Database design- system analysis and design

Systems Analysis & Design
Eleventh Edition
Chapter # 13
Designing Databases
Copyright © 2024, 2019, 2014 Pearson Education, Inc. All Rights Reserved

Learning Objectives (1 of 2)
13.1 Appreciate what a database is.
13.2 Understand basic data concepts.
13.3 Learn not to efficiently store data using normalization.
13.4 Know the guidelines for designing main files and
database relations.
13.5 Grasp the notion of normalization and why it is useful.

Learning Objectives (2 of 2)
13.6 Understand the concept of data warehouses.
13.7 Comprehend the relationship of business intelligence
to data warehouses.
13.8 Understand data analytics in helping systems and
people make decisions.
13.9 Learn about data lakes and how they differ from data
warehouses.
13.10 Gain basic knowledge about blockchains and their
usefulness.

Major Topics
• Databases
• Normalization
• Key design
• Using the database
• Data warehouses
• Data lakes
• Data mining
• Business intelligence

Data Storage (1 of 2)
• The data must be available when the user wants to use
them
• The data must be accurate and consistent
• Efficient storage of data as well as efficient updating
and retrieval
• It is necessary that information retrieval be
purposeful

Data Storage (2 of 2)
• There are two approaches to the storage of data in a
computer-based system:
– Store the data in individual files, each unique to a
particular application
– Store data in a database
▪ A database is a formally defined and centrally
controlled store of data intended for use in many
different applications

Databases (1 of 2)
• Effectiveness objectives of the database:
– Ensuring that data can be shared among users for a
variety of applications
– Maintaining data that are both accurate and consistent
– Ensuring data required for current and future applications
will be readily available
– Allowing the database to evolve as the needs of the users
grow
– Allowing users to construct their personal view of the data
without concern for the way the data are physically stored

Databases (2 of 2)
• Databases are highly useful for organizations to store,
access, and retrieve their data
• Security issues will arise
• Risk assessment is an essential part of database
planning

Database Security (1 of 2)
• Cyber attacks, including malware and denial of service
attacks, are on the increase
• Getting users involved in business continuity planning
and disaster recovery approaches will help make
databases and the organization resilient if security is
breached

Database Security (2 of 2)
• Companies must consider these questions:
– What is the likelihood of an attack?
– What is the value of the data that are being
protected?
– What are the implications of a security breach for the
company’s customers and, in the aftermath, for the
company’s image?

Reality, Data, and Metadata
• Reality
– The real world
• Data
– Collected about people, places, or events in reality
and eventually stored in a file or database
• Metadata
– Information that describes data

Figure 13.1 Reality, Data, and Metadata

Entities
• Any object or event about which someone chooses to
collect data
• May be a person, place, or thing
• May be an event or unit of time

Entity Subtype
• An entity subtype is a special one-to-one relationship
used to represent additional attributes, which may not
be present on every record of the first entity
• This eliminates null fields stored on database tables
• For example, students who have internships: the
STUDENT MASTER should not have to contain
information about internships for each student

Relationships
• Relationships
– One-to-one
– One-to-many
– Many-to-many
• A single vertical line represents one
• A crow’s foot represents many

Figure 13.2 Entity-Relationship Diagrams
Associations
Entity-relationship (E-R) diagrams
can show one-to-one, one-to-
many, or many-to-many
associations.

Figure 13.3 Entity-Relationship Symbols
and Their Meanings

Figure 13.4 The Entity-Relationship
Diagram for Patient Treatment
Attributes can be
listed alongside
the entities. The
key is underlined.

Attributes, Records, and Keys
• Attributes represent some characteristic of an entity
• Records are a collection of data items that have
something in common with the entity described
• Keys are data items in a record used to identify the
record

Figure 13.6 Attributes and Keys

Key Types
• Key types are:
– Primary key—unique attribute for the record
– Candidate key—an attribute or collection of attributes,
that can serve as a primary key
– Secondary key—a key which may not be unique,
used to select a group of records
– Composite key—a combination of two or more
attributes representing the key

Metadata
• Data about the data in the file or database
• Describe the name given and the length assigned
each data item
• Also describe the length and composition of each of
the records

Figure 13.7 Metadata
Metadata
includes a
description of
what the value of
each data item
looks like.

Files
• A file contains groups of records used to provide
information for operations, planning, management, and
decision making
• Files can be used for storing data for an indefinite period
of time, or they can be used to store data temporarily for
a specific purpose

File Types
• Master file
• Table file
• Transaction file
• Report file

Master and Table Files
• Master files:
– Contain records for a group of entities
– Contain all information about a data entity
• Table files:
– Contain data used to calculate more data or
performance measures
– Usually read-only by a program

Transaction and Report Files
• Transaction records:
– Used to enter changes that update the master file and
produce reports
• Report files:
– Used when it is necessary to print a report when no
printer is available
– Useful because users can take files to other computer
systems and output to specialty devices

Relational Databases
• A database is intended to be shared by many users
• There are three structures for storing database files:
– Relational database structures
– Hierarchical database structures
– Network database structures

