database design. database concepts for the systems analyst fields fields are common to both files...

Database Design

Information System

File FileInformation

SystemInformation

System

Database (consolidated & integrated data

from files)

File

File

Information System

Information System

INFORMATION SYSTEMS FRAMEWORK

SYSTEM

ANALYSTS

SYSTEMBUILDERS

(components)

SYSTEMDESIGNERS

(specification)

SYSTEMUSERS

(requirements)

SYSTEMOWNERS

(scope)

ExistingDatabases

andTechnology

Database Programs

Database Schema

Data Requirements

data models

Business Subjects

FOCUS ONSYSTEM

DATA

FOCUS ONSYSTEM

PROCESSES

FOCUS ON SYSTEM

INTERFACES

ExistingApplications

andTechnology

ExistingInterfaces

andTechnology

ExistingNetworks

andTechnology

FOCUS ONSYSTEM

GEOGRAPHY

Implementation

Phase

(translate technical

design into code)

Design Phase

(translate business

requirements into a

technical design)

Definition Phase(establish and

prioritizebusiness system

requirements)

Study Phase

(establishsystem

improvement objectives)

Survey Phase(establish scope and

project plan)

FASTMethodology

CUSTOMER customer-no customer-name customer-rating balance-due

PRODUCT product-no product-name unit-of-measure unit-price quantity-available

ORDER order-no order-date products-ordered quantities-ordered

Customers order zero, one, or more products. Products may be ordered by zero, one, or more customers.

C U STOMER customer_no [A lpha (10)] IN D EX customer_name [A lpha(32)] customer_rat ing [A lpha(1)] IN D EX balance_due [R eal(5,2)]

PR OD U C T product_no [A lpha(10)] IN D EX product_name [A lpha(32)] unit_of_measure [A lpha(2)] unit_price [R eal(3,2)] quantity_available [ Integer(4)]

OR D ER order_no [A lpha(12)] IN D EX order_date [D ate(mmddyyyy) C U STOMER .customer_no

OR D ER _PR OD U C T OR D ER .order_no PR OD U C T.product_no quantity_ordered [ Integer(2)

CREATE TABLE CUSTOMER (customer_no CHAR(10) NOT NULL customer_name CHAR(32) NOT NULL customer _rating CHAR(1) NOT NULL balance_due DECIMAL(5,2) CREATE INDEX cust_no_idx on CUSTOMER CREATE INDEX cust_rt_idx on CUSTOMER

Database Concepts for the Systems Analyst

Fields Fields are common to both files and databases.

A field is the implementation of a data attribute. • Fields are the smallest unit of meaningful data to be stored in a file

or database. There are four types of fields that can be stored: primary keys,

secondary keys, foreign keys, and descriptive fields. Primary keys are fields whose values identify one and only

one record in a file. Secondary keys are alternate identifiers for an database.

• A single file in a database may only have one primary key, but it may have several secondary keys.


Fields There are four types of fields that can be stored: primary keys,

secondary keys, foreign keys, and descriptive fields. (continued) Foreign keys are pointers to the records of a different file in a

database. • Foreign keys are how the database ‘links’ the records of one type

to those of another type. Descriptive fields are any other fields that store business data.


Records Fields are organized into records. Like fields, records are common to both files and databases.

A record is a collection of fields arranged in a predefined format. During systems design, records will be classified as either fixed-

length or variable-length records. Most database systems impose a fixed-length record structure,

meaning that each record instance has the same fields, same number of fields, and same logical size.

Variable-length record structures allow different records in the same file to have different lengths.

• Database systems typically disallow (or, at least, discourage) variable length records.


Records When a computer program ‘reads’ a record from a database, it

actually retrieves a group or block of records at a time. This approach minimizes the number of actual disk accesses. A blocking factor is the number of logical records included in

a single read or write operation (from the computer’s perspective). A block is sometimes called a physical record.

Today, the blocking factor is usually determined and optimized by the chosen database technology, but a qualified database expert may be allowed to fine tune that blocking factor for performance.


Files and Tables Similar records are organized into groups called files.

A file is the set of all occurrences of a given record structure. In database systems, a file corresponds to a set of similar records;

usually called a table. A table is the relational database equivalent of a file.

Some of the types of files and tables include: Master files or tables contain records that are relatively

permanent. • Once a record has been added to a master file, it remains in the

system indefinitely.

• The values of fields for the record will change over its lifetime, but the individual records are retained indefinitely.


Files and Tables Some of the types of files and tables include: (continued)

Transaction files or tables contain records that describe business events.

• The data describing these events normally has a limited useful lifetime.

• In information systems, transaction records are frequently retained on-line for some period of time.

• Subsequent to their useful lifetime, they are archived off-line. Document files and tables contain stored copies of historical

data for easy retrieval and review without the overhead of re-generating the document.



Archival files and tables contain master and transaction file records that have been deleted from on-line storage.

• Records are rarely deleted; they are merely moved from on-line storage to off-line storage.

• Archival requirements are dictated by government regulation and the need for subsequent audit or analysis.

Table look-up files contain relatively static data that can be shared by applications to maintain consistency and improve performance.



Audit files are special records of updates to other files, especially master and transaction files.

• They are used in conjunction with archive files to recover ``lost’’ data.

• Audit trails are typically built into better database technologies.


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.

A legacy file-based

information system

(built in-house)

File

FileInformation

System

(built in-house)

Information System

(built in-house)

Operational Database

File

File

Information System

(built in-house)

A legacy file-based

information system

(purchased)

File

File

File

Information System

(purchased)

Operational Database

Data Warehouse

End-User Tools

End-User Applications

Personal DB

Work-Group Database

Users and Programmers




Users

End-User Work Group


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.



