Yi, Ji Soo and Kang, Youn ah and Stasko, John and Jacko, Julie: Toward a Deeper Understanding of the Role of Interaction in Information Visualization, IEEE Transactions on Visualization and Computer Graphics, 2007
Dominique Brodbeck, Riccardo Mazza, Denis Lalanne: Interactive Visualization - A Survey, Book Chapter Human Machine Interaction, 2009

SV III: Lying with statistics

Topics to cover

Misleading scales
The importance of avg, mean, Percentile
Lying with maps

Readings:

Darrell Huff: How to lie with statistics, 1954
Mark Monmonier: Lying with Maps, Statistical Science, 2005

DM: Data Mining / Machine Learning

Reading for whole class:

Wu, Xindongand Kumar, Vipin and Ross Quinlan, J. and Ghosh, Joydeep and Yang, Qiang and Motoda, Hiroshi and McLachlan, Geoffrey J. and Ng, Angus and Liu, Bing and Yu, Philip S. and Zhou, Zhi-Hua and Steinbach, Michael and Hand, David J. and Steinberg, Dan: Top 10 algorithms in data mining, Knowledge and Information Systems, Volume 14, Issue 1, 2008

DM I : Decision Tree 1 (C4.5)

Topics to cover

ID3
C4.5
CART

Readings

Biswanath Panda, Joshua S. Herbach, Sugato Basu, Roberto J. Bayardo : PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce, In International Conference on Very Large Data Bases (VLDB), 2009

DM II: Clustering (K-MEANS)

Topics to cover

K-MEANS

How to choose k?
Impact of distance measure
Weaknesses

Parallel k-means
DBSCAN

Readings

Bahman Bahmani, Benjamin Moseley, Andrea Vattani, Ravi Kumar, Sergei Vassilvitskii, Scalable K-Means++, In International Conference on Very Large Data Bases (VLDB), 2012

DM III: Classification 1 (SVM)

Topics to cover

Convey intuition behind SVM
Trade-offs
Tutorial on how to use LibSVM

Readings

Chih-Chung Chang, Chih-Jen Lin, LibSVM: A Library for Support Vector Machines, Transactions on Intelligent Systems and Technology (TIST), 2013
Chih-wei Hsu , Chih-chung Chang , Chih-jen Lin, LIBSVM: A Practical Guide to Support Vector Classification, 2013
Bryan Catanzaro, Narayanan Sundaram, Kurt Keutzer, Fast Support Vector Machine Training and Classiﬁcation on Graphics Processors, Proceedings of the 25th international conference on Machine learning ICML ‘08, 2008

DM IV: Frequent Item Sets (Apriori)

Topics to cover

Applications of frequent itemsets
Association rules

Reading

Adrian Kügel, Enno Ohlebusch. A space efficient solution to the frequent string mining problem for many databases, Data mining knowledge discovery, 2008.
Han, J., Pei, J., Yin, Y.,Mao, R : Mining frequent patterns without candidate generation. A frequent-tree approach, SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000
Ramesh C. Agarwal , Charu C. Aggarwal , V. V. V. Prasad: A tree projection algorithm for generation of frequent itemsets, Journal of Parallel and Distributed Computing, 2000
Yongxin Tong, Lei Chen, Yurong Cheng, Philip S. Yu, Mining Frequent Itemsets over Uncertain Databases, Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 11, 2012

DM V: Maximum Likelihood (EM)

Readings

P.E. López-de-Teruel, J.M. García and M. Acacio, The Parallel EM Algorithm and its Applications in Computer Vision, In Proceedings of PDPTA, 1999
Henggang Cui, Jinliang Wei, Wei Dai, Parallel Implementation of Expectation-Maximization for Fast Convergence, Report, 2012

DM VI: Graph Mining (PageRank)

Topics to cover

Why is PageRank important
PageRank definition
Hadoop PageRank implementation
Pregel PageRank definition

Readings

Lawrence Page and Sergey Brin and Rajeev Motwani and Terry Winograd: The PageRank Citation Ranking: Bringing Order to the Web, 1999
Grzegorz Malewicz, Matthew H. Austern, Aart J. C. Bik, James C. Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski: Pregel: A System for Large-Scale Graph Processing, In the Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010

DM VII: AdaBoost

Topics to cover

What is boosting ?

