Difference between revisions of "Course: Big Data Analysis"

From VistrailsWiki
Jump to navigation Jump to search
 
(124 intermediate revisions by 2 users not shown)
Line 1: Line 1:
== Fall 2013 ==
'''''The deadline for the Pagerank assignment has been extended. I have sent a notification to all students, but for some of you, the email bounced. Make sure your nyu.edu email is working.'''''
'''''This schedule is tentative and subject to change'''''
'''''Make sure to check my.poly.edu for course announcements'''''
'''''Make sure to check my.poly.edu for course announcements'''''


== Week 1: Monday Sept. 10th - Course Overview ==
== News ==
* Assignment on Mapreduce and Pig, due on Dec 1st. Please see http://my.poly.edu


* Course overview  (First day of classes!)
* Nov 7th: New quizzes have been assigned. Please see http://www.newgradiance.com/services/servlet/COTC
* Student survey
The deadline is Nov 15th. Please make sure that you have your correct name and Poly ID in your Gradiance account.
* Introduction to Big Data


=== Readings ===
* Dr. C Mohan's presentation is now available at http://bit.ly/CMnMDS
 
For frequently asked questions about the course and homework assignments, please check our [[BigDataAnalysisFAQ]].
 
== Week 1: Monday Sept. 9th - Course Overview ==
 
* Course overview and introduction to Big Data Analysis
* Lecture notes: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/intro.pdf
* [https://docs.google.com/spreadsheet/viewform?fromEmail=true&formkey=dFdHT3BST2l1TW9KeHYzYjBDaTU0V1E6MQ Student survey] -- to be filled out today!
 
=== Required Reading ===
 
* [http://i.stanford.edu/~ullman/mmds/book.pdf Mining of Massive Datasets, Chapter 1]
* [http://lintool.github.com/MapReduceAlgorithms/MapReduce-book-final.pdf Data-Intensive Text Processing with MapReduce, Chapter1]


=== Additional References ===
* [http://dilbert.com/strips/comic/2012-07-29/ Dilbert's BigData]
* [http://dilbert.com/strips/comic/2012-07-29/ Dilbert's BigData]
* [http://www.nytimes.com/2012/08/12/business/how-big-data-became-so-big-unboxed.html?ref=stevelohr New York Time's "How BigData Became so Big"]
* [http://www.nytimes.com/2012/08/12/business/how-big-data-became-so-big-unboxed.html?ref=stevelohr New York Time's "How BigData Became so Big"]
Line 14: Line 34:
* [http://www.analytics-magazine.org/november-december-2010/54-the-analytics-journey.html The Analytics Journey]
* [http://www.analytics-magazine.org/november-december-2010/54-the-analytics-journey.html The Analytics Journey]
* [http://practicalanalytics.wordpress.com/2011/12/12/big-data-analytics-use-cases/ BigData Analytics Usecases]
* [http://practicalanalytics.wordpress.com/2011/12/12/big-data-analytics-use-cases/ BigData Analytics Usecases]
* [http://lintool.github.com/MapReduceAlgorithms/MapReduce-book-final.pdf Data-Intensive Text Processing with MapReduce, Chapter1]
 
== Week 2:  Monday Sept. 16th - Map-Reduce/Hadoop ==
 
* Introduction to Map-Reduce and high-level data processing languages
* Lecture notes: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/hadoop.pdf
* Hand out AWS tokens. [http://www.vistrails.org/index.php/AWS_Setup Notes on using AWS].
* Apache [http://hadoop.apache.org/ Hadoop]
* The Map-Reduce ecosystem: [http://pig.apache.org/ Pig], [http://hive.apache.org/ Hive], [http://mahout.apache.org/ Mahout]
 
=== Assignment ===
 
* [[cs9223 Mapreduce Assignment]]
* This is an individual assignment. You may not collude with any other individual, or plagiarise their work.
For more details see http://cis.poly.edu/policies.
* You assignment is ''due on Sun Sept 29th''. '''Make sure you can login and access my.poly.edu!'''
* If you have questions about the assignment, we will hold office hours on Sept 23, 2013 from 2:30-3:30pm at 2 Metrotech, room 10.018
 
