From a winner of the ACM/SIGCSE Award, this introduction to concurrency takes into account the importance of concurrency constructs in programming languages and of formal methods such as model checking. It focuses on algorithmic principles, and the use of the Spin model checker for modeling concurrent systems and verifying program correctness.
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
This book provides state-of-the-art coverage of the principles, techniques, and management of issues in cyber security, including threat attacks, privacy, signature and encryption schemes. One of the most important topics addressed concerns lightweight solutions for public key encryption in resource-constrained environments; the book highlights the latest developments in this area. Authentication is another central issue in cyber security. In this book, we address this aspect and sub-aspects ranging from cryptographic approaches to practical design issues, such as CAPTCHA. Privacy is another main topic that is discussed in detail, from techniques for enhancing privacy to pseudonymous schemes. Addressing key issues in the emerging field of cyber security, this book effectively bridges the gap between computer security and threat attacks, and showcases promising applications involving cryptography and security.
Teaches readers how to build big data systems using Lambda Architecture, an architecture designed specifically to capture and analyze web-scale data.
Tackle a variety of tasks in natural language processing by learning how to use the R language and tidy data principles. This practical guide provides examples and resources to help you get up to speed with dplyr, broom, ggplot2, and other tidy tools from the R ecosystem.