Principles of Concurrent and Distributed Programming provides an introduction to concurrent programming focusing on general principles and not on specific systems. Software today is inherently concurrent or distributed - from event-based GUI designs to operating and real-time systems to Internet applications. The new edition of this classic introduction to concurrency has been completely revised in view of the growing importance of concurrency constructs embedded in programming languages and of formal methods such as model checking that are widely used in industry.
Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today´s data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand Enables you to build your own algorithms and implement your own data integration applications
Examines and illustrates fundamental concepts in computer system design that are common across operating systems, networks, database systems, distributed systems, programming languages, software engineering, security, fault tolerance, and architecture. This title presents numerous pseudocode fragments that provide examples of abstract concepts.
Program analysis concerns static techniques for computing reliable approximate information about the dynamic behaviour of programs. Applications include compilers (for code improvement), software validation (for detecting errors in algorithms or breaches of security) and transformations between data representation (for solving problems such as the Y2K problem). This book is unique in giving an overview of the four major approaches to program analysis: data flow analysis, constraint based analysis, abstract interpretation, and type and effect systems. The presentation demonstrates the extensive similarities between the approaches; this will aid the reader in choosing the right approach and in enhancing it with insights from the other approaches. The book covers basic semantic properties as well as more advanced algorithmic techniques. The book is aimed at M.Sc. and Ph.D. students but will be valuable also for experienced researchers and professionals.
Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering
Through expert guidance backed by years of experience, the second edition of this practical guide provides you with more than 100 patterns, principles, and best practices for creating user interfaces that will make your social websites truly effective. Creating a social website to foster user interaction and community building is a core skill for developers and designers, but grasping the nuances of the social web is much harder than it appears. Now you have help. In this edition, Christian Crumlish and Erin Malone share hard-won insights into what works, what doesn?t, and why. You?ll learn how to balance opposing factions and grow healthy online communities by co-creating them with your users. New content includes valuable information on mobile and enterprise sites.
Teaches readers how to build big data systems using Lambda Architecture, an architecture designed specifically to capture and analyze web-scale data.
This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the ´´data-analytic thinking´´ necessary for extracting useful knowledge and business value from the data you collect. By learning data science principles, you will understand the many data-mining techniques in use today. More importantly, these principles underpin the processes and strategies necessary to solve business problems through data mining techniques. Introduces fundamental concepts of data science necessary for extracting useful information from data mining techniques, including envisioning the problem, applying data science techniques, and deploying results to improve decision making.