=== Required Reading ===
* [http://infolab.stanford.edu/~ullman/mmds/ch2.pdf Mining of Massive Datasets, Chapter 2]
* [http://lintool.github.com/MapReduceAlgorithms/MapReduce-book-final.pdf Data-Intensive Text Processing with MapReduce, Chapter 2 and Chapter 3]
* [http://research.google.com/archive/mapreduce.html original google map-reduce paper]
 
== Week 3: Monday Sept. 23rd - Data Management for Big Data ==
 
* Databases and Big Data: Persistence, Querying, Indexing, Transactions
* Lecture notes: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/paralleldb-vs-hadoop.pdf
 
=== Related Topics ===
* BigTables and NoSQL stores. Tuple store vs. column stores: [http://hbase.apache.org/ HBase], [http://www.mongodb.org/ MongoDB], [http://cassandra.apache.org/ Cassandra]
* HBase book HBase: The Definitive Guide. Random Access to Your Planet-Size Data: http://shop.oreilly.com/product/0636920014348.do
* HBase book. Chapter 8 Architecture for information about transactional processing, WriteAhead Log notably, and how consistency is being maintained.
* Transactions in NoSQL stores. Google's percolator, [http://research.google.com/pubs/pub36726.html].
* "NewSQL" stores: more on [http://hive.apache.org/ Hive], [http://voltdb.com/ VoltDB], [http://db.cs.yale.edu/hadoopdb/hadoopdb.html HadoopDB],
* Beyond MapReduce: [http://spark-project.org/ Berkeley's Spark], [http://asterix.ics.uci.edu/ UC Irvine's Asterix], Google's [http://code.google.com/p/dremel/ Dremel]
 
=== Required Reading ===
* [http://cacm.acm.org/magazines/2010/1/55743-mapreduce-and-parallel-dbmss-friends-or-foes/fulltext PDMBS vs. MapReduce]
* [http://cacm.acm.org/magazines/2010/1/55743-mapreduce-and-parallel-dbmss-friends-or-foes/fulltext PDMBS vs. MapReduce]
* http://cacm.acm.org/magazines/2010/1/55744-mapreduce-a-flexible-data-processing-tool/fulltext
* [http://www.cs.arizona.edu/~bkmoon/papers/sigmodrec11.pdf Parallel data processing with MapReduce: a survey. Lee et al, SIGMOD Record 2011]
* [http://database.cs.brown.edu/sigmod09/benchmarks-sigmod09.pdf Benchmark DBMS vs MapReduce (2009)]
* [http://database.cs.brown.edu/sigmod09/benchmarks-sigmod09.pdf Benchmark DBMS vs MapReduce (2009)]


== Week 2:   Monday Sept. 17th - Map-Reduce ==
=== Additional References ===
* http://www.computerworld.com/s/article/9224180/What_s_the_big_deal_about_Hadoop_
* [http://research.google.com/archive/bigtable.html Bigtable: A Distributed Storage System for Structured Data]
* [http://cs-www.cs.yale.edu/homes/dna/papers/hadoopdb.pdf HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads]
* [http://cs-www.cs.yale.edu/homes/dna/papers/hstore-cc.pdf Low Overhead Concurrency Control for Partitioned Main Memory Databases]
* [http://asterix.ics.uci.edu/pub/ASTERIX-DPD-2011.pdf ASTERIX: Towards a Scalable, Semistructured Data Platform for Evolving-World Models.]
* [http://research.google.com/pubs/pub36632.html Dremel: Interactive Analysis of Web-Scale Datasets]
* [http://research.google.com/pubs/pub36726.html Large-scale Incremental Processing Using Distributed Transactions and Notifications]
 
== Week 4: Monday Sept 30th - ''Invited lecture by Dr. C. Mohan (IBM)'' ==
 
* '''Note that we will meet at a different location: NYU CUSP, 1 Metrotech Center, 19th floor'''
 
