The next two chapters explain additional diagram types that are important for business analysts and that can be created using UML notation: Context Diagrams using Communication diagram notation Data Models using Class diagram notation   These chapters are followed by a chapter that describes criteria for selecting the various diagram types. The final chapter presents a case study.   Norman Daoust is a business analyst trainer, requirements modeler, data modeler, healthcare electronic data exchange specialist, fretted instrument specialist, and organic gardener. He is the principal consultant for Daoust Associates, a company based in Cambridge, Massachusetts, United States. He specializes in business analyst training, information modeling, and healthcare systems data integration.

      • Computer science
        October 2013

        UML Database Modeling Workbook

        by Michael Blaha

        Hear the author, Mike Blaha, talk about his book.   With our appetites for data on the rise, it has become more important than ever to use UML (Unified Modeling 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 modelers can leverage their understanding of existing database notations, as the book extensively compares the UML to traditional data modeling (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 modeling.   This book is concise and to the point. You will learn by induction through reading, practice, and feedback.   Since 1994 Michael Blaha has been a consultant and trainer in conceiving, architecting, modeling, designing, and tuning databases. He has worked with dozens of organizations around the world. Blaha has authored seven U.S. patents, six books, and numerous articles. He received his doctorate from Washington University in St. Louis and is an alumnus of GE Global Research in Schenectady, New York. He has worked in many problem domains and specializes in financial applications.

      • Computer science
        September 2013

        Extreme Scoping

        by Larissa T. Moss

        Do your business intelligence (BI) projects take too long to deliver? Is the value of the deliverables less than satisfactory? Do these projects propagate poor data management practices? If you screamed "yes" to any of these questions, read this book to master a proven approach to building your enterprise data warehouse and BI initiatives. Extreme Scoping, based on the Business Intelligence Roadmap, will show you how to build analytics applications rapidly yet not sacrifice data management and enterprise architecture. In addition, all of the roles required to deliver all seven steps of this agile methodology are explained along with many real-world examples. From Wayne Eckerson's Foreword I've read many books about data warehousing and business intelligence (BI). This book by Larissa Moss is one of the best. I should not be surprised. Larissa has spent years refining the craft of designing, building, and delivering BI applications. Over the years, she has developed a keen insight about what works and doesn't work in BI. This book brings to light the wealth of that development experience. Best of all, this is not some dry text that laboriously steps readers through a technical methodology. Larissa expresses her ideas in a clear, concise, and persuasive manner. I highlighted so many beautifully written and insightful paragraphs in her manuscript that it became comical. I desperately wanted the final, published book rather than the manuscript so I could dog-ear it to death and place it front-and-center in my office bookshelf! From David Well's Foreword Extreme Scoping is rich with advice and guidance for virtually every aspect of BI projects from planning and requirements to deployment and from back-end data management to front-end information and analytics services. Larissa is both a pragmatist and an independent thinker. Those qualities come through in the style of this book. Extreme Scoping is a well-written book that is easy to absorb. It is not full of surprises. It is filled with a lot of common sense and lessons learned through experience. Larissa Moss is founder and president of Method Focus Inc. She began her IT career over 30 years ago, and since 1988 she has worked primarily in data warehousing and business intelligence. She is a world renown author, lecturer, and speaker on the topics of data warehousing, business intelligence, master data management, project management, methodologies, data governance, and enterprise information management. She co-authored the books Data Warehouse Project Management, Impossible Data Warehouse Situations, Business Intelligence Roadmap, and Data Strategy.

