Introduction and MapReduce - SNAP - Stanford University

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4 longer homeworks: 40% Theoretical and programming questions All homeworks (even if empty) must be handed in Assignments take time. Start early! How to submit? Paper: Box outside the class and in the Gates east wing We will grade on paper! You should also submit electronic copy: 1 PDF/ZIP file (writeups, experimental results, code) Submission website: http://cs246.stanford.edu/submit/ SCPD: Only submit electronic copy & send us email 7 late days for the quarter: Max 5 late days per assignment 1/8/2012 Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu 4
Short weekly quizzes: 20% Short e-quizzes on Gradiance (see course website!) First quiz is already online You have 7 days to complete it. No late days! Final exam: 40% March 19 at 8:30am It’s going to be fun and hard work 1/8/2012 Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu 5
Page 1 and 2: CS246: Mining Massive Datasets Jure
Page 3: Course website: http://cs246.stanfo
Page 7 and 8: Recitation sessions: Review of pro
Page 9 and 10: CS345a: Data mining got split into
Page 11 and 12: Much of the course will be devoted
Page 13 and 14: High-dimensional data: Locality Se
Page 16 and 17: Discovery of patterns and models th
Page 18 and 19: Scalability Dimensionality Comple
Page 20 and 21: A big data-mining risk is that you
Page 22: He told these people they had ESP a
Page 25 and 26: 20+ billion web pages x 20KB = 400+
Page 27 and 28: Large-scale computing for data mini
Page 29 and 30: Problem If nodes fail, how to stor
Page 31 and 32: Reliable distributed file system D
Page 33 and 34: Case 1: File too large for memory,
Page 35 and 36: Input key-value pairs k k k … v v
Page 37 and 38: Input: a set of key/value pairs Pr
Page 39 and 40: map(key, value): // key: document n
Page 41 and 42: MAP: reads input and produces a set
Page 43 and 44: Input and final output are stored o
Page 45 and 46: Map worker failure Map tasks compl
Page 47 and 48: Fine granularity tasks: map tasks >
Page 49 and 50: Often a map task will produce many
Page 51 and 52: 1/8/2012 Jure Leskovec, Stanford CS
Page 53 and 54: Statistical machine translation: N
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Use a hash function h from B-values
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For a map-reduce algorithm: Commun
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Total communication cost = O(|R|+|S
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Google Not available outside Googl
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Jeffrey Dean and Sanjay Ghemawat: M
Page 65 and 66:
Releases from Apache download mirro
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