home
schedule
assignments
project

Schedule

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

Lecture Date Topic References Notes
1 T 1/19 Introduction intro-slides
2 R 1/21 Conjunctive Queries Alice Book: Ch. 3, 4 lecture1
3 T 1/26 Query Containment Alice Book: Ch. 6.2 lecture2
4 R 1/28 Query Containment (cont'd)
5 T 2/2 Intro to Query Complexity lecture3
6 R 2/4 Acyclic Joins Alice Book: Ch. 6.4 lecture4
7 T 2/9 Beyond Acyclic Joins lecture5
R 2/11 NO CLASS
8 T 2/16 Size Bounds for Joins lecture6
9 R 2/18 Datalog: semantics Alice Book: Ch. 12 lecture7
10 T 2/23 Datalog: bottom-up evaluation Alice Book: Ch. 13.1 lecture8
11 R 2/25 Datalog: top-down evaluation Alice Book: Ch. 13.2, 13.3
12 T 3/1 Datalog: negation Alice Book: Ch. 15.1-15.3 lecture9
13 R 3/3 Views and Rewriting Paper Review
14 T 3/8 Parallel Query Processing slides
15 R 3/10 Parallel Query Processing lecture10
T 3/15 NO CLASS
R 3/17 NO CLASS
16 T 3/29 Data Streaming Paper Review notes (Muthukrishnan)
17 R 3/31 Data Streaming notes (Chakrabati)
18 T 4/5 Probabilistic Databases Paper Review
19 R 4/7 Probabilistic Databases
20 T 4/12 Consistent Query Answering
21 R 4/14 Consistent Query Answering CQA for primary keys
22 T 4/19 Provenance: Why Paper Review Chapter 2
23 R 4/21 Provenance: How Chapter 3
24 T 4/26 Privacy Paper Review Differential Privacy
25 R 4/28 Privacy
T 5/3 Project Presentation
R 5/5 Project Presentation

Paper Reviews

  • Thursday 3/3: Answering queries using views: A survey. Focus on sections 1,2,3

    • Give a brief summary of the problem.

    • What are some of the core applications?

    • What is the difference between certain answers and query rewriting? Why would someone use one or the other technique?

  • Tuesday 3/29: Models and Issues in Data Stream Systems.

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

    • What are some applications of data streaming?

    • Discuss some of the challenges of developing a data streaming system.

  • Tuesday 4/5: Probabilistic Databases: Diamonds in the Dirt

    • Describe briefly what is a probabilistic database.

    • What are some key applications of probabilistic databases?

    • Why is query evaluation harder for probabilistic databases?

  • Tuesday 4/19: 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).

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)

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)