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Data-Intensive Text Processing with MapReduce (Synthesis Lectures on Human Language Technologies), by Jimmy Lin, Chris Dyer
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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
- Sales Rank: #597759 in Books
- Brand: Brand: Morgan and Claypool Publishers
- Published on: 2010-04-30
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .41" w x 7.50" l, .70 pounds
- Binding: Paperback
- 178 pages
- Used Book in Good Condition
Most helpful customer reviews
3 of 3 people found the following review helpful.
Educating book
By Dimitri K
The book is educating and, hopefully, will be helpful for writing map-reduce programs. It concentrates not on API, but on algorithms, which is rare and should be appreciated. Text-processing is a good example of data-intensive processing, but the book may be useful in many other fields.
4 of 5 people found the following review helpful.
A Valuable Resource for Hadoop Programmers
By Photo/Video Gearhead
This book provides a valuable discussion of the strategies involved in developing MapReduce algorithms. Although I have done considerable parallel programming using MPI and OpenMP, I find MapReduce algorithms to be somewhat non-intuitive, and this book has helped me to overcome that barrier. I have revisited this book from time to time as I have gained more and more experience with MapReduce, and I am amazed to find that this book becomes more relevant each time that I revisit it.
0 of 0 people found the following review helpful.
This book is very much helpful to get good insight about the Mapper
By janardhan
So far I studied upto chapter 3. This book is very much helpful to get good insight about the Mapper, reducer, combiner and partitioner. I almost highlighted most of the lines in this as important points in chapter 2 and 3.
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