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.

Database Management System (DBMS)

Data Definition Language

DDL

Data Manipulation

Language DML

Proprietary Data Manipulation

Language and/or Report Writers

Host-based Transaction Processing

Monitor (optional)

Internal TP Monitor

(opt)

Stored Data Metadata

Programmers

End Users

Systems Analysts and/or

Database Designers



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.



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.


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.

sells sold onOrdered Product

Customer Order Productplaces

Customers TableCustomer Number Customer Name Customer

Balance…

10112 Luck Star 1455.7710113 Pemrose 12.1410114 Hartman 0.0010117 K-Jack Industries - 20.00

OrdersTableOrderNumber

Customer Number(foreign key)

…

A633 10112A634 10114A635 10112

Ordered Products TableOrderNumber(foreignkey)

Product Number(foreign key)

QuantityOrdered

…

A633 77F02 1A633 77B12 500A634 77B13 100A634 77F01 5A635 77B12 300A635 77B15 15

Products TableProduct Number Product Description Quantity

in Stock…

77B12 Widget 800077B13 Widget 077B15 Widget 5277F01 Gadget 2077F02 Gadget 2



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).



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 The technique used to improve a data model in preparation for

database design is called 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 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.


Normalization Example First Normal Form:

The first step in data analysis is to place each entity into 1NF.

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

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

sold

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

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

is a

is a

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

placed

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

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

enrolls in

sponsors

generates

features

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

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

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

sold as

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

sells

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

CLUB (1NF) ------------------Key Data---------------------- Club-Name (PK) --------------Non-Key Data-------------------- Club-Description Club-Charter-Date

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

establishes

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

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

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

CLUB (1NF) ------------------Key Data---------------------- Club-Name (PK) --------------Non-Key Data-------------------- Club-Description Club-Charter-Date

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

enrolls in

binds

establishes

sponsors

CORRECTION


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.

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

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

MEMBER ORDERED PRODUCT (2NF) ---------------Key Data------------------ Member-Number (PK1) (FK) Product-Number (PK1) (FK) -------------Non-Key Data------------- Quantity-Ordered Purchased-Unit-Price Extended-Price

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

is a

sold as

CORRECTION


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.


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 non-primary key attribute is dependent on the primary key, the whole primary key, and nothing but the primary key.”



CORRECTION

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



CORRECTION

placed


Normalization Example 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.


Normalization Example 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

Introduction The design of any database will usually involve the DBA and

database staff. They will handle the technical details and cross-application

issues. It is useful for the systems analyst to understand the basic design

principles for relational databases.

Database Design

Goals and Prerequisites to Database Design The goals of database design are as follows:

A database should provide for the efficient storage, update, and retrieval of data.

A database should be reliable – the stored data should have high integrity to promote user trust in that data.

A database should be adaptable and scaleable to new and unforeseen requirements and applications.

Database Design

Goals and Prerequisites to Database Design The data model may have to be divided into multiple data models

to reflect database distribution and database replication decisions. Data distribution refers to the distribution of either specific

tables, records, and/or fields to different physical databases. Data replication refers to the duplication of specific tables,

records, and/or fields to multiple physical databases. Each sub-model or view should reflect the data to be stored on a

single server.

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


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


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.

Database Design

Database Prototypes Prototyping is not an alternative to carefully thought out database

schemas. On the other hand, once the schema is completed, a prototype

database can usually be generated very quickly. Most modern DBMSs include powerful, menu-driven database

generators that automatically create a DDL and generate a prototype database from that DDL. A database can then be loaded with test data that will prove

useful for prototyping and testing outputs, inputs, screens, and other systems components.

Database Design

Database Capacity Planning A database is stored on disk.

The database administrator will want an estimate of disk capacity for the new database to ensure that sufficient disk space is available.

Database capacity planning can be calculated with simple arithmetic as follows.

1 For each table, sum the field sizes. • This is the record size for the table.

2 For each table, multiply the record size times the number of entity instances to be included in the table.

• This is the table size.

Database Design

Database Capacity Planning Database capacity planning can be calculated with simple

arithmetic as follows. (continued)3 Sum the table sizes.

• This is the database size.

4 Optionally, add a slack capacity buffer (e.g., 10%) to account for unanticipated factors or inaccurate estimates above.

• This is the anticipated database capacity.

Database Design

Database Structure Generation CASE tools are frequently capable of generating SQL code for the

database directly from a CASE-based database schema. This code can be exported to the DBMS for compilation. Even a small database model can require 50 pages or more of

SQL data definition language code to create the tables, indexes, keys, fields, and triggers.

Clearly, a CASE tool’s ability to automatically generate syntactically correct code is an enormous productivity advantage.

Furthermore, it almost always proves easier to modify the database schema and re-generate the code, than to maintain the code directly.

The Next Generation of Database Design

Introduction Relational database technology is widely deployed and used in

contemporary information system shops. One new technology is slowly emerging that could ultimately

change the landscape dramatically – object database management systems. The heir apparent to relational DBMSs, object database

management systems store true objects, that is, encapsulated data and all of the processes that can act on that data.

Because relational database management systems are so widely used, we don’t expect this change to happen quickly.

• It is expected that these vendors will either build object technology into their existing relational DBMSs, or they will create new, object DBMSs and provide for the transition between relational and object models.

database design. database concepts for the systems analyst fields fields are common to both files...

Documents

database systems

systems analyst records

systems analyst fields

database concepts

types of fields

different records

database links

number of fields