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

Lecture Date Topic References Notes
1 Th 9/5 Introduction introduction
2 Tu 9/10 Conjunctive Queries Alice Book: Ch. 3, 4 lecture1
3 Th 9/12 Query Containment Alice Book: Ch. 6.2 lecture2
4 Tu 9/17 Query Containment Alice Book: Ch. 6.2
5 Th 9/19 Intro to Query Complexity lecture3
6 Tu 9/24 Acyclic Joins Alice Book: Ch. 6.4 lecture4
7 Th 9/26 Acyclic Joins
8 Tu 10/1 Query Decompositions lecture5
9 Th 10/3 Size Bouds for Joins lecture6
10 Tu 10/8 Worst-case Optimal Joins lecture7
11 Th 10/10 Datalog: semantics Alice Book: Ch. 12 lecture8
12 Tu 10/15 Datalog: evaluation Alice Book: Ch. 13.1, 13.2 lecture9
13 Th 10/17 Datalog: magic sets Alice Book: Ch. 13.3
14 Tu 10/22 Datalog: negation Alice Book: Ch. 15.1-15.3 lecture10
15 Th 10/24 Parallel Query Processing lecture11
16 Tu 10/29 Parallel Query Processing article
17 Th 10/31 Data Streaming Paper Review notes (Muthukrishnan)
18 Th 11/7 Data Streaming notes (Chakrabati)
19 Tu 11/12 Probabilistic Databases Paper Review
20 Tu 11/19 Probabilistic Databases
21 Th 11/21 Consistent Query Answering CQA for primary keys
22 Tu 11/26 Provenance Paper Review provenance
23 Tu 12/3 Differential Privacy Paper Review Differential Privacy
Th 12/5 Project Presentation
Tu 12/10 Project Presentation

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.

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).


  • 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 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)


  • 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)