Figure 13.8 Database Design
Database design includes
synthesizing user reports,
user views, and logical
and physical designs

Figure 13.9 Relational Data Structure
In a relational data structure, data are stored in many tables.
Item Price Item-# Title Price
B235 Guys and Dolls 8.99
B521 My Fair Lady 6.99
B894 42nd Street 10.99
B992 A Chorus Line 10.99
Order
Order-# Last
Name
I Street Address City St Charge
Acct
10784 MacRae G 2314 Curly
Circle
Lincoln NE 45-4654-76
10796 Jones S 34 Dream Lane Oklahoma
City
OK 44-9876-74
11821 Preston R 1008 Madison
Ave.
River City IA 34-7642-64
11845 Channing C 454 Harmonia
St.
New York NY 34-0876-87
11872 Kiley R 765 Dulcinea
Drive
La
Mancha
CA 65-8798-87
Item Status
Item-# Order-# Status
B235 10784 Shipped 5/12
B235 19796 Shipped 5/14
B235 11872 In Process
B521 11821 In Process
B894 11845 Backordered
B894 11872 Shipped 5/12
B992 10784 Shipped 5/12

Normalization
• Normalization is the transformation of complex user
views and data stores to a set of smaller, stable, and
easily maintainable data structures
• The main objective of the normalization process is to
simplify all the complex data items that are often found in
user views

Figure 13.10 Normalization of a Relation
is Accomplished in Three Major Steps

Data Model Diagrams
• Shows data associations of data elements
• Each entity is enclosed in an ellipse
• Arrows are used to show the relationships

Figure 13.13 Drawing Data Model
Drawing data model diagrams
for data associations
sometimes helps analysts
appreciate the complexity of
data storage.

First Normal Form (1NF)
• Remove repeating groups
• The primary key with repeating group attributes are
moved into a new table
• When a relation contains no repeating groups, it is in first
normal form

Figure 13.16 The Original Unnormalized
Relation
The original
unnormalized relation
SALES-REPORT is
separated into two
relations, SALESPERSON
(3NF) and
SALESPERSON-
CUSTOMER (1NF).

Second Normal Form (2NF)
• Remove any partially dependent attributes and place
them in another relation
• A partial dependency is when the data are dependent on
a part of a primary key
• A relation is created for the data that are only dependent
on part of the key and another for data that are
dependent on both parts

Figure 13.18 Second Normal Form
The relation SALESPERSON -
CUSTOMER is separated into
a relation called CUSTOMER
- WAREHOUSE (2NF) and a
relation called SALES (1NF).

Third Normal Form (3NF)
• Must be in 2NF
• Remove any transitive dependencies
• A transitive dependency is when nonkey attributes are
dependent not only on the primary key, but also on a
nonkey attribute

Figure 13.20 Third Normal Form
The relation CUSTOMER-WAREHOUSE is separated into two relations called
CUSTOMER (1NF) and WAREHOUSE (1NF).

Figure 13.22 AlS. Well Hydraulic
Company E-R Diagram

Using the Entity-Relationship Diagram to
Determine Record Keys
• When the relationship is one-to-many, the primary key
of the file at the one end of the relationship should be
contained as a foreign key on the file at the many end
of the relationship
• A many-to-many relationship should be divided into
two one-to-many relationships with an associative
entity in the middle

Guidelines for Master File/Database
Relation Design
• Each separate data entity should create a master
database table
• A specific data field should exist on one master table
• Each master table or database relation should have
programs to create, read, update, and delete the
records

Integrity Constraints
• Entity integrity
• Referential integrity
• Domain integrity

Entity Integrity
• The primary key cannot have a null value
• If the primary key is a composite key, none of the fields in
the key can contain a null value

Referential Integrity (1 of 3)
• Referential integrity governs the nature of records in a
one-to-many relationship
• Referential integrity means that all foreign keys in the
many table (the child table) must have a matching record
in the parent table

• Referential integrity implications:
– You cannot add a record in the child (many) table
without a matching record in the parent table
– You cannot change a primary key that has matching
child table records
– You cannot delete a record that has child records

• Implemented in two ways:
– A restricted database updates or deletes a key only if
there are no matching child records
– A cascaded database will delete or update all child
records when a parent record is deleted or changed

Domain Integrity
• Domain integrity rules are used to validate the data
• Domain integrity has two forms:
– Check constraints, which are defined at the table level
– Rules, which are defined as separate objects and can
be used within a number of fields

Anomalies
• Data redundancy
• Insert anomaly
• Deletion anomaly
• Update anomaly

Data Redundancy
• When the same data is stored in more than one place in
the database
• Solved by creating tables that are in third normal form

Insert Anomaly
• Occurs when the entire primary key is not known and the
database cannot insert a new record, which would violate
entity integrity
• Can be avoided by using a sequence number for the
primary key

Deletion Anomaly
• Happens when a record is deleted that results in the loss
of other related data

Update Anomaly
• When a change to one attribute value causes the
database to either contain inconsistent data or causes
multiple records to need changing
• May be prevented by making sure tables are in third
normal form