* Tutorial: An In-Depth Look at Modern Database Systems: http://bit.ly/CMnMDS


* Introduction to map-reduce
* Abstract: This tutorial is targeted at a broad set of database systems and applications people. It is intended to let the attendees better appreciate what is really behind the covers of many of the modern database systems (e.g., NoSQL and NewSQL systems), going beyond the hype associated with these open source and commercial systems. The capabilities and limitations of such systems will be addressed. Modern extensions to decades old relational DBMSs will also be described. Some application case studies will also be presented. An outline of problems for which no adequate solutions exist will be included. Such problems could be fertile grounds for new research work.


=== Readings ===
* Presenter: Dr. C. Mohan, IBM Fellow, IBM Almaden Research Center, San Jose, CA 95120, USA.
* [http://research.google.com/archive/mapreduce.html original google map-reduce paper]
 
* [http://infolab.stanford.edu/~ullman/mmds/ch2a.pdf Mining of Massive Datasets, Chapter 2]
* Bio: Dr. C. Mohan has been an IBM researcher for 31 years in the information management area, impacting numerous IBM and non-IBM products, the research community and standards, especially with his invention of the ARIES family of locking and recovery algorithms, and the Presumed Abort commit protocol. This IBM, ACM and IEEE Fellow has also served as the IBM India Chief Scientist. In addition to receiving the ACM SIGMOD Innovation Award, the VLDB 10 Year Best Paper Award and numerous IBM awards, he has been elected to the US and Indian National Academies of Engineering, and has been named an IBM Master Inventor. This distinguished alumnus of IIT Madras received his PhD at the University of Texas at Austin. He is an inventor of 38 patents. He serves on the advisory board of IEEE Spectrum and on the IBM Software Group Architecture Board’s Council. More information can be found in his home page at http://bit.ly/CMohan
* [http://lintool.github.com/MapReduceAlgorithms/MapReduce-book-final.pdf Data-Intensive Text Processing with MapReduce, Chapter 2, Chapter 3]
 
== Week 5: Monday Oct. 7th - Query Processing on Mapreduce and High-level Languages ==
 
* Pig Latin and Query Processing:
** [http://www.vistrails.org/images/1-RelationalOnMapReduce.pdf Relational processing over MapReduce]
** [http://www.vistrails.org/images/2-PigOnMapReduce.pdf Queries over MapReduce]
* In-class assignment
 
=== Required Reading ===
 
* [http://pages.cs.brandeis.edu/~olga/cs228/Reading%20List_files/piglatin.pdf Pig Latin: A Not-So-Foreign Language for Data Processing]
 
=== Additional References ===
 
* [http://www.mpi-inf.mpg.de/~rgemulla/publications/beyer11jaql.pdf Jaql: A Scripting Language for Large Scale Semistructured Data Analysis]
* [http://www.vldb.org/pvldb/2/vldb09-938.pdf Hive - A Warehousing Solution Over a Map-Reduce Framework]
 
== Week 6:  Mon Oct. 14th - Fall Break - No class ==
 
== Week 6:  Wed Oct. 16th - Fall Break - Make-up class ==
* Reproducibility and Data Exploration: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/reproducibility.pdf
* Large-scale information integration: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/web-information-integration.pdf  
 
 
== Week 7:  Monday Oct. 21st - Invited Speaker: Alberto Lerner ==
 
* Inside MongoDB


== Week 3: Monday Sept. 24th - Statistics is easy ==
== Week 8: Monday Oct 28th- Statistics is easy - Invited Speaker: Dennis Shasha ==


* Guest lecture by [http://cs.nyu.edu/shasha/ Dennis Shasha]
* Guest lecture by [http://cs.nyu.edu/shasha/ Dennis Shasha]:  [http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/stateasy.pdf Statistics is Easy]
* Statistics and Big Data
* Introduction to Provenance


