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Chapter 7
Database Systems
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Basic Data Management Concepts
Organizing Data in a Database
Database Management Systems
Using Database Systems in Organizations
Database Trends
Managing Databases
The Value of Databases
 Databases and Database Management Systems
(DBMS) transform large quantities of data into
specific and valuable information for accomplishing
some goal.
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Database Management
System (DBMS)
 A DBMS consists of a group of programs
that manipulate the database and provide an
interface between the database and the user
or the database and application programs.
DATABASE
Secure
Access
DBMS
Back End
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Front End
Database
A collection of data organized to meet
user’s needs.
File or Table
Field
(Attribute)
Records
(Entities)
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Database Fields
Fields are set to
hold specific types
of data.
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Database
A Database
is a
collection of
files/tables
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Database Hierarchy
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Keys and Primary Key
Key: A field in a record that is used to
identify the record
Primary key: A field that uniquely
identifies a record
A primary key field prevents duplicate
records from occurring in a table.
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Primary Keys
Which field would act as the best
primary key?
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Primary Keys
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Primary Key
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Simple but Restrictive DBMS
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The Database Approach to
Data Management
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7.2
Organizing Data in a
Database
Key Terms
• Relational Model
• Data Analysis
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The Relational Model
 In a relational database, tables are linked (related)
through common fields.
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Relation Types
 One-to-many
 Most typical
 Makes use of primary key
 One-to-one
 Many-to-many
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Data Analysis
 Data analysis is a process that involves
evaluating data to identify problems with the
content of a database.
Consider what would happen if CardNumber
were not a primary key, and two or more
customers had the same CardNumber.
 Data Integrity refers to the accuracy of the
data in a database.
GIGO, or Garbage In Garbage Out, refers to the
fact that inaccurate data entered in a database will
result in inaccurate information produced from the
database.
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7.3
Database Management
Systems
Key Terms
•
•
•
•
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Data Integrity
GIGO
Schema
Data Dictionary
SQL
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Creating a Database
 A schema is an outline of the logical and
physical structure of the data and
relationships among the data in the
database.
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Creating a Database
 A data dictionary provides a detailed
description of all data used in the database.
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Database Strengths
 The power of a database and DBMS lies in the
user’s ability to manipulate the data to turn up
useful information.
 Data can be sifted,
sorted and queried
through the use of data
manipulation languages.
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Data Manipulation Language
 A Data Manipulation Language (DML) is a
specific language provided with the DBMS
that allows people and other database users
to access, modify, and make queries about
data contained in the database, and to
generate reports.
 Structured Query Language (SQL): The
most popular DML.
 SELECT * FROM EMPLOYEE WHERE JOB_CLASSIFICATION = ‘C2”
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7.4
Using Database Systems
in Organizations
Key Terms
• Data Warehouse
• Data Mining
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The data deluge…
 The Machinery Moves on:
Moore’s law: processing “capacity” doubles every 18
months : CPU, cache, memory
It’s more aggressive cousin: Disk storage “capacity”
doubles every 9 months
 The Demand is exploding:
Every business is an eBusiness
Scientific Instruments and Moore’s law
Government
 The Internet – the ubiquity of the Web
 The Talent Shortage
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Data Stores
 Data Warehouse: A database that holds
important information from a variety of
sources.
 Data Mart: A small data warehouse, often
developed for a specific person or purpose.
 Data Mining: the process of
extracting information from a data
warehouse.
“Connecting the dots”
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Databases & Data
Warehouses
Operational
Databases
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What Is a Hypercube?
Create multi-dimensional
“cubes” of information
that summarize
transactional data across
a variety of dimensions.
OLAP vs. OLTP
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What is Data Mining?
Finding interesting structure in data
 Structure: refers to statistical patterns, predictive models,
hidden relationships
 Interesting: ?
 Examples of tasks addressed by Data Mining
Predictive Modeling (classification, regression)
Segmentation (Data Clustering )
Affinity (Summarization)
relations between fields, associations, visualization
 An Example
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Data Mining and Databases
Many interesting analysis queries are difficult to state precisely
 Examples:
which records represent fraudulent transactions?
which households are likely to prefer a Ford over a
Toyota?
Who’s a good credit risk in my customer DB?
 Yet database contains the information
good/bad customer, profitability
did/did not respond to mailout/survey/...
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Example: market basket
Transactions
{Bread, Milk}
{Bread, Diapers, Beer, Eggs}
{Milk, Diapers, Beer, Cola}
{Bread, Milk, Diapers, Beer}
{Bread, Milk, Diapers, Cola}
What pattern can you see?
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A more systematic approach: a “Decision Tree”
All 1615 patients
Split # 1: Age
Systolic BP
“terminal node”
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Visualization is Important
Factory food example from this week’s
New York Times
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The myths…
Companies have built up some large and
impressive data warehouses
Data mining is pervasive nowadays
Large corporations know how to do it
There are tools and applications that discover
valuable information in enterprise databases
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The truths…
 Data is a shambles,
most data mining efforts end up not benefiting
from existing data infra-structure
 Corporations care a lot about data, and are
obsessed with customer behavior and
understanding it
 They talk a lot about it…
 An extremely small number of businesses are
successfully mining data
 The successful efforts are “one-of”, “lucky strikes”
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