Download This course provides an overview on the advanced database and

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Please indicate: New___X____
Modify_______
Delete_______
Department/Unit: _______Computer Science____ Course Subject/Number: CS780 / INFS 780_______________
Submitted by:_______Jessica Lin___________ Ext:__3-4693____ Email:__ [email protected]_________
Course Title:___ Data Mining in Multimedia Databases ________________________________________________
Effective Term (New/Modified Courses only): __Fall 08_____
Credit Hours: (Fixed) ___3_
(Var.) ______ to ______
Final Term (deleted courses only):____________
Grade Type (check one): __x___
_____
_____
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Satisfactory/No Credit only
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*Note: Used only for special topics, independent study, or internships courses
Total Number of Hours Allowed: _______
Schedule Type Code(s): 1._LEC_ LEC=Lecture SEM=Seminar STU=Studio INT=Internship IND=Independent Study
2.____ LAB=Lab RCT=Recitation (second code used only for courses with Lab or Rct component)
Prereq _X_ Coreq ___ (Check one):___INFS 755 or CS 750 or permission of instructor______________________
__________________________________________________________________________________________
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COURSE PROPOSAL
BY
THE DEPARTMENT OF
COMPUTER SCIENCE
PROPOSAL DESIGNATION
New Course Proposal
I. CATALOG DESCRIPTION
A. CS 780 (cross-listed INFS 780) – Data Mining in Multimedia Databases (3:3:0)
B. Prerequisite: INFS 755 or CS 750 or permission of instructor.
This course covers advanced algorithms for data management, learning, and mining large, multimedia
databases. Issues related to handling such data including feature selection, high dimensional indexing,
interactive search and information retrieval, pattern discovery, and scalability to large datasets are discussed.
Mining techniques and data types to be covered include texts/web, images, videos, DNA, temporal, spatial,
spatiotemporal databases, graph mining, stream mining, and data visualization.
II. JUSTIFICATION
A. Desirability of adding this course:
This course provides an overview on the advanced database and mining techniques for multimedia databases.
While some of the topics discussed here might be broad enough to deserve a course of its own, this course serves
to give students a general picture of database and data mining techniques on specialized, real-world data types
for which conventional techniques are not suitable nor sufficient. Students will learn practical knowledge that
will help them relate data mining concepts to real world scenarios.
B. Relation to other courses:
This course can be considered a companion course to INFS 785, INFS 623, CS 682 and CS 782.
III. SCHEDULING
A. This course has already been offered (with modifications) as a special topics course in Spring 2007 and
should be offered in the Fall semester 2008 and every academic year thereafter in the Fall semester.
B. The current instructor is Dr. Jessica Lin. Other possible instructors are Drs. Daniel Barbara and Carlotta
Domeniconi.
IV. SAMPLE SYLLABUS
Syllabus: CS 780/INFS 780 – Data Mining in Multimedia Databases
Course Objectives: This course covers advanced algorithms for data management, learning, and mining large,
multimedia databases. Issues related to handling such data including feature selection, high dimensional
indexing, interactive search and information retrieval, pattern discovery, and scalability to large datasets are
discussed. Mining techniques and data types to be covered include texts/web, images, videos, DNA, temporal,
spatial, spatiotemporal databases, graph mining, stream mining, and data visualization.
Topics Covered:
The course will address the following topics:
 Advanced database management concepts: hashing and multi-key access methods, for main-memory and
disk-based data.
 Text/web mining
 Video clips/surveillance
 Image mining
 Audio
 Temporal/Spatial/Spatiotemporal databases
 DNA analysis
 Stream Mining
 Data Visualization
Grading Policy:
The grade will be determined by grades obtained in individual assignments, research paper review and report, a
group research project, and class participation.





Individual Assignments: 30%
Individual Research Paper Review: 20%
Project and Presentation: 25%
Class Participation/Quizzes: 10%
Final Exam: 15%
Sample Schedule:
Class
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Textbooks:
Topics
Introduction
Advanced database indexing techniques
Text/Web Mining I
Text/Web Mining II
Image Mining I
Image Mining II
Video Mining
Audio
Temporal/Sequence Data
Temporal/Sequence Data
Spatiotemporal Data
Data Streams
Data Streams
Data Visualization
Final Exam
Required
reading:
Christos Faloutsos, Searching Multimedia Databases by Content, Kluwer Academic
Press.
Optional
reading:
Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan
Kaufmann, 2000.
Course Description
CS780/INFS780 Data Mining in Multimedia Databases (3:3:0) Prerequisite: INFS 755 or CS 750 or
permission of instructor. This course covers advanced algorithms for data management, learning, and mining
large, multimedia databases. Issues related to handling such data including feature selection, high dimensional
indexing, interactive search and information retrieval, pattern discovery, and scalability to large datasets are
discussed. Mining techniques and data types to be covered include texts/web, images, videos, DNA, temporal,
spatial, spatiotemporal databases, graph mining, stream mining, and data visualization.