=== Readings ===
=== Required Reading ===
* http://www.morganclaypool.com/doi/abs/10.2200/S00142ED1V01Y200807MAS001 -- book is available for free for NYU students  
* http://www.morganclaypool.com/doi/abs/10.2200/S00142ED1V01Y200807MAS001 -- book is available for free for NYU students  
* JF: add references for issues related to stats and big data
* Second edition of the book: http://www.morganclaypool.com/doi/pdf/10.2200/S00295ED1V01Y201009MAS008


== Week 4:  Monday Oct. 1st - Databases and Big Data ==


* Databases and Big Data
* We will cover the material planned for "Week 10: Monday Nov. 11th": Finding Similar Items


=== Readings ===
== Week 9: Monday Nov. 4th  - Finding Similar Items, Information Integration ==
*  JF: ADD: NoSQL databases (reading papers from literature)
* Similarity: Applications, Measures and Efficiency considerations
Column store vs. tuple store. HBase, MongoDB, VaultDB, Cassandra, HadoopDB (Facebook)
** Lecture notes: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/similarity.pdf
Overview of different architectures, distributed databases vs. hadoop, transaction support...
* Similarity application: Information integration on the Web:
** Lecture notes: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/web-info-integration.pdf
* Homework presentation and demo
 
=== Required Reading ===
* [http://infolab.stanford.edu/~ullman/mmds/ch3.pdf Mining of Massive Datasets, chapter 3; information integration; entity resolution]
 
=== Homework Assignment ===
'''Due Nov 15th, 2013'''
Your assignment is in http://www.newgradiance.com/services. Please see http://vgc.poly.edu/~juliana/courses/cs9223 for instructions on how to access this service.
 
== Week 10: Monday Nov. 11th  - MapReduce Algorithm Design ==
 
* Lecture notes: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/mapreduce-indexing-graph.pdf


== Week 5: Monday Oct. 8st - Finding Similar Items ==
=== Required Reading ===
* Overview of information integration


=== Readings ===
* Chapters 3 and 4 in textbook: Data-Intensive Text Processing with MapReduce, by Lin and Dyer
* Mining of Massive Datasets, chapter 3; information integration; entity resolution


=== Homework Assignment ===
'''Due Nov 15th, 2013'''
Your assignment is in  http://www.newgradiance.com/services. Please see http://vgc.poly.edu/~juliana/courses/cs9223 for instructions on how to access this service.


== Week 6: Monday Oct. 15st - Graph Analysis ==
== Week 11: Monday Nov 18th- MapReduce Algorithm Design and Graph Processing ==  
* Lecture notes: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/mapreduce-indexing-graph.pdf


* Graph algorithms, link analysis, social networks
=== Homework Assignment ===
Your Mapreduce/Pig assignment is available from Blackboard. '''It is Due December  1st'''.


=== Readings ===
* Mining of Massive Datasets, Chapter 5
* Data-Intensive Text Processing with MapReduce, Chapter 5


=== Required Reading ===
* [http://lintool.github.com/MapReduceAlgorithms/MapReduce-book-final.pdf Data-Intensive Text Processing with MapReduce, Chapter 4 (Inverted Indexing for Text Retrieval) and 5(Graph Algorithms)]


== Week 7: Monday Oct. 22st - Introduction to Visualization; Data stewardship and provenance ==
=== Additional Reading ===
* Guest lecture by Claudio Silva and Lauro Lins
* [http://infolab.stanford.edu/pub/papers/google.pdf 1998 PageRank Paper]
* [http://infolab.stanford.edu/~ullman/mmds/ch5.pdf Mining of Massive Datasets, Chapter 5 (Link Analysis)]
* Pregel: A System for Large-Scale Graph Processing. Google. [http://kowshik.github.com/JPregel/pregel_paper.pdf]


=== Readings ===
== Week 12: Monday Nov. 25th - Large-Scale Visualization ==
* Hellerstein (ask Claudio for additional references)
* ADD: provenance and reproducibility


