Using Data Flow Diagrams - … · Using Data Flow Diagrams. Telkom University 2 Creating the great...

Program Studi: Manajemen Bisnis Telekomunikasi & InformatikaMata Kuliah: Systems Analysis and Design

Oleh: Yudi Priyadi

Using Data Flow Diagrams

Telkom University

2 Creating the great business leaders

Program Studi:MANAJEMEN BISNIS TELEKOMUNIKASI & INFORMATIKA

Dosen:Yudi Priyadi, M.T.

Fakultas Ekonomi dan BisnisSchool Economic and Business

Using Data Flow Diagrams

SOURCE: Systems Analysis and Design, 9eKendall & Kendall, Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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Comprehend the importance of using logical and physical data flow diagrams (DFDs) to graphically depict movement for humans and systems in an organization.

Create, use, and explode logical DFDs to capture and analyze the current system through parent and child levels.

Develop and explode logical DFDs that illustrate the proposed system.

Produce physical DFDs based on logical DFDs you have developed.

Understand and apply the concept of partitioning of physical DFDs.

Kendall & KendallCopyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

7-3

Learning Objectives

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Graphically characterize data processes and flows in a business system

Depict:

System inputs

Processes

Outputs

7-4

Data Flow Diagrams


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Data flow diagram symbols

Data flow diagram levels

Creating data flow diagrams

Physical and logical data flow diagrams

Partitioning

Communicating using data flow diagrams

7-5

Major Topics


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Freedom from committing to the technical implementation too early

Understanding of the interrelatedness of systems and subsystems

Communicating current system knowledge to users

Analysis of the proposed system

7-6

Advantages of the Data Flow Approach


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A double square for an external entity

An arrow for movement of data from one point to another

A rectangle with rounded corners for the occurrence of a transforming process

An open-ended rectangle for a data store

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Basic Symbols


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7-8

The Four Basic Symbols Used in Data Flow Diagrams, Their Meanings, and Examples (Figure 7.1)


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Represent another department, a business, a person, or a machine

A source or destination of data, outside the boundaries of the system

Should be named with a noun

7-9

External Entities


Shows movement of data from one point to another

Described with a noun

Arrowhead indicates the flow direction

Represents data about a person, place, or thing

Data Flow

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Denotes a change in or transformation of data

Represents work being performed in the system

Naming convention:

Assign the name of the whole system when naming a high-level process

To name a major subsystem attach the word subsystem to the name

Use the form verb-adjective-noun for detailed processes

7-10

Process

A depository for data that allows examination, addition, and retrieval of data

Named with a noun, describing the data

Data stores are usually given a unique reference number, such as D1, D2, D3

Represents a:

Database

Computerized file

Filing cabinet

Data Store


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Steps in Developing Data Flow Diagrams(Figure 7.2)


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The highest level in a data flow diagram

Contains only one process, representing the entire system

The process is given the number 0

All external entities, as well as major data flows are shown

7-12

Creating the Context Diagram


The data flow diagram must have one process

Must not be any freestanding objects

A process must have both an input and output data flow

A data store must be connected to at least one process

External entities should not be connected to one another

Basic Rules

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Representation ofData Flow and Information

13

Source: www.yourdon.com©2006 Ed Yourdon - rev. 013106

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IPO: Input →Process → Output

Data→ Process → Information

Data flow diagram is a graphical technique that depicts information flow and the transforms that are applied as data moves from input to output.

DFDs use four basic symbols that represent processes, data flows, data stores, and entities

Gane and Sarson symbol set

Yourdon symbol set

Concept of Data Flow Diagram


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Symbols for DFD

Data store

Processname

ExternalInteractor

External Entity:Source or destination of data

Process:Action on data

Data Store:Storage of data

Data Flow:Data Transfer

Yourdon notations (source: www.yourdon.com)


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A context diagram is a top level (also known as Level 0) data flow diagram.

It only contains one process node (process 0) that generalizes the function of the entire system in relationship to external entities.

