ТОП просматриваемых книг сайта:
Synthesis Lectures on Human Language Technologies
Скачать книги из серии Synthesis Lectures on Human Language TechnologiesSemantic Relations Between Nominals - Vivi Nastase
Synthesis Lectures on Human Language Technologies
Grammatical Inference for Computational Linguistics - Jeffrey Heinz
Synthesis Lectures on Human Language Technologies
Natural Language Processing for Historical Texts - Michael Piotrowski
Synthesis Lectures on Human Language Technologies
Computational Modeling of Human Language Acquisition - Afra Alishahi
Synthesis Lectures on Human Language Technologies
Introduction to Arabic Natural Language Processing - Nizar Y. Habash
Synthesis Lectures on Human Language Technologies
Cross-Language Information Retrieval - Jian-Yun Nie
Synthesis Lectures on Human Language Technologies
Data-Intensive Text Processing with MapReduce - Jimmy Lin
Synthesis Lectures on Human Language Technologies
Аннотация
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
Table of Contents: Introduction / MapReduce Basics / MapReduce Algorithm Design / Inverted Indexing for Text Retrieval / Graph Algorithms / EM Algorithms for Text Processing / Closing Remarks