* Invited lectures by:
** Dr. Lauro Lins (AT&T Research)
** Dr. Huy Vo (NYU Center for Urban Science and Progress)


== Week 8: Monday Oct. 29th - TBD swap oct 15==
* Lecture notes:  
* Reading: inverted index and crawling (Lin chapter 4)
** https://www.dropbox.com/s/7t2vqryj5zgs44n/intro-to-visualization.pdf
* Ask Torsten (tentative, ask him for reading material)
** https://www.dropbox.com/s/btb3ocupkmpgefi/nanocubes.pdf


=== Readings ===
* Data-Intensive Text Processing with MapReduce, Chapter 4


=== Required Reading ===
The Value of Visualization, Jarke Van Wijk
http://www.win.tue.nl/~vanwijk/vov.pdf


== Week 9: Monday Nov. 12th - Frequent Itemsets ==
Tamara Munzner's Book draft 2 available online
http://www.cs.ubc.ca/~tmm/courses/533/book/


=== Reading ===
Nanocubes Paper
* Mining of Massive Datasets, Chapter 6
http://nanocubes.net
http://nanocubes.net/assets/pdf/nanocubes_paper_preprint.pdf


=== Additional Reading ===
imMens Paper (to contrast with nanocubes)
http://vis.stanford.edu/papers/immens


== Week 10: Monday Nov. 5th - Mining Data Streams ===


=== Readings ===
== Week 13: Monday Dec. 2nd - Frequent Itemsets ==
* Mining of Massive Datasets, Chapter 4
* Lecture notes: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/association-rules.pdf


=== Additional Reading ===
* Mining association rules between sets of items in large databases. Agrawal et al., SIGMOD 1993. http://www.almaden.ibm.com/cs/quest/papers/sigmod93.pdf
* Fast algorithms for mining association rules. Agrawal and Srikant, VLDB 1994. https://www.seas.upenn.edu/~jstoy/cis650/papers/Apriori.pdf
* An effective hash-based algorithm for mining association rules. Park et al., SIGMOD 1995. http://www.dmi.unict.it/~apulvirenti/agd/PCY95.pdf


== Week 11: Monday Nov. 19th - Clustering ==
=== Optional Quiz ===
'''Due Dec 9th'''


=== Readings ===
== Week 14: Monday Dec. 9th - - EM and exam review ==
* Mining of Massive Datasets, Chapter 7


== Week 12: Monday Nov. 26th - Recommendation Systems ==
* Lecture notes: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/hmm-em-mapreduce.pdf


=== Readings ===
=== Readings ===
* Mining of Massive Datasets, Chapter 9
== Week 13  Monday Dec. 3rd -  EM algorithms for text processing==


* Data-Intensive Text Processing with MapReduce, Chapter 6
Data-Intensive Text Processing with MapReduce, Chapter 6 (EM Algorithms for Text Processing)


== Week 14: Monday Dec. 10th - Project presentation ==
== Week 15  Monday Dec. 16th - Final Exam ==

Latest revision as of 15:23, 16 December 2013

Fall 2013

The deadline for the Pagerank assignment has been extended. I have sent a notification to all students, but for some of you, the email bounced. Make sure your nyu.edu email is working.

This schedule is tentative and subject to change

Make sure to check my.poly.edu for course announcements

News

The deadline is Nov 15th. Please make sure that you have your correct name and Poly ID in your Gradiance account.

For frequently asked questions about the course and homework assignments, please check our BigDataAnalysisFAQ.

Week 1: Monday Sept. 9th - Course Overview

Required Reading

Additional References

Week 2: Monday Sept. 16th - Map-Reduce/Hadoop

Assignment

For more details see http://cis.poly.edu/policies.

