Transcript Databases

Conventional Files Versus the Database

Introduction

All information systems create, read, update and delete data. This
data is stored in files and databases.
 Files are collections of similar records.
 Databases are collections of interrelated files.
• The key word is interrelated.
• The records in each file must allow for relationships (think of them
as ‘pointers’) to the records in other files.


In the file environment, data storage is built around the
applications that will use the files.
In the database environment, applications will be built around the
integrated database.
Information
System
File
File
Information
System
File
Information
System
Database
(consolidated &
integrated data
from files)
File
Information
System
Information
System
Conventional Files Versus the Database

The Pros and Cons of Database

Pros:
 The principal advantage of a database is the ability to share the
same data across multiple applications and systems.
 Database technology offers the advantage of storing data in
flexible formats.
 Databases allow the use of the data in ways not originally
specified by the end-users - data independence.
 The database scope can even be extended without impacting
existing programs that use it.
• New fields and record types can be added to the database without
affecting current programs.
Conventional Files Versus the Database

The Pros and Cons of Database

Cons:
 Database technology is more complex than file technology.
• Special software, called a database management system (DBMS),
is required.
A DBMS is still somewhat slower than file technology.
 Database technology requires a significant investment.

• The cost of developing databases is higher because analysts and
programmers must learn how to use the DBMS.
In order to achieve the benefits of database technology, analysts
and database specialists must adhere to rigorous design
principles.
 Another potential problem with the database approach is the
increased vulnerability inherent in the use of shared data.

Conventional Files Versus the Database

Database Design in Perspective




To fully exploit the advantages of database technology, a database
must be carefully designed.
The end product is called a database schema, a technical blueprint
of the database.
Database design translates the data models that were developed for
the system users during the definition phase, into data structures
supported by the chosen database technology.
Subsequent to database design, system builders will construct
those data structures using the language and tools of the chosen
database technology.
INFORMATION SYSTEMS FRAMEWORK
FOCUS ON
SYSTEM
DATA
FOCUS ON
SYSTEM
PROCESSES
FOCUS ON
SYSTEM
INTERFACES
FOCUS ON
SYSTEM
GEOGRAPHY
Business Subjects
SYSTEM
OWNERS
(scope)
Survey Phase
(establish scope and
project plan)
Custom ers order zero,
one, or m ore products.
Products m ay be ordered
by zero, one, or m ore
custom ers.
Study Phase
(establish
system
improvement
objectives)
Data Requirements
S
Y
S
T
E
M
A
N
A
L
Y
S
T
S
SYSTEM
USERS
(requirements)
PRODUCT
product-no
product-name
unit-of-measure
unit-price
quantity-available
CUSTOMER
customer-no
customer-name
customer-rating
balance-due
ORDER
order-no
order-date
products-ordered
quantities-ordered
Definition Phase
(establish and
prioritize
business system
requirements)
data models
Database Schema
SYSTEM
DESIGNERS
(specification)
PRODUCT
CUSTOMER
product_no [Alpha(10)] INDEX
customer_no [Alpha (10)] INDEX
product_name [Alpha(32)]
customer_name [Alpha(32)]
unit_of_measure [Alpha(2)]
customer_rating [Alpha(1)] INDEX
unit_pri ce [Real(3,2)]
balance_due [Real(5,2)]
quantity_available [Integ er(4)]
ORDER_PRODUCT
ORDER
order_no [Alpha(12)] INDEX ORDER. order_no
order_date [Date(mmddyyyy) PRODUCT.product_no
quantity_ordered [Integ er(2)
CUSTOMER.customer_n o
Design Phase
(translate business
requirements into a
technical design)
Database Programs
SYSTEM
BUILDERS
(components)
FAST
Methodology
CREATE TABLE CUSTOMER
(customer_no CHAR(10) NOT NULL
customer_name CHAR(32) NOT N ULL
customer _rating CHAR (1) NOT NU LL
balance_due DECIMAL(5,2)
CREATE INDEX cust_no_idx on CUSTOMER
CREATE INDEX cust_rt_idx on CUSTOMER
Existing
Databases
and
Technology
Existing
Applications
and
Technology
Existing
Interfaces
and
Technology
Implementation
Phase
(translate technical
design into code)
Existing
Networks
and
Technology
Database Concepts

