Information Retrieval is at the core of our daily lives. Modern search, ranking and indexing systems underpinned by enhanced computing power, fast network speeds and near unlimited data storage capacity mean we have easy access to all the information we need, when we need it. Yet the principles upon which this modern technology based date back to before the 1960s. In this concise history of the early years of Information Retrieval, Donna Harman, one of the pioneers of the field, provides the reader with a plethora of insights into the important work that led us to where we are today. Written in a chronological order and in a manner that presents the technical context, the research and the early commercialization efforts, it lays out how each contribution built on what went before. The reader is offered a text that is not only a delight to read, but is also insightful in the way the technologies evolve as computing power increases. Information Retrieval: The Early Years will be of interest to everyone with an interest in understanding the foundations of the science behind search engines.
Since the first publication of The Mythical Man-Month in 1975, no software engineer´s bookshelf has been complete without it. Many software engineers and computer scientists have claimed to be on their second or third copy of the book. Now, Addison-Wesley is proud to present the 20th anniversary edition-and first revised edition ever-of Fred Brooks´s now legendary collection of essays on the management of computer programming projects. The 20th Anniversary edition is an updated, enhanced re-release of the Brooks classic. Included are all of the existing essays that were originally presented, with the addition of three new essays assessing the current status of software project management. Brooks´s well-known 1986 article, No Silver Bullet, is also included. This 20th Anniversary edition is a major event in computer publishing.
Updated new edition of Ralph Kimball´s groundbreaking book on dimensional modeling for data warehousing and business intelligence! The first edition of Ralph Kimball´s The Data Warehouse Toolkit introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more. * Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence * Begins with fundamental design recommendations and progresses through increasingly complex scenarios * Presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more * Draws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more Design dimensional databases that are easy to understand and provide fast query response with The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition.
As a software architect you work in a wide-ranging and dynamic environment. You have to understand the needs of your customer, design architectures that satisfy both functional and non-functional requirements, and lead development teams in implementing the architecture. And it is an environment that is constantly changing: trends such as cloud computing, service orientation, and model-driven procedures open up new architectural possibilities. This book will help you to develop a holistic architectural awareness and knowledge base that extends beyond concrete methods, techniques, and technologies. It will also help you to acquire or expand the technical, methodological, and social competences that you need. The authors place the spotlight on you, the architect, and offer you long-term architectural orientation. They give you numerous guidelines, checklists, and best practices to support you in your practical work. ´´Software Architecture´´ offers IT students, software developers, and software architects a holistic and consistent orientation across relevant topics. The book also provides valuable information and suggestions for system architects and enterprise architects, since many of the topics presented are also relevant for their work. Furthermore, IT project leads and other IT managers can use the book to acquire an enhanced understanding of architecture. Further information is available at www.software-architecture-book.org.
This is the first textbook on pattern recognition to present the Bayesian viewpoint. It presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible, and it uses graphical models to describe probability distributions. The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra isrequired, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.Coming soon: For students, worked solutions to a subset of exercises available on a public web site (for exercises marked ´´www´´ in the text) For instructors, worked solutions to remaining exercises from the Springer web site Lecture slides to accompany each chapter Data sets available for download
´´Eric Evans has written a fantastic book on how you can make the design of your software match your mental model of the problem domain you are addressing. ´´His book is very compatible with XP. It is not about drawing pictures of a domain; it is about how you think of it, the language you use to talk about it, and how you organize your software to reflect your improving understanding of it. Eric thinks that learning about your problem domain is as likely to happen at the end of your project as at the beginning, and so refactoring is a big part of his technique. ´´The book is a fun read. Eric has lots of interesting stories, and he has a way with words. I see this book as essential reading for software developers-it is a future classic.´´ - Ralph Johnson , author of Design Patterns ´´If you don´t think you are getting value from your investment in object-oriented programming, this book will tell you what you´ve forgotten to do. ´´Eric Evans convincingly argues for the importance of domain modeling as the central focus of development and provides a solid framework and set of techniques for accomplishing it. This is timeless wisdom, and will hold up long after the methodologies du jour have gone out of fashion.´´ - Dave Collins , author of Designing Object-Oriented User Interfaces ´´Eric weaves real-world experience modeling-and building-business applications into a practical, useful book. Written from the perspective of a trusted practitioner, Eric´s descriptions of ubiquitous language, the benefits of sharing models with users, object life-cycle management, logical and physical application structuring, and the process and results of deep refactoring are major contributions to our field.´´ - Luke Hohmann , author of Beyond Software Architecture This book belongs on the shelf of every thoughtful software developer. --Kent Beck What Eric has managed to capture is a part of the design process that experienced object designers have always used, but that we have been singularly unsuccessful as a group in conveying to the rest of the industry. We´ve given away bits and pieces of this knowledge...but we´ve never organized and systematized the principles of building domain logic. This book is important. --Kyle Brown, author of Enterprise Java(TM) Programming with IBM® WebSphere® The software development community widely acknowledges that domain modeling is central to software design. Through domain models, software developers are able to express rich functionality and translate it into a software implementation that truly serves the needs of its users. But despite its obvious importance, there are few practical resources that explain how to incorporate effective domain modeling into the software development process. Domain-Driven Design fills that need. This is not a book about specific technologies. It offers readers a systematic approach to domain-driven design, presenting an extensive set of design best practices, experience-based techniques, and fundamental principles that facilitate the development of software projects facing complex domains. Intertwining design and development practice, this book incorporates numerous examples based on actual projects to illustrate the application of domain-driven design to real-world software development. Readers learn how to use a domain model to make a complex development effort more focused and dynamic. A core of best practices and standard patterns provides a common language for the development team. A shift in emphasis--refactoring not just the code but the model underlying the code--in combination with the frequent iterations of Agile development leads to deeper insight into domains and enhanced communication between domain expert and programmer. Domain-Dr
The Ada 2012 Reference Manual is an enhanced version of the text of International Standard ISO/IEC 8652/2012(E) for the programming language Ada. The Ada 2012 Reference Manual combines all of the previous corrections of Technical Corrigendum 1 and Amendment 1 with changes and additions that improve the capabilities of the language and the reliability of programs written in the language. The Ada 2012 Reference Manual will replace the former versions as an indispensable working companion for anybody using Ada professionally or learning and studying the language systematically.
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of ´´boosting,´´ how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.