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.
This course is about scalable approaches to processing large amounts of information (terabytes and even petabytes). We focus mostly on MapReduce, which is presently the most accessible and practical means of computing at this scale, but will discuss other approaches as well.
G. Sadasivam, und G. Baktavatchalam. MDAC '10: Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud, Seite 1--7. New York, NY, USA, ACM, (2010)
A. Goyal, F. Bonchi, und L. Lakshmanan. WSDM '10: Proceedings of the third ACM international conference on Web search and data mining, Seite 241--250. New York, NY, USA, ACM, (2010)
M. Bayir, I. Toroslu, A. Cosar, und G. Fidan. WWW '09: Proceedings of the 18th international conference on World wide web, Seite 161--170. New York, NY, USA, ACM, (2009)