Difference between revisions of "Course: Massive Data Analysis 2014"

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= CS-GY 6333 Massive Data Analysis: Tentative Schedule -- ''subject to change'' =
= CS-GY 6333 Massive Data Analysis: Tentative Schedule -- ''subject to change'' =


* Course Web page: http://cs.nyu.edu/courses/spring14/CSCI-GA.2568-001/index.html
* Course Web page: http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/


* Instructor: Professor Juliana Freire (http://vgc.poly.edu/~juliana/)
* Instructor: Professor Juliana Freire (http://vgc.poly.edu/~juliana/)
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= News =
= News =


* Welcome!
* [[Massive Data Analysis 2014: Class project]]
* Aditi Nakta, our TA, will hold office hours on Tuesdays from 1 - 3 pm @ 2 MTC room 10.98D
* Your Gradiance assignment on MapReduce has been posted:  http://www.newgradiance.com/services. If you haven't registered yet, do so and use the class token 1AEF5F24. Make sure to use your official NYU email and id when you register.
* On Sept 22nd, I distributed AWS tokens that will be needed for your assignments. If you have not received your token, let me know.
* Your first assignment has been posted -- see details below and in NYU Classes.
* Instructions on how to set up your AWS account: http://www.vistrails.org/index.php/AWS_Setup
* You should get an NYU HPC account so that you can use the NYU Hadoop cluster. To submit a request for an account, follow the instructions in: https://wikis.nyu.edu/display/NYUHPC/HPC+at+NYU+-+Access. You can find instructions on how to login and use the NYU Hadoop cluster at: http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/MapReduceExample/readme-nyu-hadoop.txt


= Background (4 weeks) =
= Background (4 weeks) =


== Week 1 -- Jan 27: Course Overview; the evolution of Data Management ==
== Week 1 -- Sept 8: Course Overview; the evolution of Data Management ==


* Lecture notes:  http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/course-overview.pdf
* Lecture notes:  http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/course-overview.pdf (http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/course-overview-6p.pdf)
* Reading: Chapter 1 of Mining of Massive Data Sets (version 1.1)
* Reading: Chapter 1 of Mining of Massive Data Sets (version 1.1)
* Course survey: https://docs.google.com/spreadsheet/gform?key=0Akuw--3RI0nZdFpwTjROVzhLUWY2NVNXb0xvNTVLMnc&gridId=0#edit
* Course survey: https://docs.google.com/spreadsheet/embeddedform?formkey=dFpwTjROVzhLUWY2NVNXb0xvNTVLMnc6MA


== Week 2 -- Feb 3: Introduction to Databases ==
== Week 2 -- Sept 15: Provenance and Reproducibility ==
* Lecture notes:  http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/intro-to-db.pdf
 
* Other useful reading:  
* Lecture notes:  http://vgc.poly.edu/~fchirigati/mda-class/provenance-reproducibility.pdf
** [http://philip.greenspun.com/sql/introduction.html Greenspun's SQL for Web Nerds Intro]
* The class will have a lab component. Please bring your laptops.
** [http://philip.greenspun.com/sql/data-modeling.html SQL/Nerds Modeling (parts)]
* Before class, follow the instructions below to install and set up VisTrails as well as github
 
* VisTrails setup:
** Download VisTrails 2.1.4 from http://www.vistrails.org/index.php/Downloads and follow the installation instructions. Start the system and then quit.
** Download the following packages:
***http://vgc.poly.edu/~fchirigati/mda-class/gmaps.zip.
***http://vgc.poly.edu/~fchirigati/mda-class/tabledata-backport.zip
** After you extract the content of the zip files, place them under $HOME/.vistrails/userpackages


* Feb 6: Lab: Data Exploration and Reproducibility ==
* Github setup:
** [[Lab notes 02/06/14]]
** Create a github account (https://github.com/join)
** Learn how to set up git and create a public repository.


