Course: Big Data Analysis
Revision as of 01:15, 10 September 2012 by Juliana (talk | contribs) (→Week 2: Monday Sept. 17th - Map-Reduce)
This schedule is tentative and subject to change
Make sure to check my.poly.edu for course announcements
Week 1: Monday Sept. 10th - Course Overview
- Course overview and introduction to Big Data Analysis
- Student survey -- to be filled out today!
Required Readings
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
- PDMBS vs. MapReduce
- Benchmark DBMS vs MapReduce (2009)
Week 2: Monday Sept. 17th - Map-Reduce
- Introduction to Map-Reduce
- Introduction to [1]
- The Map-Reduce ecosystem: Pig, Hive, Jaql, Mahout, BigInsights
Required Readings
- Mining of Massive Datasets, Chapter 2
- Data-Intensive Text Processing with MapReduce, Chapter 2, 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. 24th - Databases and Big Data
- Databases and Big Data: Persistence, Querying, Indexing, Transactions
- BigTables and NoSQL stores. Tuple store vs. column stores: HBase, MongoDB, Cassandra
- Transactions in NoSQL stores. Google's percolator.
- "NewSQL" stores: more on Hive, VoltDB, HadoopDB,
- Beyond MapReduce: Berkeley's Spark, UC Irvine's Asterix, Google's Dremel
Readings
- 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 Oct. 1st - Statistics is easy - Invited Speaker: Dennis Shasha
- Guest lecture by Dennis Shasha
- Statistics and Big Data
Readings
- 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
Week 5: Monday Oct. 8st - Finding Similar Items
- Overview of information integration
Readings
Week 6: Monday Oct. 15st - Invited Speaker: Torsten Suel
- Reading: inverted index and crawling (Lin chapter 4)
- Ask Torsten (tentative, ask him for reading material)
Readings
- 1998 PageRank Paper
- Mining of Massive Datasets, Chapter 5
- Data-Intensive Text Processing with MapReduce, Chapter 5
Week 7: Monday Oct. 22st - Invited Speakers: Claudio Silva and Lauro Lins
- Introduction to Visualization; Data stewardship and provenance
- Guest lecture by Claudio Silva and Lauro Lins
Readings
- Hellerstein (ask Claudio for additional references)
- ADD: provenance and reproducibility
Week 8: Monday Oct. 29th - Graph Analysis
- Graph algorithms, link analysis, social networks
Readings
- Data-Intensive Text Processing with MapReduce, Chapter 4
Week 9: Monday Nov. 5th - Frequent Itemsets
Reading
- Mining of Massive Datasets, Chapter 6
Week 10: Monday Nov. 12th - Mining Data Streams =
Readings
- Mining of Massive Datasets, Chapter 4
Week 11: Monday Nov. 19th - Clustering
Readings
- Mining of Massive Datasets, Chapter 7
Week 12: Monday Nov. 26th - Recommendation Systems
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