Readings

Indranil Palit, Chandan K. Reddy, Scalable and Parallel Boosting with MapReduce: IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 10, 2012

DM VIII: Classification 2 (kNN)

Reading

Chi Zhang, Feifei Li, Jeffrey Jestes, Efﬁcient Parallel kNN Joins for Large Data in MapReduce, EDBT '12 Proceedings of the 15th International Conference on Extending Database Technology, 2012

DM IX: Classification 3 (Naive Bayes)

Reading

Carlos Ordonez, Sasi K. Pitchaimalai, Bayesian classiﬁers programmed in SQL, IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 1, 2010

DM X: Optimization: Gradient Descent

Topics to cover

Gradient Descent
Stochastic Descent
Parallel Implementation
Hogwild

Readings

Feng Niu, Benjamin Recht, Christopher Re, Stephen J. Wright: HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent, In NIPS (2011), 2011

DM XI: Evaluation

Topics to cover

Overfitting
Evaluation

Fixed
k-fold cross validaiton

Confusion matrix

Reading

Pedro Domingos, A Few Useful Things to Know about Machine Learning, Magazine Communications of the ACM, Volume 55 Issue 10, 2012

SML: Systems for machine learning

SML I: Hazy

Arun Kumar, Feng Niu, Christopher Ré: Hazy: Making it easier to build and maintain big-data analytics. Commun. ACM 56(3): 40-49, 2013
Xixuan Feng, Arun Kumar, Benjamin Recht, Christopher Ré: Towards a unified architecture for in-RDBMS analytics. SIGMOD Conference 2012: 325-336 (2012)

SML II: MAD Skills & MLbase

Joseph M. Hellerstein, Christopher Ré, Florian Schoppmann, Daisy Zhe Wang, Eugene Fratkin, Aleksander Gorajek, Kee Siong Ng, Caleb Welton, Xixuan Feng, Kun Li, Arun Kumar: The MADlib Analytics Library or MAD Skills, the SQL. PVLDB 5(12): 1700-1711 (2012)
Tim Kraska, Ameet Talwalkar, John Duchi, Rean Griffith, Michael J. Franklin, Michael Jordan, MLBase: A Distributed Machine-learning System, CIDR, Asilomar, California, 2013

SML III: Graph Processing

Topics

GraphLab Programming Model and System
Pregel Programming Model and System
Comparison

Readings:

Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin, Joseph M. Hellerstein, Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud, In International Conference on Very Large Data Bases (VLDB), 2012
Grzegorz Malewicz, Matthew H. Austern, Aart J. C. Bik, James C. Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski: Pregel: A System for Large-Scale Graph Processing, In the Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010

DI: Data Integration & CrowdSourcing

DI I: Intro to Data Integration

Topics to cover

What is data integration
Example Algorithms for

Schema Alignment
Entity Resolution (e.g., similarity)
Fusion

WebTables
Schema Extraction for Tabular Data on the Web

Readings

Luna Dong and Divesh Srivastava, Big Data Integration, Tutorial in Proceedings of the IEEE International Conference on Database Engineering (ICDE), 2013
Lise Getoor, Ashwin Machanavajjhala, Entity Resolution: Theory, Practice & Open Challenges, PVLDB 5(12): 2018-2019 (2012)
Michael J. Cafarella, Alon Y. Halevy, Daisy Zhe Wang, Eugene Wu, Yang Zhang: WebTables: exploring the power of tables on the web. PVLDB 1(1): 538-549 (2008)
Marco D. Adelfio, Hanan Samet, Schema Extraction for Tabular Data on the Web, In International Conference on Very Large Data Bases (VLDB), 2013