* Homework assignment: [[Assignment 1 - Data Exploration]]
* During class, you will add the trail of your analysis to github, and submit the link to your public github repo using this form: https://docs.google.com/forms/d/17OScN8Ea-El20AC4mHIb32S3e62mAbGEiU-BET0PyX8/viewform?usp=send_form


== Week 3 -- Feb 10: Overview: Relational Model and SQL ==
== Week 3 -- Sept 22: Introduction to Databases; Relational Model and SQL ==
* Lecture notes:   
* Lecture notes:   
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/relational-algebra.pdf
**http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/intro-to-db.pdf
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/sql-intro.pdf
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/relational-algebra.pdf
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/sql-more.pdf
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/sql-intro.pdf
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/sql-more.pdf
* Other useful reading:  
* Other useful reading:  
** [http://philip.greenspun.com/sql/introduction.html Greenspun's SQL for Web Nerds Intro]
** [http://philip.greenspun.com/sql/introduction.html Greenspun's SQL for Web Nerds Intro]
** [http://philip.greenspun.com/sql/data-modeling.html SQL/Nerds Modeling (parts)]
** [http://philip.greenspun.com/sql/data-modeling.html SQL/Nerds Modeling (parts)]


* Feb 13: Lab: Canceled -- University closed due to snow ==
* [[Assignment 1: Provenance and Data Exploration]]


== Week 4 -- Sept 29: Overview: Advanced SQL and Query Optimization  ==


== Week 3.1 -- Feb 17:  Holiday ==
* Lecture notes:   
* No class, holiday
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/xml_schema_query.pdf
* Feb 20 Lab: hands-on SQL
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/query-opt.pdf
** [[Big Data Lab notes 02/19/14]]
 
* In-class exercise: http://vistrails.org/index.php/Big_Data_Lab_SQL


== Week 4 -- Feb 24: Overview: Advanced SQL and Query Optimization  ==
= Big Data Foundations and Infrastructure (3 weeks) =


== Week 5 -- Oct 6: Cloud computing, Map Reduce and  Hadoop ==
* Lecture notes:   
* Lecture notes:   
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/xml_schema_query.pdf
** http://vgc.poly.edu/~fchirigati/mda-class/mapreduce-intro.pdf
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/query-opt.pdf
 
* Lab: after the lecture, you will work on an in-class exercise. For this you need to install Hadoop on your laptop and have your account setup on AWS. See instructions below.


* Homework assignment: [[Assignment 2 - Data Exploration using SQL]]
* You will use two different Hadoop configurations:
** Local (on your laptop)
<!--** NYU HPC will provide accounts so that you can use a local Hadoop cluster. Please submit  a request for the to create an account for you *ASAP*. Follow the instructions to obtain an HPC account in: https://wikis.nyu.edu/display/NYUHPC/HPC+at+NYU+-+Access. You can find instructions on how to login and use the NYU Hadoop cluster at: http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/MapReduceExample/readme-nyu-hadoop.txt
** Amazon AWS: each student will receive a token with $100 credit towards computing time at AWS. See http://www.vistrails.org/index.php/AWS_Setup for instructions on how to set up AWS. '''Always remember to terminate your instances! If you don't you will be charged and you are responsible for the charges beyond your credit.'''-->
** Amazon AWS: Each student should have received a token with $100 credit towards computing time at AWS. If you have not received the token yet, contact us immediately! '''When using AWS, always remember to terminate your instances! If you don't, you will be charged and you are responsible for the charges beyond your credit.'''
** See installation instructions for Hadoop on your local machine and how to setup your AWS account in http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/HadoopExerciseInstructions.pdf
** '''Warning: Install Hadoop in your machine and setup your AWS account before class starts. There will be no time for installing software during our in-class exercise.'''