Databases



Databases provide for the technical implementation of entities and
relationships.
The history of information systems has led to one inescapable
conclusion:
 Data is a resource that must be controlled and managed!
Out of necessity, database technology was created so an
organization could maintain and use its data as an integrated whole
instead of as separate data files.
Users and
Programmers
Information System
File
A legacy
file-based
information
system
Users and
Programmers
File
(built in-house)
Information
System
Information
System
Operational
Database
(built
in-house)
(built
in-house)
(built
in-house)
File
End-User
Tools
File
Data
Warehouse
End-User
Applications
File
A legacy
file-based
information
system
Users and
Programmers
Users
Personal
DB
File
(purchased)
File
Operational
Database
Information
System
(purchased)
Work-Group
Database
End-User
Work Group
Users and
Programmers
Database Concepts

Databases

Database Architecture:
 Database architecture refers to the database technology
including the database engine, database management utilities,
database CASE tools for analysis and design, and database
application development tools.
 The control center of a database architecture is its database
management system.
• A database management system (DBMS) is specialized
computer software available from computer vendors that is used to
create, access, control, and manage the database. The core of the
DBMS is often called its database engine. The engine responds to
specific commands to create database structures, and then to
create, read, update, and delete records in the database.
Database Concepts

Databases

Database Architecture:
 A systems analyst, or database analyst, designs the structure of
the data in terms of record types, fields contained in those
record types, and relationships that exist between record types.
 These structures are defined to the database management
system using its data definition language.
• Data definition language (or DDL) is used by the DBMS to
physically establish those record types, fields, and structural
relationships. Additionally, the DDL defines views of the database.
Views restrict the portion of a database that may be used or
accessed by different users and programs. DDLs record the
definitions in a permanent data repository.
Programmers
Systems Analysts
and/or
Database Designers
End Users
Host-based
Transaction
Processing
Monitor
(optional)
Data
Manipulation
Language
DML
Data
Definition
Language
DDL
Internal
TP Monitor
(opt)
Proprietary Data
Manipulation
Language and/or
Report Writers
Database Management System (DBMS)
Stored Data
Metadata
Database Concepts

Databases

Database Architecture:
 Some data dictionaries include formal, elaborate software that
helps database specialists track metadata – the data about the
data –such as record and field definitions, synonyms, data
relationships, validation rules, help messages, and so forth.
 The database management system also provides a data
manipulation language to access and use the database in
applications.
• A data manipulation language (or DML) is used to create, read,
update, and delete records in the database, and to navigate between
different records and types of records. The DBMS and DML hide
the details concerning how records are organized and allocated to
the disk.
Database Concepts

Databases

Database Architecture:
 Many DBMSs don’t require the use of a DDL to construct the
database, or a DML to access the database.
• They provide their own tools and commands to perform those
tasks. This is especially true of PC-based DBMSs.
Many DBMSs also include proprietary report writing and
inquiry tools to allow users to access and format data without
directly using the DML.
 Some DBMSs include a transaction processing monitor (or
TP monitor) that manages on-line accesses to the database, and
ensures that transactions that impact multiple tables are fully
processed as a single unit.

Database Concepts

Databases

Relational Database Management Systems:
 There are several types of database management systems and
they can be classified according to the way they structure
records.
 Early database management systems organized records in
hierarchies or networks implemented with indexes and linked
lists.
 Relational databases implement data in a series of tables that
are ‘related’ to one another via foreign keys.
• Files are seen as simple two-dimensional tables, also known as
relations.
• The rows are records.
• The columns correspond to fields.
Customer
places
Order
sells
Ordered
Product
sold on
Product
Customers Table
Customer Number
Customer Name
10112
10113
10114
10117
Luck Star
Pemrose
Hartman
K-Jack Industries
Customer
Balance
…
1455.77
12.14
0.00
- 20.00
Orders
Table
Order
Number
Customer Number
(foreign key)
A633
A634
A635
10112
10114
10112
…
Ordered Products Table
Order
Number
(foreign
key)
Product Number
(foreign key)
Quantity
Ordered
A633
A633
A634
A634
A635
A635
77F02
77B12
77B13
77F01
77B12
77B15
1
500
100
5
300
15
…
Products Table
Product Number
Product Description
Quantity
in Stock
77B12
77B13
77B15
77F01
77F02
Widget
Widget
Widget
Gadget
Gadget
8000
0
52
20
2
…
Database Concepts for the Systems Analyst