      • Computer science
        October 2013

        Enterprise Architecture Made Simple

        by Håkan Edvinsson, Lottie Aderinne

        Hear one of the authors, Håkan Edvinsson, talk about his book.   Learn how to institute and implement enterprise architecture in your organization. You can make a quick start and establish a baseline for your enterprise architecture within ten weeks, then grow and stabilize the architecture over time using the proven Ready, Set, Go Approach.     Reading this book will: Give you directions on how to institute and implement enterprise architecture in your organization. You will be able to build close relationships with stakeholders and delivery teams, but you will not need to micromanage the architecture’s operations. Increase your awareness that enterprise architecture is about business, not information technology. Enable you to initiate and facilitate dramatic business development. The architecture of an enterprise must be tolerant of currently unknown business initiatives. Show you how to get a holistic view of the process of implementing enterprise architecture. Make you aware that information is a key business asset and that information architecture is a key part of the enterprise architecture.  Allow you to learn from our experiences. This book is based on our 30 years of work in the enterprise architecture field, colleagues in Europe, customer cases, and students.     We do not pretend to cover all you need to know about enterprise architecture within these pages. Rather, we give you the information that is most important for effective and successful guidance. Sometimes, less is more.

      • Computer science
        May 2014

        Data Scientist

        by Zacharias Voulgaris, PhD

        As our society transforms into a data-driven one, the role of the Data Scientist is becoming more and more important. If you want to be on the leading edge of what is sure to become a major profession in the not-too-distant future, this book can show you how. Each chapter is filled with practical information that will help you reap the fruits of big data and become a successful Data Scientist: Learn what big data is and how it differs from traditional data through its main characteristics: volume, variety, velocity, and veracity. Explore the different types of Data Scientists and the skillset each one has. Dig into what the role of the Data Scientist requires in terms of the relevant mindset, technical skills, experience, and how the Data Scientist connects with other people. Be a Data Scientist for a day, examining the problems you may encounter and how you tackle them, what programs you use, and how you expand your knowledge and know-how. See how you can become a Data Scientist, based on where you are starting from: a programming, machine learning, or data-related background. Follow step-by-step through the process of landing a Data Scientist job: where you need to look, how you would present yourself to a potential employer, and what it takes to follow a freelancer path. Read the case studies of experienced, senior-level Data Scientists, in an attempt to get a better perspective of what this role is, in practice. At the end of the book, there is a glossary of the most important terms that have been introduced, as well as three appendices - a list of useful sites, some relevant articles on the web, and a list of offline resources for further reading. Dr. Zacharias Voulgaris was born and raised in Greece. Upon completing a 5-year Engineering degree at the Technical University of Crete, he enrolled in the City University of London for a Masters course in Information Systems and Technology. Afterwards, he pursued a PhD in Birkbeck College (University of London), under the joint supervision of Prof. G. Magoulas and Prof. B. Mirkin, in the field of Machine Learning. Upon receiving his doctorate, he was recruited by the Georgia Institute of Technology as a research fellow. Since January 2013 he has been working as a Data Scientist.

