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Presented by: Josh Herbach, Google Date: Wednesday, 12 August 2009, 6:30 PM Location: NASA Exploration Center NASA Ames Research Center Moffett Field, CA Cost: Free and open to all who wish to attend, but membership is only $20/year. Anyone may join our mailing list at no charge, and receive announcements of upcoming events. |
Topic
References: http://www.bayardo.org/papers.html , http://www.bayardo.org/ps/vldb2009.pdf
Classification and regression tree learning on massive datasets is a common data mining task at Google, yet many state of the art tree learning algorithms require training data to reside in memory on a single machine. While more scalable implementations of tree learning have been proposed, they typically require specialized parallel computing architectures. In contrast, the majority of Google’s computing infrastructure is based on commodity hardware.
In this paper, we describe PLANET: a scalable distributed framework for learning tree models over large datasets. PLANET defines tree learning as a series of distributed computations, and implements each one using the MapReduce model of distributed computation. We show how this framework supports scalable construction of classification and regression trees, as well as ensembles of such models. We discuss the benefits and challenges of using a MapReduce compute cluster for tree learning, and demonstrate the scalability of this approach by applying it to a real world learning task from the domain of computational advertising.
About the Speaker
Josh Herbach is an engineer at Google where he works on ads
quality. Prior to joining Google in June 2008, he received his
bachelors degree in computer science from Princeton University where
he did research in clustering evaluation, electronic voting systems
and autonomous vehicles. When he isn't busy making self-driving cars
that can hack elections and run k-means, he occasionally spends his
time puzzling, backpacking, or hunting for good dim sum restaurants.