DI II: Data Wrangling

Topics to cover

What is data wrangling
Data Wrangler
Potters Wheel
Research challenges

Readings

Sean Kandel, Andreas Paepcke, Joseph Hellerstein, Jeffrey Heer: Wrangler: Interactive Visual Speciﬁcation of Data Transformation Scripts, CHI 2011
Sean Kandel, Jeffrey Heer, Catherine Plaisant, Jessie Kennedy, Frank van Ham, Nathalie Henry Riche, Chris Weaver, Bongshin Lee, Dominique Brodbeck, Paolo Buono, Research directions in data wrangling: Visualizations and transformations for usable and credible data, Information Visualization, 2011
Vijayshankar Raman, Joseph M. Hellerstein: Potter’s Wheel: An Interactive Data Cleaning System, VLDB 2011

DI III: CrowdSourcing Overview & Quality Control

Topics to cover

What is crowdsourcing
Quality control techniques
CrowdDB & Quark
Overview over Amazon Mechanical Turk

Readings

AnHai Doan, Michael Franklin, Donald Kossmann, Tim Kraska, Crowdsourcing Applications and Platforms: A Data Management Perspective (Tutorial), In International Conference on Very Large Data Bases (VLDB), 2013
Panagiotis G. Ipeirotis, Praveen K. Paritosh, Managing Crowdsourced Human Computation: A Tutorial, Tutorial at the 20th International World-Wide Web Conference (WWW), 2011
Panagiotis G. Ipeirotis, Foster Provost, Jing Wang, Quality Management on Amazon Mechanical Turk, KDD-HCOMP, 2010

DI IV: Entity Resolution

Topics to cover

What is Entity Resolution
Automatic ML Algorithms
CrowdSourcing Approaches

Readings

Jiannan Wang, Guoliang Li, Tim Kraska, Michael J. Franklin, Jianhua Feng, Leveraging Transitive Relations for Crowdsourced Joins, In the Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, 2013
Jiannan Wang, Tim Kraska, Michael J. Franklin, Jianhua Feng CrowdER: Crowdsourcing Entity Resolution, VLDB, Istanbul, Turkey, 2012
Steven Whang, Peter Lofgren, Hector Garcia-Molina, Question Selection for Crowd Entity Resolution, In the Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, 2013

DI V: Declarative Crowd-Sourcing

Topics to cover

TurkIT
CrowdDB
Qurk
Scoop
Comparison between systems

Readings

Greg Little, Lydia B. Chilton, Max Goldman, Robert C. Miller TurKit: Human Computation Algorithms on Mechanical Turk, UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology, 2010
Michael J. Franklin, Donald Kossmann, Tim Kraska, Sukriti Ramesh, Reynold Xin: CrowdDB: answering queries with crowdsourcing, In the Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, 2011
Adam Marcus, Eugene Wu, David Karger, Samuel Madden, Robert Miller, (Qurk) Human-Powered Sort and Joins, VLDB, Istanbul, Turkey, 2012
Aditya Parameswaran , Neoklis Polyzotis, Answering queries using humans, algorithms, and databases, CIDR, 2011
Aditya Parameswaran, Anish Das Sarma, Hector Garcia-Molina, Neoklis Polyzotis, Jennifer Widom, Human-Assisted Graph Search: It’s Okay to Ask Questions, In International Conference on Very Large Data Bases (VLDB), 2010
Hyunjung Park, Jennifer Widom, Query Optimization over Crowdsourced Data, In International Conference on Very Large Data Bases (VLDB), 2013

AF: Analytic Frameworks, Storage & Databases

AF I: Map/Reduce - Basics

Topics to cover

Map/Reduce model

Map
Reduce
Combine
Shuffle

Hadoop
Amazon Elastic MapReduce Tutorial
Critique on Map/Reduce

Readings

Jeffrey Dean and Sanjay Ghemawat, MapReduce: Simplified Data Processing on Large Clusters, In OSDI, 2004.
M. Stonebraker, D. Abadi, D. J. DeWitt, S. Madden, E. Paulson, A. Pavlo, and A. Rasin, MapReduce and Parallel DBMSs: Friends or Foes?, Communications of the ACM, vol. 53, iss. 1, pp. 64-71, 2010
A. Pavlo, E. Paulson, A. Rasin, D. J. Abadi, D. J. DeWitt, S. Madden, and M. Stonebraker, A comparison of approaches to large-scale data analysis, In SIGMOD ’09: Proceedings of the 35th SIGMOD international conference on Management of data, New York, NY, USA, 2009
http://www.umiacs.umd.edu/~jimmylin/publications/Lin_BigData2013.pdf

