Introduction to Database Systems

townripeData Management

Jan 31, 2013 (4 years and 7 months ago)

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Data Warehousing/Mining

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Data Warehousing/Mining


Comp 150DW

Course Overview

Instructor: Dan Hebert

Data Warehousing/Mining

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Comp 150


Thursday 6:50
-

9:50 PM



Instructor
-

Mr. Dan Hebert


email
-

dhebert@mitre.org


Location
-

Halligan Hall, rm. 108

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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

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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

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Required Textbook


Data Mining Concepts and Techniques


Jiawei Han & Micheline Kamber


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

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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



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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



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Grading


Homework

30%


Project


10%


Midterm


25%


Final


35%

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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

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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

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Midterm & Final


Open book, open notes


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

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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