Databases

Relational Database Management Systems:
 Both the DDL and DML of most relational databases is called
SQL (which stands for Structured Query Language).
• SQL supports not only queries, but complete database creation and
maintenance.
• A fundamental characteristic of relational SQL is that commands
return ‘a set’ of records, not necessarily just a single record (as in
non-relational database and file technology).
Database Concepts for the Systems Analyst

Databases

Relational Database Management Systems:
 High-end relational databases also extend the SQL language to
support triggers and stored procedures.
• Triggers are programs embedded within a table that are
automatically invoked by a updates to another table.
• Stored procedures are programs embedded within a table that can
be called from an application program.

Both triggers and stored procedures are reusable because they
are stored with the tables themselves.
• This eliminates the need for application programmers to create the
equivalent logic within each application that use the tables.
Data Analysis for Database Design

What is a Good Data Model?



A good data model is simple.
 As a general rule, the data attributes that describe an entity
should describe only that entity.
A good data model is essentially non-redundant.
 This means that each data attribute, other than foreign keys,
describes at most one entity.
A good data model should be flexible and adaptable to future
needs.
 We should make the data models as application-independent as
possible to encourage database structures that can be extended
or modified without impact to current programs.
Data Analysis for Database Design

Data Analysis

Data analysis is a process that prepares a data model for
implementation as a simple, non-redundant, flexible, and adaptable
database. The specific technique is called normalization.
• Normalization is a technique that organizes data attributes such
that they are grouped together to form stable, flexible, and adaptive
entities.
Data Analysis for Database Design

Data Analysis

Normalization is a three-step technique that places the data model
into first normal form, second normal form, and third normal form.
 An entity is in first normal form (1NF) if there are no
attributes that can have more than one value for a single
instance of the entity.
 An entity is in second normal form (2NF) if it is already in
1NF, and if the values of all non-primary key attributes are
dependent on the full primary key – not just part of it.
 An entity is in third normal form (3NF) if it is already in 2NF,
and if the values of its non-primary key attributes are not
dependent on any other non-primary key attributes.
Data Analysis for Database Design