= Big Data Foundations and Infrastructure (2 weeks) =
* In-Class Exercise: [[Course:_Massive_Data_Analysis_2014/Hadoop_Exercise | Hadoop Exercise]]


== Week 5 -- Mar 3: Cloud computing, Map Reduce and  Hadoop ==
* Lecture notes: 
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/mapreduce-intro.pdf


* Required reading:  
* Required reading:  
Line 68: Line 90:
** Hadoop: The Definitive Guide.  http://www.amazon.com/Hadoop-Definitive-Guide-Tom-White/dp/1449311520
** Hadoop: The Definitive Guide.  http://www.amazon.com/Hadoop-Definitive-Guide-Tom-White/dp/1449311520


* Homework Assignment -- Your first quiz is available on [http://www.newgradiance.com Gradiance]. It is ''due on March 17th at 5pm.''
== Week 6 -- Oct  13: Fall Break ==


== Week 6 -- Mar 10: Algorithm Design for MapReduce ==
== Week 7 -- Oct  20: Big Data Analysis with Myria ==


* Lecture notes:   
* Lecture notes:   
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/mapreduce-algo-design.pdf
** http://bigdata.poly.edu/~fchirigati/mda-class/dan-myria.pdf
 
* Useful reading:
** Myria Demo Paper: http://myria.cs.washington.edu/publications/Halperin_Myria_demo_SIGMOD_2014.pdf
 
== Week 7 -- Oct  27: Algorithm Design for MapReduce  ==
 
* Lecture notes: 
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/mapreduce-algo-design.pdf


* Required reading:  
* Required reading:  
Line 79: Line 109:
** Mining of Massive Datasets (2nd Edition), Chapter 2.
** Mining of Massive Datasets (2nd Edition), Chapter 2.


 
== Week 8 -- Nov 3: Parallel Databases vs MapReduce, Query Processing on Mapreduce and High-level Languages ==
= Machine Learning and Big Data  (3 weeks) =
 
== Week 7 -- Mar 23: Hashing and AllReduce ==
* Invited lecture by John Langford


* Lecture notes:
* Lecture notes:
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/langford_hashing_2014.pdf
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/paralleldb-vs-hadoop-2014.pdf
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/langford_parallel_learning_2014.pdf
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/data-analysis-mapreduce.pdf
** http://cilvr.cs.nyu.edu/diglib/lsml/lecture08-hashing.pdf
** http://cilvr.cs.nyu.edu/diglib/lsml/lecture04-allreduce.pdf
 
* Homework assignment: [[Assignment 3 - MapReduce algorithm design]]


== Week 8 -- Mar 30: Bandits ==
* Discussion about project
* Invited lecture by John Langford


* Lecture notes:
* Assignment: check Gradiance!
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/langford_interact.pdf
** http://cilvr.cs.nyu.edu/diglib/lsml/lecture10_using_exploration.pdf
** http://cilvr.cs.nyu.edu/diglib/lsml/lecture10_doing_exploration.pdf
 
== Week 9 -- Apr 7: Large Scale Machine Learning in the Real World ==
* Invited lecture by Leon Bottou
 
* Lecture notes:
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/bottou-ml-real-world.pdf
** http://cilvr.cs.nyu.edu/diglib/lsml/lecture09-ads-bottou.pdf
** http://cilvr.cs.nyu.edu/diglib/lsml/lecture11-ads-bottou.pdf
 
= Big Data Foundations and Infrastructure -- cont. (2 weeks) =
 
== Week 10 -- April 14:  Parallel Databases vs MapReduce, Query Processing on Mapreduce and High-level Languages ==
 
* Lecture notes:
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/paralleldb-vs-hadoop-2014.pdf
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/hive-pig.pdf
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/data-analysis-mapreduce.pdf


* Required reading:  
* Required reading:  
** Data-Intensive Text Processing with MapReduce (Jan 27, 2013), Chapter 6 -- Processing Relational Data (this chapter appears in the 2013 version of the textbook -- I have placed this version in http://vgc.poly.edu/~juliana/courses/BigData2014/Textbooks/MapReduce-algorithms-Jan2013-draft.pdf)
** Data-Intensive Text Processing with MapReduce (Jan 27, 2013), Chapter 6 -- Processing Relational Data (this chapter appears in the 2013 version of the textbook -- I have placed this version in http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Textbooks/MapReduce-algorithms-Jan2013-draft.pdf)
** 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
** MapReduce: A Flexible Data Processing Tool: http://cacm.acm.org/magazines/2010/1/55744-mapreduce-a-flexible-data-processing-tool/fulltext
** MapReduce: A Flexible Data Processing Tool: http://cacm.acm.org/magazines/2010/1/55744-mapreduce-a-flexible-data-processing-tool/fulltext
Line 128: Line 129:
** Hive - A Warehousing Solution Over a Map-Reduce Framework: http://www.vldb.org/pvldb/2/vldb09-938.pdf
** Hive - A Warehousing Solution Over a Map-Reduce Framework: http://www.vldb.org/pvldb/2/vldb09-938.pdf


