EAN: 9783031010088

SYNTHESIS LECTURES ON HUMAN LANGUAGE TECHNOLOGI...

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

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