Stanford CS345A: Data Mining
Handouts: 1/5: Introduction Introduction [slides] [reading] 1/7: MapReduce MapReduce [slides] [reading] 1/12: Recommendation System Recommendation System [slides] [reading] 1/14: Near Neighbor Search in High Dimensional Data Near Neighbor Search in High Dimensional Data [slides] [reading] 1/19: Locality Sensitive Hashing (LSH) Locality Sensitive Hashing (LSH) [slides] [reading] 1/21: Structure of
Mining of Massive Datasets
The book has a new Web site www.mmds.org. This page will no longer be maintained. Your browser should be automatically redirected to the new site in 10 seconds. The book has now been published by Cambridge University Press. The publisher is offering a 20% discount to anyone who buys the hardcopy Here. By agreement with the publisher, you can still download it free from this page. Cambridge Press d
Google Trends: Hadoop versus Big Data versus MapReduce — Too much information
I was just looking through some slides handed to me by Cloudera’s John Kreisa and Mike Olson during our meeting last week and one of them jumped out at me. It contains a graphic showing a Google Trends result for searches for Hadoop and “Big Data”. See for yourself why the graphic stood out: In case you hadn’t guessed, Hadoop is in blue and “Big Data” is in red. Even taken with a pinch of salt it’
Dremel: Interactive Analysis of Web-Scale Datasets - Data as a Programming Paradigm - High Scalability -
If Google was a boxer then MapReduce would be a probing right hand that sets up the massive left hook that is Dremel, Google's—scalable (thousands of CPUs, petabytes of data, trillions of rows), SQL based, columnar, interactive (results returned in seconds), ad-hoc—analytics system. If Google was a magician then MapReduce would be the shiny thing that distracts the mind while the trick goes unnoti
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Large-scale Incremental Processing Using Distributed Transactions and Notifications