= Big Data Algorithms and Techniques (3 weeks) =
= Big Data Algorithms, Techniques, and Visualization (3 weeks) =


== Week 11 -- April 21: Data Management for Big Data (cont) and Association Rules  ==
== Week 9 -- Nov 10: Visualization and Big Data -- Invited lecture by Dr. Huy Vo (NYU CUSP) ==


* Lecture notes:
* Lecture notes:
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/association-rules.pdf
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/vis_and_big_data_resized.pdf
 
 
== Week 10 -- Nov 17:  Visualization Techniques -- Invited lecture by Dr. Lauro Lins (AT&T Research) ==
 
* Project status report due!


* Reading: Chapter 6 [http://vgc.poly.edu/~juliana/courses/BigData2014/Textbooks/ullman-book-v1.1-mining-massive-data.pdf Mining of Massive Datasets]
* Lecture notes:
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/intro-to-visualization.pdf
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/nanocubes.pdf


* Homework Assignment -- Your  quiz is available on [http://www.newgradiance.com Gradiance]. It is ''due on April  28th.''
* Reading:
** Nanocubes for real-time exploration of spatiotemporal datasets. Lins et al. http://nanocubes.net/assets/pdf/nanocubes_paper.pdf


== Week 12 -- Apr 28: Finding similar items: Invited lecture by Dr. Harish Doraiswami ==
== Week 11 -- Nov 25 Association Rules ==


* Lecture notes:
* Lecture notes:
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/similarity.pdf
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/association-rules.pdf
 
* Assignment on frequent items and association rule mining. ''Due on Dec 7th.''  Check http://www.newgradiance.com/services


* Reading: Chapter 3 [http://vgc.poly.edu/~juliana/courses/BigData2014/Textbooks/ullman-book-v1.1-mining-massive-data.pdf Mining of Massive Datasets]  
* Reading: Chapter 6 [http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Textbooks/ullman-book-v1.1-mining-massive-data.pdf Mining of Massive Datasets]


* Homework Assignment
* Suggested additional reading:
** There are two new quizes on [http://www.newgradiance.com Gradiance] -- Distance measures and document similarity. They ''due on May  5th.''
**Fast algorithms for mining association rules, Agrawal and Srikant, VLDB 1994.
** Your final assignment is available at http://www.vistrails.org/index.php/Assignment_4_-_Querying_with_Pig_and_Mapreduce. This is an optional assignment and will count towards extra credit
**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


== Week 13 -- May 5: Graph Analysis and Exam Review ==
== Week 12 -- Dec 1: Project Updates  ==


* Lecture notes:
* Lecture notes:
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/graph-algos.pdf
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/similarity.pdf
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/exam-review.pdf


== Week 14 -- May 12: Final Exam  ==
* Reading: Chapter 3 [http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Textbooks/ullman-book-v1.1-mining-massive-data.pdf Mining of Massive Datasets]


* Quizzes on Distance Measures and Document Similarity . ''These quizzes are optional and will count as extra credit. Due on Dec 14th.''  Check http://www.newgradiance.com/services


== Week 15 -- May 19: Large-Scale Visualization -- Invited lecture by Dr. Lauro Lins (AT&T Research) ==
== Week 13 -- Dec 8: Finding Similar Items and Link Analysis ==