AF II: Map/Reduce Extensions

Topics to cover

Scientific Workloads
Iterative extension
Adaptive Indexes

Readings

Kai Ren, YongChul Kwon, Magdalena Balazinska, Bill Howe, Hadoop’s Adolescence: An Analysis of Hadoop Usage in Scientific Workloads, In International Conference on Very Large Data Bases (VLDB), 2013
Yingyi Bu, Bill Howe, Magdalena Balazinska, and Michael D. Ernst. Haloop: efficient iterative data processing on large clusters, Proc. VLDB Endow., 3:285, 296, September 2010.
Jens Dittrich, Jorge-Arnulfo Quiane-Ruiz, Stefan Richter, Stefan Schuh, Alekh Jindal, Jorg Schad, Only Aggressive Elephants are Fast Elephants, Proc. VLDB Endow., 2012.

AF III: R & Julia

Topics to cover

R/Matlab introduction
Julia (http://julialang.org/)
SciDB
SciDB R integration

Readings

Brown, Paul G., Overview of SciDB: large scale array storage, processing and analysis, In SIGMOD ’10: Proceedings of the SIGMOD international conference on Management of Data, Indianapolis, Indiana, USA., 2010
Sudipto Das, Yannis Sismanis, Kevin S. Beyer, Rainer Gemulla, Peter J. Haas, John McPherson, Ricardo: Integrating R and Hadoop, In SIGMOD ’10: Proceedings of the SIGMOD international conference on Management of Data, Indianapolis, Indiana, USA., 2010

AF IV: Languages for Hadoop

Topics to cover

Pig Latin (Tutorial + System Overview)
Hive (Tutorial + System Overview)
Differences between Pig and Hive

Readings

Christopher Olston, Benjamin Reed, Utkarsh Srivastava, Ravi Kumar, Andrew Tomkins: Pig Latin: A Not-So-Foreign Language for Data Processing, SIGMOD '08 Proceedings of the 2008 ACM SIGMOD international conference on Management of data, 2008
Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Antony, Hao Liu, Raghotham Murthy: Hive – A Petabyte Scale Data Warehouse Using Hadoop, In ICDE, 2010

AF V: Spark

Topics to cover

Intro to Spark (Tutorial)
Spark Streaming
Shark Overview (with examples)
Architecture of Spark

Readings

Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, Ion Stoica, Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing. NSDI 2012. April 2012. Best Paper Award and Honorable Mention for Community Award.
Reynold Xin, Joshua Rosen, Matei Zaharia, Michael J. Franklin, Scott Shenker, Ion Stoica, Shark: SQL and Rich Analytics at Scale. Technical Report UCB/EECS-2012-214. November 2012.
Matei Zaharia, Tathagata Das, Haoyuan Li, Scott Shenker, Ion Stoica, Discretized Streams: An Efficient and Fault-Tolerant Model for Stream Processing on Large Clusters. HotCloud 2012. June 2012.