      • Computer science
        October 2012

        Secrets of Analytical Leaders

        by Wayne Eckerson

        Imagine spending a day with top analytical leaders and asking any question you want. In this book, Wayne Eckerson illustrates analytical best practices by weaving his perspective with commentary from seven directors of analytics who unveil their secrets of success. With an innovative flair, Eckerson tackles a complex subject with clarity and insight. Each of the book's 20 chapters is a stand-alone essay on an analytical topic, yet collectively they form a concise methodology about how to implement a successful analytics program. Wayne Eckerson has created a unique - and uniquely valuable - book in "The Secrets of Analytical Leaders." He's pulled together the insights of seven leading analytics practitioners, and combined them with his own considerable experience. The result is an interesting dialogue among some really smart and distinguished people. I'm not sure which is more interesting - the format or the content! Thomas H. Davenport Visiting Professor, Harvard Business School Co-Founder and Director of Research International Institute for Analytics Professors are hard at work molding the world's future analytical leaders - but the chasm between theoretical textbook learning and the real world is considerable. Wayne, with his customary clarity and insight, helps professors cross the chasm by delivering frameworks, commentary, and scenarios that are real, interesting, and important. Hugh Watson Professor of MIS and a C. Herman and Mary Virginia Chair of Business Administration Terry College of Business, University of Georgia and Barbara Wixom Associate Professor at the McIntire School of Commerce, University of Virginia Research Affiliate, Sloan School of Management, Massachusetts Institute of Technology From the Foreword by Michael Halbherr, Executive Vice President, Nokia We are living in a time of radical change. From my vantage point as head of Nokia's Location and Commerce business, I see many business and technical trends shaping our future—and all depend on a new commodity: data. In our mapping business, I see the need to evolve from a road-centric tool to something that allows people to truly understand and maneuver the complexities of a modern city. To accomplish this, we need a lot of data and ways to correlate disparate information into what we call "Smart Data." Analytics is core to what we do, and how we deliver value to customers today and in the future. I recently spoke to the Nokia board about our data, and some members questioned how we could monetize this asset. Since a few members are executives in the oil industry, I told them that data is the "oil of the future", and that you monetize this new resource the same way you monetize oil, by spending time and money refining it. In our case, we are refining data about people, locations, social interactions, traffic, musical preferences, and so on to bring maps to life. The analytical leaders profiled in this book demonstrate how to refine data for business gain and innovation. They play a pivotal role by bridging the worlds of business and technology. When supported by the business, they've delivered remarkable solutions that have given their organizations a competitive edge. I highly recommend this book to anyone who wants to monetize the most important resource of our time: data. It's written in language that both a CEO and a CIO can understand, and carries important lessons no matter what side of the business-technology aisle someone sits. Wayne Eckerson has been a thought leader in the business intelligence and analytics field since the early 1990s. He has conducted numerous in-depth research studies and is the author of the best-selling book Performance Dashboards: Measuring, Monitoring, and Managing Your Business. He is a noted keynote speaker and blogger, and he consults and conducts workshops on many topics. Wayne is the founder of BI Leadership Forum, a network of business intelligence (BI) directors who meet regularly to exchange ideas about best practices in BI and educate the larger BI community. Wayne is also director of research at TechTarget where he writes a popular weekly blog called “Wayne's World of BI” that focuses on industry trends and examines best practices in the application of business intelligence. For many years, Wayne served as director of education and research at The Data Warehousing Institute (TDWI) where he oversaw the company's content and training programs and chaired its BI Executive Summit.

      • Computer science
        January 2011

        Building the Agile Database

        How to Build a Successful Application Using Agile Without Sacrificing Data Management

        by Larry Burns

        Hear the author, Larry Burns, talk about his book.   Is fast development the enemy of good development? Not necessarily. Agile development requires that databases are designed and built quickly enough to meet fast-based delivery schedules - but in a way that also delivers maximum business value and reuse. How can these requirements both be satisfied? This book, suitable for practitioners at all levels, will explain how to design and build enterprise-quality high-value databases within the constraints of an Agile project.   Starting with an overview of the business case for good data management practices, the book defines the various stakeholder groups involved in the software development process, explains the economics of software development (including "time to market" vs. "time to money"), and describes an approach to Agile database development based on the five PRISM principles.   This book explains how to work with application developers and other stakeholders, examines critical issues in Agile Development and Data Management, and describes how developers and data professionals can work together to make Agile projects successful while delivering maximum value data to the enterprise.   Building the Agile Database will serve as an excellent reference for application developers, data managers, DBAs, project managers, Scrum Masters and IT managers looking to get more value from their development efforts. Larry Burns has worked in IT for more than 25 years as a database administrator, application developer, consultant and teacher.