Normalization Example

First Normal Form:
 The first step in data analysis is to place each entity into 1NF.
sold
PRODUCT
------------Key Data---------------Product-Number (PK1)
Universal-Product-Code (PK2)
--------Non-Key Data------------Quantity-in-Stock
Product-Type
Suggested-Retail-Price
Club-Default-Unit-Price
Current-Special-Unit-Price
Current-Month-Units-Sold
Current-Year-Units-Sold
Total-Lifetime-Units-Sold
MEMBER ORDER
------------------Key Data--------------------Order-Number (PK)
----------------Non-Key Data----------------Order-Creation-Date
Order-Automatic-Fill-Date
Member Number (FK1)
Member-Name
Member-Address
Shipping-Address
Shipping Instructions
Club-Name (FK2)
Promotion-Number (FK2)
0 { Ordered-Product-Description } n
0 { Ordered-Product-Title } n
1 { Quantity-Ordered } n
1 { Purchased-Unit-Price } n
1 { Extended-Price } n
Order-Sub-Total-Cost
Order-Sales-Tax
Ship-Via-Method
Shipping-Charge
Order-Status
Prepaid-Amount
Method-of-Payment
placed
MEMBER
---------------------Key Data---------------------Member-Number (PK1)
------------------Non-Key Data------------------Member-Name
Member-Status
Member-Street-Address
Member-Daytime-Phone-Number
Date-of-Last-Order
Member-Balance-Due
Member-Bonus-Balance-Available
Member-Credit-Card-Information
1 { Club-Name } n
1 { Agreement-Number } n
1 { Taste Code } n
1 { Media Preference } n
1 { Date-Enrolled } n
1 { Expiration-Date } n
1 { Number-of-Credits-Required } n
1 { Number of Credits-Earned } n
enrolls in
CLUB
------------------Key Data---------------------Club-Name (PK)
--------------Non-Key Data-------------------Club-Description
Club-Charter-Date
1 { Agreement-Number } n
1 { Agreement-Active-Date } n
1 { Agreement-Expiration-Date } n
1 { Obligation-Period } n
1 { Required-Number-of-Credits } n
1 { Bonus-Credits-After-Obligation } n
sponsors
is a
generates
MERCHANDISE
-------------Key Data--------------Product-Number (PK1)
Universal-Product-Code (PK1)
---------Non-Key Data-----------Merchandise-Name
Merchandise-Description
Merchandise-Size
Merchasnise-Color
Unit-of-Measure
TITLE
--------------Key Data-------------Product-Number (PK1)
Universal-Product-Code (PK2)
----------Non-Key Data----------Title-of-Work
Title-Cover
Catalog-Description
Copyright-Date
Entertainment-Category
Credit-Value
features
is a
AUDIO TITLE
-------------Key Data--------------Product-Number (PK1)
Universal-Product-Code (PK1)
---------Non-Key Data-----------Artist
Audio-Category
Audio-Sub-Category
Number-of-Units-in-Package
Audio-Media-Code
Content-Advisory-Code
VIDEO TITLE
-------------Key Data--------------Product-Number (PK1)
Universal-Product-Code (PK1)
---------Non-Key Data-----------Producer
Director
Video-Category
Video-Sub-Category
Closed-Captioned
Language
Running-Time
Video-media-Type
Video-Encoding
Screen-Aspect
MPA-Rating-Code
GAME TITLE
-------------Key Data--------------Product-Number (PK1)
Universal-Product-Code (PK1)
---------Non-Key Data-----------Manufacturer
Game-Category
Game-Sub-Category
Game-Platform
Game-Media-Type
Number-of-Players
Parent-Advisory-Code
PROMOTION
---------Key Data------------Club-Name (PK1)
Promotion-Number (PK1)
-------Non-Key Data-------Product-Number (FK1)
Promotion-Release-Date
Promotion-Status
Promotion-Type
Automatic-Fill-Delay
MEMBER ORDER (1NF)
------------------Key Data--------------------Order-Number (PK)
----------------Non-Key Data----------------Order-Creation-Date
Order-Automatic-Fill-Date
Member Number (FK1)
Member-Name
Member-Address
Shipping-Address
Shipping Instructions
Club-Name (FK2)
Order-Sub-Total-Cost
Order-Sales-Tax
