Introduction to Database Systems

Data Warehousing/Mining

1

Data Warehousing/Mining


Comp 150DW

Course Overview

Instructor: Dan Hebert

Data Warehousing/Mining

2

Comp 150


Thursday 6:50
-

9:50 PM



Instructor
-

Mr. Dan Hebert

–
email
-

dhebert@mitre.org

–
Location
-

Halligan Hall, rm. 108

Data Warehousing/Mining

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Course Description


Fundamental concepts
and techniques of data
warehousing and data mining

–
concepts, principles, architecture, design, implementation,
and application of data warehousing and data mining



Topics
:
Data warehousing and OLAP technology for
data mining, data preprocessing, data mining
primitives, languages and systems, descriptive data
mining, both characterization and comparison,
association analysis, classification and prediction,
cluster analysis, mining complex types of data, and
applications and trends in data mining

Data Warehousing/Mining

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Course Prerequisite



Comp 115
–

Introduction to RDBMS

–
Familiarity with programming with C/C++ is assumed


Students should be comfortable with:

–
relational model basics

–
relational algebra

–
SQL

–
Views

–
Security

–
conceptual database design and ER models

–
schema refinement and normal forms

–
physical database design and tuning

Data Warehousing/Mining

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Required Textbook


Data Mining Concepts and Techniques

–
Jiawei Han & Micheline Kamber

–
Morgan Kaufmann Publishers; ISBN: 1
-
55860
-
489
-
8

Data Warehousing/Mining

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Reading Schedule

Lecture Date



Topic




Reading: Text Chapter

Janu
ary 22

Introduction to Comp 150, Introduction

1

January 29

Data Warehouse and OLAP
Technology for Data Mining

2

February 5

Aggregation in SQL, Data
Warehousing Introduction, Data
Warehousing Design

Not In Book

February 19

President’s Day
Schedule Shift
–
No
Class


February 26


Data Warehouse Semanti
cs

Semistructured Data

Not In Book

March 4

Data Preprocessing

3

March 11

Data Mining Primitives, Languages,
and System Architectures
–
Midterm
Review

4

March 18

Midterm Exam



Data Warehousing/Mining

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Reading Schedule (continued)

Lecture Date



Topic




Reading: Text Chapter

April 1

Concept Description: Characterization
and C
omparison

5

April 8

Mining Association Rules in Large
Databases

6

April 15

Classification and Prediction

7

April 14

Cluster Analysis

8

April 22

Mining Complex Types of Data

9

April 29

Applications and Trends in Data
Mining
–
Final Exam Review

10

May
6

Reading Period/Project Completion


May 13


Final Exam



Data Warehousing/Mining

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Grading


Homework

30%


Project


10%


Midterm


25%


Final


35%

Data Warehousing/Mining

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Homework


Assigned weekly (each Wednesday)

–
Due at the start of lecture the following Wednesday


Late policy:


–
Homework turned in up to one week after the due date
-

20% penalty.

–
Homework turned in anytime later
-

100% penalty


Typical homework assignment

–
Exercises from the text

–
“Hands
-
on” problems that involve building data
warehouses and performing data mining


Working with PostgresQL

Data Warehousing/Mining

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Project


Develop a data warehouse and perform data
mining on it using Postgres as the
underlying datastore


Additional details provided as the course
progresses

Data Warehousing/Mining

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Midterm & Final


Open book, open notes


Opportunity during class for review of
material covered prior to midterm and final

Data Warehousing/Mining

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Computing Environment


All students will have a computer account
on psql.cs.tufts.edu

–
Account will work on all workstations in the
SUN lab


Commercial RDBMS utilized will be
PostgreSQL

–
For information
-

http://www.postgresql.org/index.html

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Course Homepage


Course web page will be available


Lectures/homework assignments will also
be posted in my account

–
~dhebert/comp150dw