Data-Intensive Text Processing with MapReduce - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-Intensive Text Processing with MapReduce write by Jimmy Lin. This book was released on 2022-05-31. Data-Intensive Text Processing with MapReduce available in PDF, EPUB and Kindle. Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks
Data-intensive Text Processing with MapReduce
Data-intensive Text Processing with MapReduce - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-intensive Text Processing with MapReduce write by Jimmy Lin. This book was released on 2010. Data-intensive Text Processing with MapReduce available in PDF, EPUB and Kindle. This volume is a printed version of a work that appears in the Synthesis Digital Library of Engineering and Computer Science. Synthesis Lectures provide concise, original presentations of important research and development topics, published quickly, in digital and print formats. For more information visit www.morganclaypool.com --Book Jacket.
Data-Intensive Text Processing with MapReduce
Data-Intensive Text Processing with MapReduce - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-Intensive Text Processing with MapReduce write by Jimmy Lin. This book was released on 2010-10-10. Data-Intensive Text Processing with MapReduce available in PDF, EPUB and Kindle. Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well. Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks
Data-intensive Systems
Data-intensive Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Data-intensive Systems write by Tomasz Wiktorski. This book was released on 2019-01-01. Data-intensive Systems available in PDF, EPUB and Kindle. Data-intensive systems are a technological building block supporting Big Data and Data Science applications.This book familiarizes readers with core concepts that they should be aware of before continuing with independent work and the more advanced technical reference literature that dominates the current landscape. The material in the book is structured following a problem-based approach. This means that the content in the chapters is focused on developing solutions to simplified, but still realistic problems using data-intensive technologies and approaches. The reader follows one reference scenario through the whole book, that uses an open Apache dataset. The origins of this volume are in lectures from a master’s course in Data-intensive Systems, given at the University of Stavanger. Some chapters were also a base for guest lectures at Purdue University and Lodz University of Technology.
Designing Data-Intensive Applications
Designing Data-Intensive Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Designing Data-Intensive Applications write by Martin Kleppmann. This book was released on 2017-03-16. Designing Data-Intensive Applications available in PDF, EPUB and Kindle. Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures
