Class meeting for 10-605 Workflows For Hadoop

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This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2017.




Also discussed

Things to Remember

  • The TFIDF representation for documents.
  • What dataflow languages are, what sort of abstract operations they use, and what the complexity of these operations is.
  • How joins are implemented in dataflow (and the difference between map-side and reduce-side joins)
  • What the PageRank algorithm is
  • Common ways of representing graphs in map-reduce system
    • A list of edges
    • A list of nodes with outlinks
  • Why iteration is often expensive in pure dataflow algorithms.
  • How Spark differs from and/or is similar to other dataflow algorithms
    • Actions/transformations
    • RDDs
    • Caching
  • Definition of a similarity join/soft join.
  • Why inverted indices make TFIDF representations useful for similarity joins
    • e.g., whether high-IDF words have shorter or longer indices, and more or less impact in a similarity measure