AF VI: Scope & Reef

Topics to cover

Scope stack
Query compilation in Scope (PeriScope)
Reef

Readings

Ronnie Chaiken, Bob Jenkins, Per-Åke Larson, Bill Ramsey, Darren Shakib, Simon Weaver, Jingren Zhou: SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets, Proceedings of the VLDB Endowment Volume 1 Issue 2, 2008
Jingren Zhou, Nicolas Bruno, Ming-chuan Wu, Paul Larson, Ronnie Chaiken, Darren Shakib: SCOPE: Parallel Databases Meet MapReduce, The VLDB Journal, 2012.
Zhenyu Guo, Xuepeng Fan, Rishan Chen, Jiaxing Zhang, Hucheng Zhou, Sean McDirmid, Chang Liu, Wei Lin, Jingren Zhou, and Lidong Zhou: Spotting Code Optimizations in Data-Parallel Pipelines through Peri, in Proc. of the 2012 OSDI Conference, 2012
Byung-Gon Chun, Chris Douglas, Shravan Narayanamurthy, Josh Rosen, Tyson Condie, Sergiy Matusevych, Raghu Ramakrishnan, Russell Sears, Carlo Curino, Brandon Myers, Sriram Rao, Markus Weimer, REEF: Retainable Evaluator Execution Framework, In the Proceedings of the VLDB Endowment, Vol. 6, No. 12, 2013

AF VII: NoSQL

Topics to cover

What is NoSQL
Overview of Systems
ACID vs BASE (Eventual Consistency)
The Google Stack

GFS / Colossos
BigTable
MegaStore
Spanner

HBase

Readings

Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung, The Google File System, SOSP’03, 2003
Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, Robert E. Gruber, Bigtable: A Distributed Storage System for Structured Data, OSDI, 2006
Jason Baker, Chris Bond, James C. Corbett, JJ Furman, Andrey Khorlin, James Larson, Jean-Michel Leon, Yawei Li, Alexander Lloyd, Vadim Yushprakh: Megastore: Providing Scalable, Highly Available Storage for Interactive Services, Proceedings of the Conference on Innovative Data system Research (CIDR) (2011), pp. 223-234
James C. Corbett, Jeffrey Dean, Michael Epstein, Andrew Fikes, Christopher Frost, JJ Furman, Sanjay Ghemawat, Andrey Gubarev, Christopher Heiser, Peter Hochschild, Wilson Hsieh, Sebastian Kanthak, Eugene Kogan, Hongyi Li, Alexander Lloyd, Sergey Melnik, David Mwaura, David Nagle, Sean Quinlan, Rajesh Rao, Lindsay Rolig, Yasushi Saito, Michal Szymaniak, Christopher Taylor, Ruth Wang, and Dale Woodford: Spanner: Google's Globally-Distributed Database, In the Proceedings of OSDI'12: Tenth Symposium on Operating System Design and Implementation, 2012

AF VIII: Other NoSQL Systems

Topics to cover

Consistent Hashing
Vector Clocks
Dynamo / Cassandra
CouchDB
F1

Readings

Giuseppe DeCandia, Deniz Hastorun, Madan Jampani, Gunavardhan Kakulapati, Avinash Lakshman, Alex Pilchin, Swaminathan Sivasubramanian, Peter Vosshall, Werner Vogels: Dynamo: Amazon’s Highly Available Key-value Store, SOSP’07, 2007
Jeff Shute, Radek Vingralek, Bart Samwel, Ben Handy, Chad Whipkey, Eric Rollins, Mircea Oancea, Kyle Littleﬁeld, David Menestrina,Stephan Ellner, John Cieslewicz, Ian Rae, Traian Stancescu, Himani Apte: F1: A Distributed SQL Database That Scales, VLDB 2013

AF IX: Column Databases

Topics to cover

What are column stores
Vertica architecture
Hardware optimization

Readings

Mike Stonebraker, Daniel Abadi, Adam Batkin, Xuedong Chen, Mitch Cherniack, Miguel Ferreira, Edmond Lau, Amerson Lin, Sam Madden, Elizabeth O'Neil, Pat O'Neil, Alex Rasin, Nga Tran, Stan Zdonik: C-Store: A Column Oriented DBMS. VLDB, pages 553-564, 2005
The Vertica Analytic Database: C-Store 7 Years Later http://vldb.org/pvldb/vol5/p1790_andrewlamb_vldb2012.pdf
Max Heimel, Michael Saecker, Holger Pirk, Stefan Manegold, Volker Markl: Hardware-Oblivious Parallelism for In-Memory Column-Stores, VLDB 2013