This book aims to show how programming will be changed by the concepts of parallel systems and how these concepts relate to the ideas of functions and objects. To demonstrate the kind of programming that can be done on these systems, the research language Balinda K is used. The book will also enhance the reader's grasp of the concepts of concurrency and modular programming, reviewing these from the perspectives of application programming in a parallel language. The book should interest graduate and senior undergraduate students of computer science and computer engineering, and IT professionals working in a multiprocesing or distributed computing environment.

Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.

During the last few years, parallel object-relational database management systems have emerged as the leading data management technology on the market. These systems are extensible by user-defined data types and user-defined functionality for the data. This work focuses on the efficient parallel execution of user-defined functionality. The main contributions describe techniques to support data parallelism for user-defined scalar and aggregate functions and intra-function parallelism for the execution of a scalar function on a large object, and a new technology to provide extensibility with regard to new set-oriented database operations that can efficiently implement user-defined functionality in parallel object-relational database management systems.

Content Description #Includes bibliographical references and index.

This volume contains papers presented at the Japan-Singapore joint seminar on Parallel Programming Systems sponsored by the Japan Society for the Promotion of Science. The papers cover recent research in Japan and Singapore on hardware systems and language processors for processing parallel programs. The areas discussed include dataflow machines, parallel functional and imperative languages, and parallel application algorithms.

R is a wonderful thing, indeed: in recent years this free, open-source product has become a popular toolkit for statistical analysis and programming. Two of R's limitations -- that it is single-threaded and memory-bound -- become especially troublesome in the current era of large-scale data analysis. It's possible to break past these boundaries by putting R on the parallel path. Parallel R will describe how to give R parallel muscle. Coverage will include stalwarts such as snow and multicore, and also newer techniques such as Hadoop and Amazon's cloud computing platform.

This volume contains papers presented at the DIMACS workshop on Specification of Parallel Algorithms, held in May 1994 at Princeton University. The goal of the workshop was to bring together some of the best researchers in parallel languages, algorithms, and systems to present and discuss recent developments in their areas of expertise. Among the topics discussed were new specification techniques for concurrent and distributed systems, behavioral and operational specification techniques, new parallel language and system abstractions, novel concurrent architectures and systems, large-scale parallel systems, specification tools and environments, and proof techniques for concurrent systems.