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Course: Big Data 2016

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(Week 7 - March 7: MapReduce Algorithm Design Patterns)
(Week 9- March 21st: Parallel Databases vs MapReduce; Storage Solutions; Introduction to SPARK)
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== Week 9- March 21st: Parallel Databases vs MapReduce; Storage Solutions; Introduction to SPARK==  
== Week 9- March 21st: Parallel Databases vs MapReduce; Storage Solutions; Introduction to SPARK==  
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*''' Lecture notes:''' ** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/paralleldb-vs-hadoop.pdf
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*''' Lecture notes:'''  
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** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/paralleldb-vs-hadoop.pdf
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**** http://vgc.poly.edu/~juliana/courses/BigData2016/Lectures/hive-pig.pdf
* '''Lab:''' NoSQL
* '''Lab:''' NoSQL
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* '''Programming assignment:''' Pig and Spark
 
* '''Readings''':  
* '''Readings''':  
** Benchmark DBMS vs MapReduce (2009): http://database.cs.brown.edu/sigmod09/benchmarks-sigmod09.pdf
** Benchmark DBMS vs MapReduce (2009): http://database.cs.brown.edu/sigmod09/benchmarks-sigmod09.pdf

Revision as of 13:27, 19 March 2016

Contents

DS-GA 1004- Big Data: Tentative Schedule -- subject to change

  • TAs:
    • Yuan Feng
    • Kevin Ye
  • Lecture: Mondays, 4:55pm-7:35pm at Silver 207
  • Some classes will include a lab session, please always bring your laptop.

News

Week 1 - Jan 25: Course Overview

Week 2 - Feb 1: The evolution of Data Management and introduction to Big Data; Introduction to Databases and Relational Model

Week 3 - Feb 8: Introduction to Databases, Relational Model and SQL (cont.)

Week 4 - Feb 15: Holiday

Transparency and Reproducibility (1 week)

Week 5 - Feb 22: Data Exploration and Reproducibility

Big Data Foundations and Infrastructure (3 weeks)

Week 6 - Feb 29: Introduction to Map Reduce

Week 7 - March 7: MapReduce Algorithm Design Patterns

Week 8-- March 14th: Spring Break

Week 9- March 21st: Parallel Databases vs MapReduce; Storage Solutions; Introduction to SPARK

Big Data Algorithms, Mining Techniques, and Visualization (6 weeks)

Week 10 - March 28th: Finding similar items

  • Homework Assignment
    • See quizzes on Gradiance -- Distance measures and document similarity.

Week 11 - April 4th: Association Rules


  • Suggested additional reading:
    • Fast algorithms for mining association rules, Agrawal and Srikant, VLDB 1994.
    • Data Mining Concepts and Techniques, Jiawei Han and Micheline Kamber, Morgan Kaufmann
    • Dynamic Itemset Counting and Implication Rules for Market Basket Data. Brin et al., SIGMOD 1997. http://www-db.stanford.edu/~sergey/dic.html
  • Homework Assignment
    • See quizes on Gradiance -- Distance measures and document similarity.

Week 12 - April 11th: Visualization and Spatio-Temporal Data -- Invited lecture by Dr. Harish Doraiswamy (NYU CDS)

Week 13 - April 18th: Data Cleaning - Invited lecture by Dr. Divesh Srivastava, AT&T Research

Week 14 - April 25th: Graph Analysis

  • Required Reading: Data-Intensive Text Processing with MapReduce. Chapters 5 -- Graph Algorithms

Week 15 - May 2: TBD

Week 16 - May 9: Final Exam

Week 17 - May 16: Project Presentations

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