Ship-Via-Method
Shipping-Charge
Order-Status
Prepaid-Amount
MEMBER ORDER (unnormalized)
------------------KeyData--------------------Order-Number (PK)
---------------Non-Key Data----------------Order-Creation-Date
Order-Automatic-Fill-Date
Member Number (FK1)
Member-Name
Member-Address
Shipping-Address
Shipping Instructions
Club-Name (FK2)
Promotion-Number (FK2)
0 { Ordered-Product-Description } n
0 { Ordered-Product-Title } n
1 { Quantity-Ordered } n
1 { Purchased-Unit-Price } n
1 { Extended-Price } n
Order-Sub-Total-Cost
Order-Sales-Tax
Ship-Via-Method
Shipping-Charge
Order-Status
Prepaid-Amount
Method-of-Payment
sells
CORRECTION
MEMBER ORDERED PRODUCT (1NF)
---------------Key Data-----------------Member-Number (PK1) (FK)
Product-Number (PK1) (FK)
-------------Non-Key Data------------Ordered-Product-Description
Ordered-Product-Title
Quantity-Ordered
Purchased-Unit-Price
Extended-Price
sold as
PRODUCT (1NF)
------------Key Data---------------Product-Number (PK1)
Universal-Product-Code (PK2)
--------Non-Key Data------------Quantity-in-Stock
Product-Type
Suggested-Retail-Price
Club-Default-Unit-Price
Current-Special-Unit-Price
Current-Month-Units-Sold
Current-Year-Units-Sold
Total-Lifetime-Units-Sold
CLUB (1NF)
------------------Key Data---------------------Club-Name (PK)
--------------Non-Key Data-------------------Club-Description
Club-Charter-Date
establishes
CLUB (unnormalized)
------------------Key Data---------------------Club-Name (PK)
--------------Non-Key Data-------------------Club-Description
Club-Charter-Date
1 { Agreement-Number } n
1 { Agreement-Active-Date } n
1 { Agreement-Expiration-Date } n
1 { Obligation-Period } n
1 { Required-Number-of-Credits } n
1 { Bonus-Credits-After-Obligation } n
CORRECTION
AGREEMENT (1NF)
----------Key Data----------------Club-Name (PK1) (FK)
Agreement-Number (PK1)
--------Non-Key Data------------Agreement-Active-Date
Agreement-Expiration-Date
Obligation-Period
Required-Number-of-Credits
Bonus-Credits-After-Obligation
MEMBER (1NF)
---------------------Key Data---------------------Member-Number (PK1)
------------------Non-Key Data------------------Member-Name
Member-Status
Member-Street-Address
Member-Daytime-Phone-Number
Date-of-Last-Order
Member-Balance-Due
Member-Bonus-Balance-Available
Member-Credit-Card-Information
MEMBER (unnormalized)
---------------------Key Data---------------------Member-Number (PK1)
------------------Non-Key Data------------------Member-Name
Member-Status
Member-Address
Member-Daytime-Phone-Number
Date-of-Last-Order
Member-Balance-Due
Member-Bonus-Balance-Available
Member-Credit-Card-Information
1 { Club-Name } n
1 { Agreement-Number } n
1 { Taste Code } n
1 { Media Preference } n
1 { Date-Enrolled } n
1 { Expiration-Date } n
1 { Number-of-Credits-Required } n
1 { Number of Credits-Earned } n
enrolls in
CORRECTION
CLUB MEMBERSHIP (1NF)
-------------Key Data-------------Member-Number (PK1) (FK)
Club-Name (PK1) (FK)
Agreement-Number (PK1) (FK)
---------Non-Key Data----------Taste Code
Media Preference
Date-Enrolled
Expiration-Date
Number-of-Credits-Required
Number of Credits-Earned
binds
AGREEMENT (1NF)
----------Key Data----------------Club-Name (PK1) (FK)
Agreement-Number (PK1)
--------Non-Key Data------------Agreement-Active-Date
Agreement-Expiration-Date
Obligation-Period
Required-Number-of-Credits
Bonus-Credits-After-Obligation
establishes
CLUB (1NF)
------------------Key Data---------------------Club-Name (PK)
--------------Non-Key Data-------------------Club-Description
Club-Charter-Date
sponsors
Data Analysis for Database Design

