This course covers techniques for analyzing very large data sets. We introduce the MapReduce programming model and the core technologies it relies on in practice, such as a distributed file system. Related approaches and technologies from distributed databases and Cloud Computing will also be introduced. Particular emphasis is placed on practical examples and hands-on programming experience. Both plain MapReduce and database-inspired advanced programming models running on top of a MapReduce infrastructure will be used.
Link to Piazza discussion forum: https://piazza.com/northeastern/fall2012/cs6240/home
Acknowledgment: This course was kindly supported by an AWS in Education Coursework Grant award from Amazon.com, Inc.
[12/11/2012] Lecture audio updated
(Future lectures and events are tentative.)
|Date||Topic||Remarks and Reading Assignments|
|Sep 11||Introduction, simple algorithms, measures of success|
|Sep 18||MapReduce, word count, equi-join, handling failures||Read the Google MapReduce paper. Look carefully at the word count and equi-join examples and make sure you can explain how the computation works.|
|Sep 25||Reverse Web graph, inverted index, sorting, Google File System||Read the relevant chapters in White's book. Read the Google File System paper.|
|Oct 2||Hadoop specifics, MapReduce Design Patterns||Read the relevant chapters in White's book. Read the appropriate sections in the Lin/Dyer book (see below). Try to re-write the word count example so that it uses the Local Aggregation design pattern.|
|Oct 9||Design Patterns||Read the appropriate sections in the Lin/Dyer book (see below). Go through the Order Inversion design pattern in detail by using an example like the relative bird color counts we discussed in class.|
|Oct 16||Design Patterns, Theta-Joins in MapReduce||Read the appropriate sections in the Lin/Dyer book (see below). For the joins, take a look at our paper.|
|Oct 23||Graph Algorithms||Read the appropriate sections in the Lin/Dyer book (see below). Create a small example graph and manually run the MapReduce programs on the example to better understand what happens in each iteration.|
|Oct 30||Graph Algorithms; Pig||Read the Pig paper and the corresponding chapter in the Tom White book.|
|Nov 6||Midterm Exam||Same time and location as lecture.|
|Nov 13||HW 2 discussion; Databases|
|Nov 20||Project and midterm discussion; Databases, HBase, and Hive; Reducing Map-to-Reduce data transfer||Read more about HBase and Hive in the books by Tom White and Lars George (see below).|
|Nov 27||Project progress presentations|
|Dec 4||MapReduce for Machine Learning; Parallel Computing Landscape||Read more about MapReduce for machine learning in this paper. LLNL has a good overview of high-performance computing and MPI.|
|Dec 11||Final project presentations|
Instructor: Mirek Riedewald
TA: Alper Okcan
Meeting times: Tue 6 - 9 PM
Meeting location: 425 Shillman Hall
CS 5800 or CS 7800, or consent of instructor
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