Difference between revisions of "Course: Massive Data Analysis 2014"
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== Week 6 -- Oct 13: Fall Break == | == Week 6 -- Oct 13: Fall Break == | ||
== Week 7 -- Oct 20: Big Data Analysis with Myria == | |||
* Lecture notes: | |||
** http://bigdata.poly.edu/~fchirigati/mda-class/dan-myria.pdf | |||
== Week 7 -- Oct | * 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: | * Lecture notes: | ||
| Line 98: | Line 104: | ||
** 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 == | |||
== Week 8 -- | |||
* Lecture notes: | * Lecture notes: | ||
| Line 119: | Line 124: | ||
= Big Data Algorithms and Techniques (3 weeks) = | = Big Data Algorithms and Techniques (3 weeks) = | ||
== Week 9 -- Nov | == Week 9 -- Nov 10: Association Rules == | ||
* Lecture notes: | * Lecture notes: | ||
| Line 128: | Line 133: | ||
== Week 10 -- Nov | == Week 10 -- Nov 17: Finding similar items == | ||
* Lecture notes: | * Lecture notes: | ||
| Line 136: | Line 141: | ||
== Week 11 -- Nov | == Week 11 -- Nov 25: Graph Analysis == | ||
* Lecture notes: | * Lecture notes: | ||
| Line 142: | Line 147: | ||
== Week 12 -- | == Week 12 -- Dec 1: Large-Scale Visualization -- Invited lecture by Dr. Lauro Lins (AT&T Research) == | ||
* Lecture notes: | * Lecture notes: | ||
| Line 161: | Line 166: | ||
== Week 13 -- Dec | == Week 13 -- Dec 8: Data Cleaning and Integration == | ||
== Week 14 -- Dec | == Week 14 -- Dec 15: Project Presentations == | ||
== Week 15 -- Dec 15: Project Presentations == | <!--== Week 15 -- Dec 15: Project Presentations ==--> | ||
Revision as of 21:56, 20 October 2014
CS-GY 6333 Massive Data Analysis: Tentative Schedule -- subject to change
- Course Web page: http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/
- Instructor: Professor Juliana Freire (http://vgc.poly.edu/~juliana/)
- Lecture: Mondays, 1:00pm-3:25pm at 2MTC, room 9.011.
News
- 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.
Background (4 weeks)
Week 1 -- Sept 8: Course Overview; the evolution of Data Management
- 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)
- Course survey: https://docs.google.com/spreadsheet/embeddedform?formkey=dFpwTjROVzhLUWY2NVNXb0xvNTVLMnc6MA
Week 2 -- Sept 15: Provenance and Reproducibility
- Lecture notes: http://vgc.poly.edu/~fchirigati/mda-class/provenance-reproducibility.pdf
- The class will have a lab component. Please bring your laptops.
- 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:
- After you extract the content of the zip files, place them under $HOME/.vistrails/userpackages
- Github setup:
- Create a github account (https://github.com/join)
- Learn how to set up git and create a public repository.
- 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 -- Sept 22: Introduction to Databases; Relational Model and SQL
- Lecture notes:
- http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/intro-to-db.pdf
- http://vgc.poly.edu/~juliana/courses/MassiveDataAnalysis2014/Lectures/relational-algebra.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:
Week 4 -- Sept 29: Overview: Advanced SQL and Query Optimization
- Lecture notes:
- In-class exercise: http://vistrails.org/index.php/Big_Data_Lab_SQL
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.
- In-Class Exercise: Hadoop 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).
- Other useful reading:
- Hadoop: The Definitive Guide. http://www.amazon.com/Hadoop-Definitive-Guide-Tom-White/dp/1449311520
Week 6 -- Oct 13: Fall Break
Week 7 -- Oct 20: Big Data Analysis with Myria
- Lecture notes:
- Useful reading:
Week 7 -- Oct 27: Algorithm Design for MapReduce
- Lecture notes:
- 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
- Lecture notes:
- 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/MassiveDataAnalysis2014/Textbooks/MapReduce-algorithms-Jan2013-draft.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
- Additional reading:
- Pig Latin: A Not-So-Foreign Language for Data Processing: http://pages.cs.brandeis.edu/~olga/cs228/Reading%20List_files/piglatin.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)
Week 9 -- Nov 10: Association Rules
- Lecture notes:
- Reading: Chapter 6 Mining of Massive Datasets
Week 10 -- Nov 17: Finding similar items
- Reading: Chapter 3 Mining of Massive Datasets
Week 11 -- Nov 25: Graph Analysis
- Lecture notes:
Week 12 -- Dec 1: Large-Scale Visualization -- Invited lecture by Dr. Lauro Lins (AT&T Research)
- Lecture notes:
- Reading:
The Value of Visualization, Jarke Van Wijk 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 http://nanocubes.net http://nanocubes.net/assets/pdf/nanocubes_paper_preprint.pdf