Normalization Example

Second Normal Form:
 The next step of data analysis is to place the entities into 2NF.
• It is assumed that you have already placed all entities into 1NF.
• 2NF looks for an anomaly called a partial dependency, meaning an
attribute(s) whose value is determined by only part of the primary
key.
• Entities that have a single attribute primary key are already in 2NF.
• Only those entities that have a concatenated key need to be
checked.
MEMBER ORDERED PRODUCT (1NF)
---------------Key Data-----------------Member-Number (PK1) (FK)
Product-Number (PK1) (FK)
-------------Non-Key Data------------Ordered-Product-Description
Ordered-Product-Title
Quantity-Ordered
Purchased-Unit-Price
Extended-Price
CORRECTION
MEMBER ORDERED PRODUCT (2NF)
---------------Key Data-----------------Member-Number (PK1) (FK)
Product-Number (PK1) (FK)
-------------Non-Key Data------------Quantity-Ordered
Purchased-Unit-Price
Extended-Price
sold as
PRODUCT (2NF)
------------Key Data---------------Product-Number (PK1)
Universal-Product-Code (PK2)
--------Non-Key Data------------Quantity-in-Stock
Product-Type
Suggested-Retail-Price
Club-Default-Unit-Price
Current-Special-Unit-Price
Current-Month-Units-Sold
Current-Year-Units-Sold
Total-Lifetime-Units-Sold
is a
MERCHANDISE (2NF)
-------------Key Data--------------Product-Number (PK1)
Universal-Product-Code (PK1)
---------Non-Key Data-----------Merchandise-Name
Merchandise-Description
Merchandise-Size
Merchasnise-Color
Unit-of-Measure
TITLE (2NF)
--------------Key Data-------------Product-Number (PK1)
Universal-Product-Code (PK2)
----------Non-Key Data----------Title-of-Work
Title-Cover
Catalog-Description
Copyright-Date
Entertainment-Category
Credit-Value
Data Analysis for Database Design

Normalization Example

Third Normal Form:
 Entities are assumed to be in 2NF before beginning 3NF
analysis.
 Third normal form analysis looks for two types of problems,
derived data and transitive dependencies.
• In both cases, the fundamental error is that non key attributes are
dependent on other non key attributes.
• Derived attributes are those whose values can either be calculated
from other attributes, or derived through logic from the values of
other attributes.
• A transitive dependency exists when a non-key attribute is
dependent on another non-key attribute (other than by derivation).
• Transitive analysis is only performed on those entities that do not
have a concatenated key.
Data Analysis for Database Design

Normalization Example

Third Normal Form:
 Third normal form analysis looks for two types of problems,
derived data and transitive dependencies. (continued)
• A transitive dependency exists when a non-key attribute is
dependent on another non-key attribute (other than by derivation).
– This error usually indicates that an undiscovered entity is still
embedded within the problem entity.
• Transitive analysis is only performed on those entities that do not
have a concatenated key.

“An entity is said to be in third normal form if every nonprimary key attribute is dependent on the primary key, the
whole primary key, and nothing but the primary key.”
MEMBER ORDERED PRODUCT (2NF)
---------------Key Data-----------------Member-Number (PK1) (FK)
Product-Number (PK1) (FK)
-------------Non-Key Data------------Quantity-Ordered
Purchased-Unit-Price
Extended-Price
CORRECTION
MEMBER ORDERED PRODUCT (3NF)
---------------Key Data-----------------Member-Number (PK1) (FK)
Product-Number (PK1) (FK)
-------------Non-Key Data------------Quantity-Ordered
Purchased-Unit-Price
Extended-Price
MEMBER (3NF)
---------------------Key Data---------------------Member-Number (PK1)
------------------Non-Key Data------------------Member-Name
Member-Status
Member-Street-Address
Member-Daytime-Phone-Number
Date-of-Last-Order
Member-Balance-Due
Member-Bonus-Balance-Available
Member-Credit-Card-Information
placed
MEMBER ORDER (2NF)
------------------Key Data--------------------Order-Number (PK)
----------------Non-Key Data----------------Order-Creation-Date
Order-Automatic-Fill-Date
Member Number (FK1)
Member-Name
Member-Address
Shipping-Address
Shipping Instructions
Club-Name (FK2)
Order-Sub-Total-Cost
Order-Sales-Tax
Ship-Via-Method
Shipping-Charge
Order-Status
Prepaid-Amount
CORRECTION
MEMBER ORDER (3NF)
------------------Key Data--------------------Order-Number (PK)
----------------Non-Key Data----------------Order-Creation-Date
Order-Automatic-Fill-Date
Member Number (FK1)
Member-Name
Member-Address
Shipping-Address
Shipping Instructions
Club-Name (FK2)
Order-Sub-Total-Cost
Order-Sales-Tax
Ship-Via-Method
Shipping-Charge
Order-Status
Prepaid-Amount
Data Analysis for Database Design

Simplification by Inspection:

When several analysts work on a common application, it is not
unusual to create problems that won’t be taken care of by
normalization.
• These problems are best solved through simplification by
inspection, a process wherein a data entity in 3NF is further
simplified by such efforts as addressing subtle data redundancy.
Data Analysis for Database Design

CASE Support for Normalization:
 Most CASE tools can only normalize to first normal form.
• They accomplish this in one of two ways.
– They look for many-to-many relationships and resolve those
relationships into associative entities.
– They look for attributes specifically described as having
multiple values for a single entity instance.

