This course introduces the latest research results
from the data management and database research. We will focus on I/O
efficient data structures and algorithms first and expand our
discussion to indexing for multidimensional data, such as the popular
R-tree and kd-Tree. Next, streaming data will be discussed with its model
and related algorithms. Then probabilistic data management will be
introduced. We will focus on the ranking and aggregate query processing
over uncertain data. Finally, we will touch the issue of security and
privacy in data management as well.
An undergraduate computer
science background is required for this class. General knowledge on
statistics and probability theory is necessary. Student expects to
learn an overview of various topics in data management and database
research, especially on the issue of scalability, efficiency and data
models. A course project, done individually, will be implemented as
well as a term paper of the student's interest.
introduce some related research tools that are required for
this class as well, examples include how to use LaTex, XFig, and
other plotting tools to produce the EPS figures.
Instructor: Feifei Li
Email lifeifei AT
and Wednesday 1:00pm-2:00pm, or
3:35pm-4:50pm on Tuesdays and Thursdays; LOVE
Recommended (But not required to buy): Randomized Algorithms
, by Rajeev Motvani and Prabhakar Raghavan. Cambridge University
Most of the course materials, including the syllabus, lecture
notes, reading assignments, homeworks, programming FAQs, etc., will be
available through the course Web page (http://www.cs.fsu.edu/~lifeifei/cis5930/).
Tentaive Syllabus in PDF format:
There are four to five homeworks. There is
a course project (done individually) and a term paper.Details will be available in