Difference between revisions of "Course: Big Data Analysis"
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== Week 10: Monday Nov. 11th | == Week 10: Monday Nov. 11th - Finding Similar Items and Information Integration == | ||
* Similarity: Applications, Measures and Efficiency considerations | * Similarity: Applications, Measures and Efficiency considerations | ||
** Lecture notes: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/similarity.pdf | ** Lecture notes: http://vgc.poly.edu/~juliana/courses/cs9223/Lectures/similarity.pdf | ||
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'''Due November 17th''' | '''Due November 17th''' | ||
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. | 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- Frequent Itemsets == | == Week 11: Monday Nov 18th- Frequent Itemsets == | ||
Revision as of 19:54, 8 September 2013
Fall 2013
This schedule is tentative and subject to change
Make sure to check my.poly.edu for course announcements
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
- Student survey -- to be filled out today!
Required Reading
Additional References
- Dilbert's BigData
- New York Time's "How BigData Became so Big"
- World Economic Forum: Big Data, Big Impact
- The Analytics Journey
- BigData Analytics Usecases
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. Notes on using AWS.
- First assignment, due on Sun Sept 29th.
- Introduction to Hadoop
- The Map-Reduce ecosystem: Pig, Hive, Jaql, Mahout, BigInsights
Required Reading
- Mining of Massive Datasets, Chapter 2
- Data-Intensive Text Processing with MapReduce, Chapter 2 and Chapter 3
- original google map-reduce paper
Additional References
- Pig Latin: A Not-So-Foreign Language for Data Processing
- Jaql: A Scripting Language for Large Scale Semistructured Data Analysis
- Hive - A Warehousing Solution Over a Map-Reduce Framework
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: HBase, MongoDB, 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, [1].
- "NewSQL" stores: more on Hive, VoltDB, HadoopDB,
- Beyond MapReduce: Berkeley's Spark, UC Irvine's Asterix, Google's Dremel
Required Reading
- PDMBS vs. MapReduce
- http://cacm.acm.org/magazines/2010/1/55744-mapreduce-a-flexible-data-processing-tool/fulltext
- Parallel data processing with MapReduce: a survey. Lee et al, SIGMOD Record 2011
- Benchmark DBMS vs MapReduce (2009)
Additional References
- http://www.computerworld.com/s/article/9224180/What_s_the_big_deal_about_Hadoop_
- Bigtable: A Distributed Storage System for Structured Data
- HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads
- Low Overhead Concurrency Control for Partitioned Main Memory Databases
- ASTERIX: Towards a Scalable, Semistructured Data Platform for Evolving-World Models.
- Dremel: Interactive Analysis of Web-Scale Datasets
- Large-scale Incremental Processing Using Distributed Transactions and Notifications
Week 4: Monday Sept 30th - Query Processing on Mapreduce and High-level Languages
- Pig Latin and Query Processing:
- In-class assignment
Required Reading
- http://www.morganclaypool.com/doi/abs/10.2200/S00142ED1V01Y200807MAS001 -- book is available for free for NYU students
- Second edition of the book: http://www.morganclaypool.com/doi/pdf/10.2200/S00295ED1V01Y201009MAS008
Week 5: Monday Oct. 7th Invited Speaker: Torsten Suel
- Big Data and Information Retrieval. Invited lecture by Torsten Suel.
Week 6: Mon Oct. 14th - Fall Break - No class
Week 7: Monday Oct. 22st - Graph Algorithms
TODO
Readings
- 1998 PageRank Paper
- Data-Intensive Text Processing with MapReduce, Chapter 4 (Inverted Indexing for Text Retrieval) and 5(Graph Algorithms)
- Mining of Massive Datasets, Chapter 5 (Link Analysis)
- Pregel: A System for Large-Scale Graph Processing. Google. [2]
Week 8: Monday Oct 28th- Statistics is easy - Invited Speaker: Dennis Shasha
- Guest lecture by Dennis Shasha: Statistics is Easy
Week 9: Monday Nov 5th - EM and Text Processing
TODO
Readings
- Data-Intensive Text Processing with MapReduce, Chapter 6
Week 10: Monday Nov. 11th - Finding Similar Items and Information Integration
- Similarity: Applications, Measures and Efficiency considerations
- Similarity application: Information integration on the Web:
- Homework presentation and demo
Required Reading
Homework Assignment
Due November 17th 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- Frequent Itemsets
Required Reading
- Mining of Massive Datasets, Chapter 4
Homework Assignment
Due November 24th
Additional Reading
- Mining association rules between sets of items in large databases. Agrawal et al., SIGMOD 1993. http://delivery.acm.org/10.1145/180000/170072/p207-agrawal.pdf?ip=128.238.251.32&acc=ACTIVE%20SERVICE&CFID=198467341&CFTOKEN=23537886&__acm__=1352747519_b80a516e0f5e294b36dc021f13f55bbb
- 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://dl.acm.org/citation.cfm?id=223813
Week 12: Monday Nov. 25th - Clustering
- Lecture notes:
Homework Assignment
Due Dec 1st
Readings
- Mining of Massive Datasets, Chapter 7
- See readings for previous class
- Web Mining, by Bing Liu. http://www.cs.uic.edu/~liub/WebMiningBook.html
- Information Retrieval. http://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf
Further Readings
Week 13: Monday Dec. 2nd - Invited lecture by Enrico Bertini
- Introduction to Visual Analytics
Readings
The Value of Visualization. IEEE Visualization 2005. Jarke J. van Wijk. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.78.1138
Visualization Analysis and Design: Principles, Methods, and Practice. Tamara Munzner (Book Draft 2 from Sep. 2012). http://www.cs.ubc.ca/~tmm/courses/533-11/book/vispmp-draft.pdf
Week 14: Monday Dec. 9th - Recommendation Systems
Readings
- Ullman chapter 9
Week 15 Monday Dec. 16th - Final Exam
Other topics
Provenance
Juliana Freire and Claudio Silva. In Computing in Science and Engineering 14(4): 18-25, 2012.
Juliana Freire, David Koop, Emanuele Santos, and Claudio T. Silva. In IEEE Computing in Science & Engineering, 2008.