Retrieving and Presenting Database Data
• Choose a relation from the database
• Join two relations together
• Project columns from the relation
• Select rows from the relation
• Derive new attributes
• Index or sort rows
• Calculate totals and performance measures
• Present data

Denormalization
• Denormalization is the process of taking the logical data
model and transforming it into an efficient physical model

Data Warehouses and Database
Differences (1 of 2)
• Data warehouses are used to organize information for
quick and effective queries
• In the data warehouse, data are organized around major
subjects
• Data in the warehouse are stored as summarized rather
than detailed raw data
• Data in the data warehouse cover a much longer time
frame than in a traditional transaction-oriented database
• Data warehouses are organized for fast queries

Data Warehouses and Database
Differences (2 of 2)
• Data warehouses are usually optimized for answering
complex queries, known as OLAP
• Data warehouses allow for easy access via data-
mining software
• Data warehouses include multiple databases that
have been processed so that data are uniformly
defined
• Data warehouses usually include data from outside
sources

Online Analytic Processing
• Online analytic processing (OLAP) is meant to answer
decision makers’ complex questions by defining a
multidimensional database

Data-Mining Decision Aids
• Software
– Statistical analysis
– Decision trees
– Neural networks
– Intelligent agents
– Fuzzy logic
– Data visualization

Data-Mining Patterns
• Associations—patterns that occur together
• Sequences—patterns of actions that take place over a
period of time
• Clustering—patterns that develop among groups of
people
• Trends—the patterns that are noticed over a period of
time

Figure 13.27 Data Mining
Data mining collects
personal
information about
customers in an
effort to be more
specific in
interpreting and
anticipating their
preferences

Data-Mining Problems
• Costs may be too high to justify
• Has to be coordinated
• Ethical aspects

Business Intelligence (BI)
• Business intelligence is a decision support system (DSS)
for organizational decision makers
• It is composed of features that gather and store data
• It uses knowledge management approaches combined
with analysis
• This becomes input to decision makers’ decision-making
processes

Business Intelligence
• Business intelligence is built around processing large
volumes of data
• Big data is when data sets become too large or too
complex to be handled with traditional tools or within
traditional databases or data warehouses
• Big data is a strategy that allows organizations to cope
with ever-increasing numbers of data from a myriad of
sources
– Human generated
– Generated via sensors of some type

Analyzing Business Intelligence
• Five prominent methods are used for analyzing business
intelligence
– Slice-and-dice drilldown
– Ad hoc queries
– Real-time analysis
– Forecasting
– Scenarios

Data Analytics
• Data analytics use algorithms to analyze vast amounts
of data in databases or to analyze huge quantities of
unstructured data
• Analytics can refer to:
– Solving business problems and the decision-
making process
– The analytical approaches that help organizations
create value
– A broader term is business intelligence (BI)

Eight Aspects of Business Analysis (1 of 3)
1. Ensure the quality of the data
2. Facilitate communication between users and data
analytics professionals
3. Educate users and decision makers about how to use
analytical tools and how to interpret reports based on
use of those tools
4. Create reports that can be user generated

5. Work with users who prefer to have a data analytics
professional create reports
6. Develop a collaborative business intelligence platform
by collaborating on the creation of reports and making it
possible for decision makers to share insights

7. Ensure that the process of data analytics has the
desired impact on a business
8. Provide deep insights and understanding that support
decision makers and data analysts in making sense of
their individual and organizational experience

Data Lakes
• Repositories for raw data and normalized and
unnormalized data
• Data deposited needs not be structured or predefined
• Can be queried
• Are intended to make use of the large amount of data an
organization collects
• Can provide insights into customer behavior

Figure 13.28 Differences Between a Data
Warehouse and a Data Lake

Blockchains (1 of 5)
• A blockchain network is a data structure built on an
Internet platform that permits sharing and creation of a
digital ledger of data
• Can be shared with others on a network that is either
public or private

• Useful whenever a business or an individual wants to
possess a verifiable electronic record to track any kind of
business asset
• Blockchains came into the public consciousness with the
introduction of bitcoins

• Blockchain is especially useful in food supply chains for
tracking perishable products
• A blockchain is an open, immutable record of
transactions

• Are meant to:
– Improve security
– Cut down on risk
– Improve reliability and efficiency when conducting
transactions of all sorts

• Blockchains can be thought of as distributed databases
that feature controls on information that also store
information and share it

Permissioned Blockchains
• Businesses use special permissioned blockchains which
offer
– Better privacy
– Upgraded auditability
– Increased efficiency for operations
• A permissioned blockchain means that transactions can
be trusted

Web 3.0
• Just being built
• About decentralization of data and data ownership
• Users own their own data
• Blockchain based

Summary (1 of 3)
• Storing data
– Individual files
– Database
• Reality, data, metadata
• Conventional files
– Type
– Organization

Summary (2 of 3)
• Database
– Relational
– Hierarchical
– Network
• E-R diagrams
• Normalization
– First normal form
– Second normal form
– Third normal form

Summary (3 of 3)
• Denormalization
• Data warehouse
• Data mining
• Business Intelligence (BI)
• Data lakes
• Blockchains
• Web 3.0

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