Context Diagrams (incomplete)


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Create a graphical model of the information system based on your fact-finding results

Performing three main tasks

Step 1: Draw a context diagram

Step 2: Draw a DFD level 1

Step 3: Draw the lower-level diagrams

Creating a Set of DFDs


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Drawing Guidelines1. Draw the context diagram so it fits on one page

2. Use the name of the information system as the process name in the context diagram

3. Use unique names within each set of symbols

4. Do not cross lines

5. Provide a unique name and reference number for each process

6. Obtain user input and feedback

Draw a Context Diagram


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External entity represents the sources and destination of data created by the system.

External entity represents the immediate interface of the system with the external world.

When an external source of data is also a destination for data, a loop or occurrence number may be used.

In case the destination or use of data created by the process are not known, the flow simply points outside the system. Similarly, data flows may originate from “nowhere”.

External Entity


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Each processes box in a DFD describes an action on data. The Identifier. A number indicating the sequence of the process. The Action. A verb specifying the action on which it is performed on

the data. The Actor or Place. A noun indicating who performs the action or

where it is performed.

Process Boxes


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Data flow arrows link all the process boxes and data stores in DFDs. Data flows should be labeled, except in case the data flows into and

out of simple files. DFDs show only the flow of data, not materials. A DFD depicts information flow without explicit representation of

procedural logic (e.g., conditions or loops).

Data Flow Arrows


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Data stores can be manual files or computer files. The type of file is not indicated.

Only in case the data store is altered the flow is not indicated. A simple access is not indicated.

A data store is never the direct recipient of unprocessed data from external sources or from other data stores nor is data from a data store ever directly delivered to an external sources. There must be a process step in between.

Data Store Rectangles


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Examples of Data Stores

Read

Write

Read/Write

A data item is createdor deleted or updated in the

data store bya process


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Rules for Constructing DFD


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DFD Not Allowed Flows


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DFD Not Allowed Flows

If part of our system

If not part of our flow ignore


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Only one direction of flow between processesData Flows


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Joins & forks allowed only if exactly the same dataData Flows


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Cannot go directly back to the process it leavesData Flows

Incorrect if.........


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Data which moves together should be shown in a single data flow

Data Flows

itemised calls

invoice

invoicepayment

itemised callsAnd invoice

Pay Invoice

TelephoneCompany

Pay Invoice

TelephoneCompany

invoicepayment


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DFD Rules Incorrect Correct


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DFD Rules

Incorrect

Correct


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DFD RulesIncorrect Correct


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Level 0 CD

Origin #1

Destination 4

0System

a

b

c

z

r

Destination 3

Origin #2

Explanation:a: .........b: ...........c: .......... Etc


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Level 1 DFD

1

a

b

c

z

r

2

3

4

5

d

e

f

g h

i

Origin #1

Origin #2

Destination 3

Destination 4

n

p

dtstore1

dtstore2


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Level 2 DFD

c

f

2.1 2.2

2.3

Origin #2

m

k

j

dtstore3

4


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The conservation of input and output flows through different levels

Balancing

A

B

C

A

B

C

D

E


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A balanced DFD Fragment

source: www.yourdon.com

Example


E1 E2

E3

E1

E2

E3

1

4 2

E1 E2

E3

Edited by

Yudi Priyadi

http://yourdon.com/strucanalysis/wiki/index.php?title=Image:Figure922a.jpg

http://yourdon.com/strucanalysis/wiki/index.php?title=Image:Figure922a.jpg

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Example of Context Diagram(Let’s check with the rules together ^^ ...)


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Example of DFD Level 1(Let’s check with the rules together ^^ ...)


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41

Context Diagram

Agate

Campaign

Management

System

Campaign

Manager

Client

Budget

Campaign

Staff

Campaign

Advert

Staff Assignment

Accountant

Concept Note

StaffConcept Note

Staff

Staff Grade

Staff

Contact

Payment

Advert Completion

Client Contact

(Let’s check with the rules together ^^ ...)

© 2010 Bennett, McRobb and Farmer


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Top Level Diagram (Level 1)

1.

Record

Clients

Campaign

Manager

Client

Staff Assignment

Campaign

Staff

Campaign

Advert

Accountant

Concept Note

Staff

Concept

Note

Staff

Staff Grade

Staff

Contact

Payment

Advert Completion

Client Contact

3.