* Lecture notes:
* Lecture notes:
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/intro-to-visualization.pdf
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/similarity.pdf
** http://vgc.poly.edu/~juliana/courses/BigData2014/Lectures/nanocubes.pdf
** http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/graph-algos.pdf
 
* Readings:
**Chapter 3 (pages 55-79) [http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Textbooks/ullman-book-v1.1-mining-massive-data.pdf Mining of Massive Datasets]
**Chapter 5 (pages 87-106) [http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Textbooks/MapReduce-algorithms-Jan2013-draft.pdf Data-Intensive Text Processing with MapReduce]
 
== Week 13 -- Dec 10: Project Discussion ==


* Reading:
* Meeting with individual groups at 2 MTC, 10.097


The Value of Visualization, Jarke Van Wijk
== Week 14 -- Dec 15: Project Presentations  ==
http://www.win.tue.nl/~vanwijk/vov.pdf


Tamara Munzner's Book draft 2 available online
http://www.cs.ubc.ca/~tmm/courses/533/book/


Nanocubes Paper
<!--== Week 15 -- Dec 15: Project Presentations ==-->
http://nanocubes.net
http://nanocubes.net/assets/pdf/nanocubes_paper_preprint.pdf

Latest revision as of 20:58, 8 December 2014

CS-GY 6333 Massive Data Analysis: Tentative Schedule -- subject to change

  • Lecture: Mondays, 1:00pm-3:25pm at 2MTC, room 9.011.

News

Background (4 weeks)

Week 1 -- Sept 8: Course Overview; the evolution of Data Management

Week 2 -- Sept 15: Provenance and Reproducibility

  • Github setup:

Week 3 -- Sept 22: Introduction to Databases; Relational Model and SQL

Week 4 -- Sept 29: Overview: Advanced SQL and Query Optimization

Big Data Foundations and Infrastructure (3 weeks)

Week 5 -- Oct 6: Cloud computing, Map Reduce and Hadoop

  • Lab: after the lecture, you will work on an in-class exercise. For this you need to install Hadoop on your laptop and have your account setup on AWS. See instructions below.
  • You will use two different Hadoop configurations:
    • Local (on your laptop)
    • Amazon AWS: Each student should have received a token with $100 credit towards computing time at AWS. If you have not received the token yet, contact us immediately! When using AWS, always remember to terminate your instances! If you don't, you will be charged and you are responsible for the charges beyond your credit.
    • See installation instructions for Hadoop on your local machine and how to setup your AWS account in http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/HadoopExerciseInstructions.pdf
    • Warning: Install Hadoop in your machine and setup your AWS account before class starts. There will be no time for installing software during our in-class exercise.


  • Required reading:
    • Data-Intensive Text Processing with MapReduce, Chapters 1 and 2
    • Mining of Massive Datasets (2nd Edition), Chapter 2 - 2.1 and 2.2 (Large-Scale File Systems and Map-Reduce).

Week 6 -- Oct 13: Fall Break

Week 7 -- Oct 20: Big Data Analysis with Myria

Week 7 -- Oct 27: Algorithm Design for MapReduce

  • Required reading:
    • Data-Intensive Text Processing with MapReduce, Chapters 1 and 2
    • Mining of Massive Datasets (2nd Edition), Chapter 2.

Week 8 -- Nov 3: Parallel Databases vs MapReduce, Query Processing on Mapreduce and High-level Languages

  • Discussion about project
  • Assignment: check Gradiance!


Big Data Algorithms, Techniques, and Visualization (3 weeks)

Week 9 -- Nov 10: Visualization and Big Data -- Invited lecture by Dr. Huy Vo (NYU CUSP)


Week 10 -- Nov 17: Visualization Techniques -- Invited lecture by Dr. Lauro Lins (AT&T Research)

  • Project status report due!

Week 11 -- Nov 25 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

Week 12 -- Dec 1: Project Updates

Week 13 -- Dec 8: Finding Similar Items and Link Analysis

Week 13 -- Dec 10: Project Discussion

  • Meeting with individual groups at 2 MTC, 10.097

Week 14 -- Dec 15: Project Presentations

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