Schedule

This is a tentative schedule that will be updated throughout the course.

Paper Reviews

• October 31: Data Streaming. Sections 1-6

  • How do data stream systems differ from traditional relational databases?

  • Mention a couple of data stream applications, and explain why a RDBMS would not be able to support them.

  • Why do you think that exact query processing is very hard to achieve in the context of data streaming?

• Tuesday 11/12: Probabilistic Databases: Diamonds in the Dirt

  • Describe briefly what is a probabilistic database.

  • What are some key applications of probabilistic databases?

  • Why do you think that probabilistic databases have not been widely adopted yet?

• Tuesday 11/26: Provenance in Databases: Why, How, and Where (read only the introduction)

  • What are some applications of provenance?

  • What is the difference between why, how and where provenance?

  • Discuss the differences between eager and lazy provenance computation.

• Tuesday 12/3: A Firm Foundation for Private Data Analysis

  • Describe briefly what is differential privacy.

  • What are some of the challenges in applying differential privacy in practical settings?

Reading Material

During the first lectures, some of the material will be from the Alice Book:

• Foundations of Databases, Abiteboul, Hull, Vianu (book).

Some of the papers that we will study throughout the course:

Query Complexity

• Optimal implementation of conjunctive queries in relational databases, Chandra, Merlin, STOC 1977 (paper)

• The Complexity of Relational Query Languages, Vardi, STOC 1982 (paper)

• Algorithms for acyclic database schemes, Yannakakis, VLDB 1981.

• Size bounds and query plans for relational joins, Atserias, Grohe, Marx, FOCS 2008 (paper)

• Hypertree Decompositions and Tractable Queries, Gottlob, Leone, Scarcello, JCSS 2002 (paper)

• Leapfrog Triejoin: a worst-case optimal join algorithm, Veldhuizen, ICDT 2014 (paper)

• Skew Strikes Back: New Developments in the Theory of Join Algorithms, Ngo, Re, Rudra, SIGMOD RECORD 2013 (paper).

Datalog

• What You Always Wanted to Know About Datalog(And Never Dared to Ask), Ceri, Gottlob, Tanca, TKDE 1989 (paper)

Parallel Query Processing

• MapReduce: simplified data processing on large clusters, Dean, Ghemawat, OSDI 2004 (paper)

• MapReduce and parallel DBMSs: friends or foes?, Stonebraker et al., CACM 2010 (paper)

• Optimizing Joins in a Map-Reduce Environment, Afrati, Ullman, EDBT 2010 (paper)

• A Guide to Formal Analysis of Join Processing in Massively Parallel Systems, Koutris, Suciu, SIGMOD Record 2016 (paper)

Data Streaming

• Models and issues in data stream systems, Babcock, Babu, Datar, Motwani, Widom, PODS 2002 (paper).

• The space complexity of approximating the frequency moments, Alon, Matias, Szegedy, STOC 1996 (paper).

Uncertain Data

• Probabilistic Databases, Suciu, Olteanu, Re, Koch (book)

• Probabilistic Databases: Diamonds in the Dirt, Dalvi, Re, Suciu, CACM 2008 (paper)

• The dichotomy of probabilistic inference for unions of conjunctive queries, Dalvi, Suciu, JACM 2012 (paper)

• Consistent Query Answering: Five Easy Pieces, Chomicki, ICDT 2007 (paper)

• Consistent Query Answers in Inconsistent Databases, Arenas, Bertossi, Chomicki, PODS 1999 (paper)

Provenance

• Provenance Semirings, Green, Karvounarakis, Tannen, PODS 2007 (paper)

• Provenance in Databases: Why, How and Where, Cheney, Chiticariu, Tan, Foundations and Trends in Databases 2009 (paper)

• On Propagation of Deletions and Annotations Through Views, Buneman, Khanna, Tan, PODS 2002 (paper)

• Maximizing Conjunctive Views in Deletion Propagation, Kimefeld, Vondrak, Williams, TODS 2012 (paper)

Privacy

• On the Complexity of Optimal K-Anonymity, Meyerson, Williams, PODS 2004 (paper)

• Revealing Information while Preserving Privacy, Dinur, Nissim, PODS 2003 (paper)

• A Firm Foundation for Private Data Analysis, Dwork, CACM 2011 (paper)