Prepare

Adverts

Notes

6.

Browse

Concept

Notes

Concept

Note

Concept Note

4.

Maintain

Staff

5.

Manage

Adverts

Adverts

Advert

Contact

+ Completion Date

Clients

Client

2.

Plan and

Manage

Campaigns

Staff Members

Staff

Budget

Cost

Concept

Note

Campaigns

Campaign

Staff

Staff


© 2010 Bennett, McRobb and FarmerSource: www.yourdon.com©2006 Ed Yourdon - rev. 013106

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43

© 2010 Bennett, McRobb and Farmer

Level 2 Diagram

5.1

Set Client

Contact

Adverts

Contact

Staff Members

Staff

Completion Date5.2

Set Advert

Completed

Staff

Contact



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Context Diagram (Figure 7.3)


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The explosion of the context diagram

May include up to nine processes

Each process is numbered

Major data stores and all external entities are included

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Drawing Diagram 0

Start with the data flow from an entity on the input side

Work backward from an output data flow

Examine the data flow to or from a data store

Analyze a well-defined process

Take note of any fuzzy areas Kendall & KendallCopyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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Note Greater Detail in Diagram 0 (Figure 7.3)


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Data flow diagrams are built in layers

The top level is the context level

Each process may explode to a lower level

The lower level diagram number is the same as the parent process number

Processes that do not create a child diagram are called primitive

7-47

Data Flow Diagram Levels


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Each process on diagram 0 may be exploded to create a child diagram

A child diagram cannot produce output or receive input that the parent process does not also produce or receive

The child process is given the same number as the parent process

Process 3 would explode to Diagram 3

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Creating Child Diagrams

Entities are usually not shown on the child diagrams below Diagram 0

If the parent process has data flow connecting to a data store, the child diagram may include the data store as well

When a process is not exploded, it is called a primitive process


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Differences between the Parent Diagram (above) and the Child Diagram (below) (Figure 7.4)


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Forgetting to include a data flow or pointing an arrow in the wrong direction

Connecting data stores and external entities directly to each other

Incorrectly labeling processes or data flow

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Data Flow Diagrams Error Summary

Including more than nine processes on a data flow diagram

Omitting data flow

Creating unbalanced decomposition (or explosion) in child diagrams


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Forgetting to include a data flow or pointing an arrow in the wrong direction

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Checking the Diagrams for Errors (Figure 7.5)


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Connecting data stores and external entities directly to each other

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Checking the Diagrams for Errors (continued Figure 7.5)


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Typical Errors that Can Occur in a Data Flow Diagram (Payroll Example) (continued Figure 7.5)


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Logical

Focuses on the business and how the business operates

Not concerned with how the system will be constructed

Describes the business events that take place and the data required and produced by each event

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Logical and Physical Data Flow Diagrams

Physical

Shows how the system will be implemented

Depicts the system


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Features Common of Logical and Physical Data Flow Diagrams (Figure 7.7)


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The Progression of Models from Logical to Physical (Figure 7.8)


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Logical Data Flow Diagram Example (Figure 7.9)


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Physical Data Flow Diagram Example (Figure 7.9)


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Better communication with users

More stable systems

Better understanding of the business by analysts

Flexibility and maintenance

Elimination of redundancy and easier creation of the physical model

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Developing Logical Data Flow Diagrams


Clarifying which processes are performed by humans and which are automated

Describing processes in more detail

Sequencing processes that have to be done in a particular order

Identifying temporary data stores

Specifying actual names of files and printouts

Adding controls to ensure the processes are done properly

Developing Physical Data Flow Diagrams

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Physical Data Flow Diagrams Contain Many Items Not Found in Logical Data Flow Diagrams (Figure 7.10)


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The acronym CRUD is often used for

Create

Read

Update

Delete

These are the activities that must be present in a system for each master file

A CRUD matrix is a tool to represent where each of these processes occurs in a system

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CRUD Matrix


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CRUD Matrix (Figure 7.11)


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An input flow from an external entity is sometimes called a trigger because it starts the activities of a process

