Class Meetings
Venue: FL-311
Date/Time: Tuesday, 6:00 pm - 8:50 pm
Teaching Staff
Professor: Murali
Mani, FL-235, x6421, mmani@cs.wpi.edu
Office Hours: Tuesdays after lecture + TBD
You are always welcome to approach the instructor with
questions. However, outside the specified office hours, you may want
to set up an appointment, rather than just dropping in, to ensure that
the instructor is around. However, if you have any problem whatsoever,
do NOT hesitate to approach the instructor. Further you are encouraged
to discuss among yourselves so as to get an excellent understanding of
the topics.
Objectives
This course is designed for students with a keen interest in database
systems research. As part of this course, you will be exposed to some
of the foundational aspects on which database systems are based.
The course will consist of 3 parts:
In Part 1 (expected about 5-6 lectures) will cover databases
from logic perspective. We will learn datalog, use a query engine (XSB),
and learn the theory and techniques for executing and optimizing recursive
queries, queries with negation etc.
In Part 2 (expected about 5-6 lectures), we will cover some
advanced topics. The coverage will be aimed at understanding a given area
within databases, and analyzing the important results in that area. The
topics covered will include (a) decorrelation of nested queries (b) overview
of data integration and specific aspects of data integration, especially
mappings and view maintenance (c) Overview of stream query processing (d)
Overview of GIS and spatial databases and data structures for them.
In Part 3 (expected about 3-4 weeks), students will present
papers. The instructor will be providing a set of papers to choose from,
and you can also discuss with the instructor if there is a specific topic
that you would like to present on outside the given list. Suggested
topics include (a) Information leakage in data publishing (b) The data
exchange problem (c) Multi-criteria based optimization (skyline queries)
(d) automatic tuning and selectivity estimation in database systems (e)
probabilistic databases.
Background Material
The course will require you to have as a pre-requisite a good
understanding of relational databases. Also we will assume that you
have a good breadth in computer science, including knowledge of
programming, algorithms, and basic graph theory.