SYLLABUS

Commercial Topics in Information Systems

CS 761
Spring 2001
Dr. David A. Grossman: grossman@iit.edu

Dr. Ophir Frieder: ophir@ir.iit.edu

TA : Vamsi Krishna Tokala : tokala@ir.iit.edu

IR Research Group Web Site: http://www.ir.iit.edu
Class Web Site: http://www.ir.iit.edu/~dagr/cs761.html

Required Text:

Grossman, D. and Frieder, O. Information Retrieval: Algorithms and Heuristics, Kluwer Academic Publishers, 1999.

Grading:

The final grade will be determined as follows: 

Class Participation..................................... 20% 
Project …………..................................… 80% 

Academic Integrity:

Each member of this course bears responsibility for maintaining the highest standards of academic integrity.  All breaches of academic integrity must be reported immediately.

Date	Topics for Discussion
01/19	Database Systems: Practical Topic Overview
01/20	Text Database Processing: Practical Techniques, Integrating Structured Data and Text
01/26	Data Warehousing: Introduction
01/27	Practical Data Mining: Overview
Project:
Students will write a short term paper summarizing commercial practice in one of the areas described in the course. A survey of relevant commercial products and research efforts should be included. Students must submit their paper via e-mail April 1^st. A late paper will not be accepted. The paper should be well written and submitted in HTML. At least twenty links to relevant web sites should be included in the paper. Most good papers are typically between 10 to 20 pages but this should be viewed only as a guideline. The best papers will describe an overview of the topic discussed as well as a detailed discussion of pros and cons of products in the market. The entire paper should be in your own words. Do not simply copy content on the web and change a few phrases. Read the material, learn what it is talking about, and describe it in the context of your paper. Failure to do this will result in a grade of a zero on the project.
Late Assignment Policy:
Assignments must be submitted on or before their due date.  No late assignments will be assigned a grade.
Class Participation:
Students must actively participate in class as this is a significant portion of the grade.

Acknowledgements:

Special thanks to Carrig Emerging Technology for allowing the use of materials Dr. Grossman developed for a commercial data warehouse and data mining course.  The following sections contain materials from the Carrig course.  

Data Warehousing: Brief Introduction

Practical Data Mining: Overview