Events cause the system to do something and act as a trigger to the system

An approach to creating physical data flow diagrams is to create a data flow diagram fragment for each unique system event

7-63

Event Modeling and Data Flow Diagrams


An event table is used to create a data flow diagram by analyzing each event and the data used and produced by the event

Every row in an event table represents a data flow diagram fragment and is used to create a single process on a data flow diagram

Event Response Tables

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An Event Response Table for an Internet Storefront (Figure 7.12)


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Data Flow Diagrams for the First Three Rows of the Internet Storefront Event Response Table (Figure 7.13)


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Each use case defines one activity and its trigger, input, and output

Allows the analyst to work with users to understand the nature of the processes and activities and then create a single data flow diagram fragment

7-66

Use Cases and Data Flow Diagrams


Partitioning is the process of examining a data flow diagram and determining how it should be divided into collections of manual procedures and computer programs

A dashed line is drawn around a process or group of processes that should be placed in a single computer program

Partitioning Data Flow Diagrams

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Different user groups

Timing

Similar tasks

Efficiency

Consistency of data

Security

7-67

Reasons for Partitioning

Improves the way humans use the site

Improves speed of processing

Ease of maintaining the site

Keep the transaction secure

Partitioning Websites


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Use unexploded data flow diagrams early when ascertaining information requirements

Meaningful labels for all data components

7-68

Communicating Using Data Flow Diagrams


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Data flow diagrams

Structured analysis and design tools that allow the analyst to comprehend the system and subsystems visually as a set of interrelated data flows

DFD symbols

Rounded rectangle

Double square

An arrow

Open-ended rectangle

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Summary Creating the logical DFD

Context-level data flow diagram

Level 0 logical data flow diagram

Child diagrams

Creating the physical DFD

Create from the logical data flow diagram

Partitioned to facilitate programming

Partitioning data flow diagrams

Whether processes are performed by different user groups

Processes execute at the same time

Processes perform similar tasks

Batch processes can be combined for efficiency of data

Processes may be partitioned into different programs for security reasons


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Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

Program Studi: Manajemen Bisnis Telekomunikasi & InformatikaMata Kuliah: Systems Analysis and Design

Oleh: Yudi Priyadi

Designing Databases

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Understand database concepts.

Use normalization to efficiently store data in a database.

Use databases for presenting data.

Understand the concept of data warehouses.

Comprehend the usefulness of publishing databases to the Web.

Understand the relationship of business intelligence to data warehouses, big data, business analytics and text analytics in helping systems and people make decisions.

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Learning Objectives


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Databases

Normalization

Key design

Using the database

Data warehouses

Data mining

Business intelligence

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Major Topics


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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

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Data Storage 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


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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

13-75

Databases


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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

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Reality, Data, and Metadata


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13-77

Reality, Data, and Metadata (Figure 13.1)


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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

13-78

Entities

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

Entity Subtype

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Relationships

One-to-one

One-to-many

Many-to-many

A single vertical line represents one

A crow’s foot represents many

13-79

Relationships


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13-80

Entity-Relationship Diagrams Associations (Figure 13.2, Part 1)

Entity-relationship (E-R) diagrams can show one-to-

one, one-to-many, or many-to-many associationsKendall & KendallCopyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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Entity-relationship (E-R) diagrams can show one-to-

one, one-to-many, or many-to-many associationsKendall & KendallCopyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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Entity-relationship (E-R) diagrams

can show one-to-one, one-to-many,

or many-to-many associations


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13-83

Entity-Relationship Symbols and Their Meanings (Figure 13.3)


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13-84

The Entity-Relationship Diagram for Patient Treatment (Figure 13.4)

Attributes can be listed

alongside the entities.

The key is underlined.


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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

13-85

Attributes, Records, and Keys

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

Key Types

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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

13-86

Metadata


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Metadata (Figure 13.7) Metadata

includes a

description of

what the value

of each data

item looks

like.