  • You assignment is due on Sun Sept 29th. Make sure you can login and access my.poly.edu!
  • If you have questions about the assignment, we will hold office hours on Sept 23, 2013 from 2:30-3:30pm at 2 Metrotech, room 10.018

Required Reading

Week 3: Monday Sept. 23rd - Data Management for Big Data

Related Topics

Required Reading

Additional References

Week 4: Monday Sept 30th - Invited lecture by Dr. C. Mohan (IBM)

  • Note that we will meet at a different location: NYU CUSP, 1 Metrotech Center, 19th floor
  • Abstract: This tutorial is targeted at a broad set of database systems and applications people. It is intended to let the attendees better appreciate what is really behind the covers of many of the modern database systems (e.g., NoSQL and NewSQL systems), going beyond the hype associated with these open source and commercial systems. The capabilities and limitations of such systems will be addressed. Modern extensions to decades old relational DBMSs will also be described. Some application case studies will also be presented. An outline of problems for which no adequate solutions exist will be included. Such problems could be fertile grounds for new research work.
  • Presenter: Dr. C. Mohan, IBM Fellow, IBM Almaden Research Center, San Jose, CA 95120, USA.
  • Bio: Dr. C. Mohan has been an IBM researcher for 31 years in the information management area, impacting numerous IBM and non-IBM products, the research community and standards, especially with his invention of the ARIES family of locking and recovery algorithms, and the Presumed Abort commit protocol. This IBM, ACM and IEEE Fellow has also served as the IBM India Chief Scientist. In addition to receiving the ACM SIGMOD Innovation Award, the VLDB 10 Year Best Paper Award and numerous IBM awards, he has been elected to the US and Indian National Academies of Engineering, and has been named an IBM Master Inventor. This distinguished alumnus of IIT Madras received his PhD at the University of Texas at Austin. He is an inventor of 38 patents. He serves on the advisory board of IEEE Spectrum and on the IBM Software Group Architecture Board’s Council. More information can be found in his home page at http://bit.ly/CMohan

Week 5: Monday Oct. 7th - Query Processing on Mapreduce and High-level Languages

Required Reading

Additional References

Week 6: Mon Oct. 14th - Fall Break - No class

Week 6: Wed Oct. 16th - Fall Break - Make-up class


Week 7: Monday Oct. 21st - Invited Speaker: Alberto Lerner

  • Inside MongoDB

Week 8: Monday Oct 28th- Statistics is easy - Invited Speaker: Dennis Shasha

Required Reading


  • We will cover the material planned for "Week 10: Monday Nov. 11th": Finding Similar Items

Week 9: Monday Nov. 4th - Finding Similar Items, Information Integration

Required Reading

Homework Assignment

Due Nov 15th, 2013 Your assignment is in http://www.newgradiance.com/services. Please see http://vgc.poly.edu/~juliana/courses/cs9223 for instructions on how to access this service.

Week 10: Monday Nov. 11th - MapReduce Algorithm Design

Required Reading

  • Chapters 3 and 4 in textbook: Data-Intensive Text Processing with MapReduce, by Lin and Dyer

Homework Assignment

Due Nov 15th, 2013 Your assignment is in http://www.newgradiance.com/services. Please see http://vgc.poly.edu/~juliana/courses/cs9223 for instructions on how to access this service.

Week 11: Monday Nov 18th- MapReduce Algorithm Design and Graph Processing

Homework Assignment

Your Mapreduce/Pig assignment is available from Blackboard. It is Due December 1st.


Required Reading

Additional Reading

Week 12: Monday Nov. 25th - Large-Scale Visualization

  • Invited lectures by:
    • Dr. Lauro Lins (AT&T Research)
    • Dr. Huy Vo (NYU Center for Urban Science and Progress)


Required 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

Additional Reading

imMens Paper (to contrast with nanocubes) http://vis.stanford.edu/papers/immens


Week 13: Monday Dec. 2nd - Frequent Itemsets

Additional Reading

Optional Quiz

Due Dec 9th

Week 14: Monday Dec. 9th - - EM and exam review

Readings

Data-Intensive Text Processing with MapReduce, Chapter 6 (EM Algorithms for Text Processing)

Week 15 Monday Dec. 16th - Final Exam

Retrieved from "https://www.vistrails.org//index.php?title=Course:_Big_Data_Analysis&oldid=6496"