      • Computer science
        November 2011

        UML and Data Modeling

        A Reconciliation

        by David C. Hay

        Here you will learn how to develop an attractive, easily readable, conceptual, business-oriented entity/relationship model, using a variation on the UML Class Model notation.   Hear the author, David Hay, talk about his book.    This book has two audiences:  Data modelers (both analysts and database designers) who are convinced that UML has nothing to do with them; and UML experts who don’t realize that architectural data modeling really is different from object modeling (and that the differences are important). David Hay’s objective is to finally bring these two groups together in peace.   Here all modelers will receive guidance on how to produce a high quality (that is, readable) entity/relationship model to describe the data architecture of an organization.  The notation involved happens to be the one for class models in the Unified Modeling Language, even though  UML was originally developed to support object-oriented design. Designers have a different view of the world from those who develop business-oriented conceptual data models, which means that to use UML for architectural modeling requires some adjustments.  These adjustments are described in this book.   David Hay is the author of Enterprise Model Patterns: Describing the World, a comprehensive model of a generic enterprise. The diagrams were at various levels of abstraction, and they were all rendered in the slightly modified version of UML Class Diagrams presented here.  This book is a handbook to describe how to build models such as these.  By way of background, an appendix provides a history of the two groups, revealing the sources of their different attitudes towards the system development process.   If you are an old-school ER modeler and now find yourself having to come up to speed on UML to get that next job (or keep the current one), this is your guidebook to success. If you are a long time object oriented programmer who has to interact with data modelers, this book is for you too. David has done the hard work of mapping out how to do a logical entity relationship model using standard (and accepted) UML diagram components. This book shows you step-by-step, with ample examples, how to get from here to there with the least pain possible for all concerned. Kent Graziano Certified Data Vault Master and Oracle ACE Past-President of ODTUG & RMOUG

      • Computing & IT
        July 2018

        Artificial Intelligence & Soft Computing For Beginners, Third Edition

        by Anindita Das-Bhattacharjee

        The course of Artificial Intelligence is taken by all engineering undergraduate and postgraduate students pursuing computer science. Apart from this, it is a popular elective in almost all other branches of engineering. It is also a field chosen for research by many doctoral students. This book has in-depth detail illustration of all the chapters of Artificial Intelligence (AI) with Soft Computing, covering the syllabus of WBUT, Gitam, JNTU, NIT and few different universities. During the course of teaching Artificial Intelligence, the author had found that no textbook covers both Artificial intelligence (AI) with intelligent systems (IS) and soft computing in a comprehensive manner. This book provides a comprehensive coverage of the fundamental concepts and techniques in Artificial Intelligence and Soft Computing with mathematical in depth explanation. The main emphasis is on the solution of real world problems using the latest AI techniques. During teaching artificial intelligence, author realized that the basic text books do not have an organized content according to the syllabus. In this book all the chapters are organized properly and contain a complete coverage. The sequence of the chapters has been set in a manner which would be very easy for the students to understand. The main importance of this book is on the solution of many real world problems. Each chapter contains multiple choice questions with answer and possible explanation. Also some important solutions and answers at the end as “Take a look to be more acquainted with”. In addition, modern and current topic in AI for an example Pattern Recognition, Data Clustering Algorithm, Genetic Algorithm with Data Clustering method, Swarm Intelligence, Tabu Search, Ant Colony Optimization are discussed in details. These concepts may motivate students to do projects .This book contains information about programming languages and the proper syntax with example is provided which may help students to practically apply this programming concepts. Mathematical explanation to understand the concept in detail about Single & Multi objective Genetic Algorithm, Neural Network, Fuzzy Logic is provided. The basic coverage of each and every chapter is mentioned before the chapter as “chapter utility”. This book has been designed in such a manner so that it becomes very easy to understand the language and comprehend not only for computer science department student but also for non departmental student as well.

      • Databases
        October 2019

        Data Science for Professionals

        by Prof. N.C. Das

        The book is meant for a wide-spectrum of readership-empirical scientists, consultants, technocrats, advertisers, researchers and students learning Data Science for conducting small replicated or non-replicated experiments / demonstrations. It deals with two-factor three-level designs to be conducted only in six experimental units. For this an exhaustive set of 9c6=9c3 = 84 designs have been generated. Least Square algorithm has been applied for estimating separate effect of each component and their interaction along with fitting of response surface; meeting both the objectives for any factorial experiment. Of them seventy-six could be found as valid or estimable designs and the rest as non-estimable ones. Value of the determinant obtained for Least Square Matrix for each such design has been the indicator of its D-optimality status: the same could also be obtained from geometric structure of the designs. Such exhaustive search ascertains (8+4) D-optimal designs; of them only (1+3) are hitherto known. Also, rotatable feature of any such design does not affect its D-optimality status. A simple method SAME has been devised to meet the said twin objectives, thus escaping matrix formation and related operations. Each such design could be combined to form pair of replicates or blocks of an experiment so as to fetch statistical significance test based on ANOVA and fitting the response surface.

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