Introduction and MapReduce - SNAP - Stanford University

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For questions/clarifications use Piazza! If you don’t have @stanford.edu email address email us and we will register you To communicate with the course staff use cs246-win1112-staff@lists.stanford.edu We will post announcements to cs246-win1112-all@lists.stanford.edu If you are not registered or auditing send us email and we will subscribe you! You are welcome to sit-in & audit the class Send us email saying that you will be auditing 1/8/2012 Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu 10
Much of the course will be devoted to ways to data mining on the Web: Mining to discover things about the Web E.g., PageRank, finding spam sites Mining data from the Web itself E.g., analysis of click streams, similar products at Amazon, making recommendations 1/8/2012 Jure Leskovec, Stanford CS246: Mining Massive Datasets, http://cs246.stanford.edu 11
Page 1 and 2: CS246: Mining Massive Datasets Jure
Page 3 and 4: Course website: http://cs246.stanfo
Page 5 and 6: Short weekly quizzes: 20% Short e-
Page 7 and 8: Recitation sessions: Review of pro
Page 9: CS345a: Data mining got split into
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
Page 55 and 56: Use a hash function h from B-values
Page 57 and 58: For a map-reduce algorithm: Commun
Page 59 and 60: Total communication cost = O(|R|+|S
Page 61 and 62:
Google Not available outside Googl
Page 63 and 64:
Jeffrey Dean and Sanjay Ghemawat: M
Page 65 and 66:
Releases from Apache download mirro
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Introduction and MapReduce - SNAP - Stanford University

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