It is exceedingly difficult for a CASE tool to identify second
and third normal form errors.
• That would require the CASE tool to have the intelligence to
recognize partial and transitive dependencies.
Database Design

The Database Schema

The design of a database is depicted as a special model called a
database schema.
 A database schema is the physical model or blueprint for a
database. It represents the technical implementation of the
logical data model.
 A relational database schema defines the database structure in
terms of tables, keys, indexes, and integrity rules.
 A database schema specifies details based on the capabilities,
terminology, and constraints of the chosen database
management system.
Database Design

The Database Schema

Transforming the logical data model into a physical relational
database schema rules and guidelines:
1 Each fundamental, associative, and weak entity is implemented
as a separate table.
• The primary key is identified as such and implemented as an index
into the table.
• Each secondary key is implemented as its own index into the table.
• Each foreign key will be implemented as such.
• Attributes will be implemented with fields.
– These fields correspond to columns in the table.
Database Design

The Database Schema

Transforming the logical data model into a physical relational
database schema rules and guidelines: (continued)
• The following technical details must usually be specified for each
attribute.
– Data type. Each DBMS supports different data types, and terms for
those data types.
– Size of the Field. Different DBMSs express precision of real numbers
differently.
– NULL or NOT NULL. Must the field have a value before the record
can be committed to storage?
– Domains. Many DBMSs can automatically edit data to ensure that
fields contain legal data.
– Default. Many DBMSs allow a default value to be automatically set in
the event that a user or programmer submits a record without a value.
Database Design

The Database Schema

Transforming the logical data model into a physical relational
database schema rules and guidelines: (continued)
2 Supertype/subtype entities present additional options as
follows:
• Most CASE tools do not currently support object-like constructs
such as supertypes and subtypes.
• Most CASE tools default to creating a separate table for each
entity supertype and subtype.
• If the subtypes are of similar size and data content, a database
administrator may elect to collapse the subtypes into the supertype
to create a single table.
3
Evaluate and specify referential integrity constraints.
Database Design

Data and Referential Integrity


There are at least three types of data integrity that must be
designed into any database - key integrity, domain integrity and
referential integrity.
Key Integrity:
 Every table should have a primary key (which may be
concatenated).
• The primary key must be controlled such that no two records in the
table have the same primary key value.
• The primary key for a record must never be allowed to have a
NULL value.
Database Design

Data and Referential Integrity


Domain Integrity:
 Appropriate controls must be designed to ensure that no field
takes on a value that is outside of the range of legal values.
Referential Integrity:
 A referential integrity error exists when a foreign key value in
one table has no matching primary key value in the related
table.
Database Design

Data and Referential Integrity

Referential Integrity:
 Referential integrity is specified in the form of deletion rules as
follows:
• No restriction.
– Any record in the table may be deleted without regard to any
records in any other tables.
• Delete:Cascade.
– A deletion of a record in the table must be automatically
followed by the deletion of matching records in a related table.
• Delete:Restrict.
– A deletion of a record in the table must be disallowed until any
matching records are deleted from a related table.
Database Design

Data and Referential Integrity

Referential Integrity:
 Referential integrity is specified in the form of deletion rules as
follows: (continued)
• Delete:Set Null.
– A deletion of a record in the table must be automatically
followed by setting any matching keys in a related table to the
value NULL.
Database Design

Roles



Some database shops insist that no two fields have exactly the
same name.
 This presents an obvious problem with foreign keys
A role name is an alternate name for a foreign key that clearly
distinguishes the purpose that the foreign key serves in the table.
The decision to require role names or not is usually established by
the data or database administrator.