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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

13-88

Files

Master file

Table file

Transaction file

Report file

File Types


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Master files:

Contain records for a group of entities

Contain all information about a data entity

Table files:

Contains data used to calculate more data or performance measures

Usually read-only by a program

13-89

Master and Table Files


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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

13-90

Transaction and Report Files


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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

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Relational Databases


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Database Design (Figure 13.8)

Database design

includes

synthesizing

user reports,

user views, and

logical and

physical designs


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Relational Data Structure (Figure 13.9)

In a relational

data structure,

data are

stored in

many tables.


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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

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Normalization


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13-95

Normalization of a Relation Is Accomplished in Three Major Steps(Figure 13.10)


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Shows data associations of data elements

Each entity is enclosed in an ellipse

Arrows are used to show the relationships

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Data Model Diagrams


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13-97

Drawing Data Model (Figure 13.13)

Drawing data model

diagrams for data

associations

sometimes helps

analysts appreciate

the complexity of data

storage.


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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

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First Normal Form (1NF)


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13-99

The Original Unnormalized Relation (Figure 13.16)

The original

unnormalized relation

SALES-REPORT is

separated into two

relations,

SALESPERSON (3NF)

and SALESPERSON-

CUSTOMER (1NF).

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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

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Second Normal Form (2NF)


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13-101

Second Normal Form (Figure 13.18 )

The relation SALESPERSON-

CUSTOMER is separated into a

relation called CUSTOMER-

WAREHOUSE (2NF) and a relation

called SALES (1NF).


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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

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Third Normal Form (3NF)


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13-103

Third Normal Form (Figure 13.20)

The relation

CUSTOMER-

WAREHOUSE is

separated into two

relations called

CUSTOMER

(1NF) and

WAREHOUSE

(1NF).


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13-104

Kendall & Kendall Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

Al S. Well Hydraulic Company E-R Diagram (Figure 13.22)

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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

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Using the Entity-Relationship Diagram to Determine Record Keys


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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

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Guidelines for Master File/Database Relation Design

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Entity integrity

Referential integrity

Domain integrity

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Integrity Constraints

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

Entity Integrity


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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

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Referential Integrity

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

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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

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Domain Integrity

Data redundancy

Insert anomaly

Deletion anomaly

Update anomaly

Anomalies

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When the same data is stored in more than one place in the database

Solved by creating tables that are in third normal form

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Data Redundancy

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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

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Insert Anomaly

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

Deletion 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

Update Anomaly

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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

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Retrieving and Presenting Database Data

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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

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Data Warehouses and Database Differences

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

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

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Online Analytic Processing

Software

Statistical analysis

Decision trees

Neural networks

Intelligent agents

Fuzzy logic

Data visualization

Data-Mining Decision Aids 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

Data-Mining Patterns

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Data Mining (Figure 13.27)

Data mining collects

personal information

about customers in

an effort to be more

specific in

interpreting and

anticipating their

preferences


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Costs may be too high to justify

Has to be coordinated

Ethical aspects

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Data-Mining Problems

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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

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Business Intelligence (BI)

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 permits organizations to cope with ever-increasing numbers of data from a myriad of sources

Human generated

Generated via sensors of some type

Telkom University





Five prominent methods are used for analyzing business intelligence

Slice-and-dice drilldown

Ad hoc queries

Real-time analysis

Forecasting

Scenarios

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Analyzing Business Intelligence

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Text analytics is a way to structure the unstructured

Turning qualitative material into quantitative material

The broader view is to tap into qualitative unstructured data that can be of use to decision makers who must recommend courses of action to their organizations that are backed by data

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Text Analytics

Telkom University





Sources of big data for text analytics include unstructured, qualitative, or “soft,” data generated through:

Blogs

Chat rooms

Questionnaires using open-ended questions

Online discussions conducted on the Web

Social media such as Facebook

Twitter

Other Web-generated dialogs between customers and an organization

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Text Analytics Sources

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Storing data

Individual files

Database

Reality, data, metadata

Conventional files

Type

Organization

Database

Relational

Hierarchical

Network

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Summary E-R diagrams

Normalization

First normal form

Second normal form

Third normal form

Data warehouse

Data mining

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Copyright © 2014 Pearson Education, Inc. Publishing as Prentice Hall

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