James hall ch 9

Accounting Information Systems, 6th
edition
James A. Hall
COPYRIGHT © 2009 South-Western, a division of Cengage Learning. Cengage Learning and South-Western
are trademarks used herein under license

Objectives for Chapter 9
Problems inherent in the flat file approach to data
management that gave rise to the database concept
Relationships among the defining elements of the
database environment
Anomalies caused by unnormalized databases and the
need for data normalization
Stages in database design: entity identification, data
modeling, constructing the physical database, and
preparing user views
Features of distributed databases and issues to consider in
deciding on a particular database configuration

Flat-File Versus Database Environments
Computer processing involves two components: data and
instructions (programs)
Conceptually, there are two methods for designing the
interface between program instructions and data:
File-oriented processing: A specific data file was created
for each application
Data-oriented processing: Create a single data
repository to support numerous applications.
Disadvantages of file-oriented processing include
redundant data and programs and varying formats for
storing the redundant data.

Flat-File Environment
Program 1
Program 2
Program 3
A,B,C
X,B,Y
L,B,M
User 2
Transactions
User 1
Transactions
User 3
Transactions
Data

Data Redundancy and Flat-File
Problems
Data Storage - creates excessive storage costs of
paper documents and/or magnetic form
Data Updating - any changes or additions must
be performed multiple times
Currency of Information - potential problem
of failing to update all affected files
Task-Data Dependency - user’s inability to
obtain additional information as his or her needs
change

Program 1
Program 2
Program 3
User 2
Transactions
User 1
Transactions
User 3
Transactions
Database
D
B
M
S
A,
B,
C,
X,
Y,
L,
M
Database Approach

Advantages of the Database Approach
Data sharing/centralize database resolves flat-file
problems:
 No data redundancy: Data is stored only once,
eliminating data redundancy and reducing storage
costs.
 Single update: Because data is in only one place, it
requires only a single update, reducing the time and
cost of keeping the database current.
 Current values: A change to the database made by any
user yields current data values for all other users.
 Task-data independence: As users’ information needs
expand, the new needs can be more easily satisfied
than under the flat-file approach.

Disadvantages of the Database Approach
Can be costly to implement
additional hardware, software, storage, and network
resources are required
Can only run in certain operating
environments
may make it unsuitable for some system
configurations
Because it is so different from
the file-oriented approach, the database
approach requires training users
may be inertia or resistance

Elements of the Database Environment
System Development
Process
Database
Administrator
U
S
E
R
S
DBMS
Host
Operating
System
Physical
Database
User
Programs
User
Programs
User
Programs
Applications
Data
Definition
Language
Data
Manipulation
Language
Query
Language
User Queries
Transactions
Transactions
Transactions
SystemRequests

Internal Controls and DBMS
The database management system (DBMS) stands
between the user and the database per se.
Thus, commercial DBMS’s (e.g., Access or Oracle)
actually consist of a database plus…
Plus software to manage the database, especially
controlling access and other internal controls
Plus software to generate reports, create data-entry
forms, etc.
The DBMS has special software to know which data
elements each user is authorized to access and deny
unauthorized requests of data.

DBMS Features
Program Development - user created applications
Backup and Recovery - copies database
Database Usage Reporting - captures statistics on
database usage (who, when, etc.)
Database Access - authorizes access to sections of the
database
Also…
User Programs - makes the presence of the DBMS
transparent to the user
Direct Query - allows authorized users to access data
without programming

Data Definition Language (DDL)
DDL is a programming language used to define
the database per se.
It identifies the names and the relationship of all data
elements, records, and files that constitute the
database.
DDL defines the database on three viewing levels
Internal view – physical arrangement of records (1
view)
Conceptual view (schema) – representation of
database (1 view)
User view (subschema) – the portion of the database
each user views (many views)

Data Manipulation Language (DML)
DML is the proprietary programming language
that a particular DBMS uses to retrieve, process,
and store data to / from the database.
Entire user programs may be written in the
DML, or selected DML commands can be
inserted into universal programs, such as
COBOL and FORTRAN.
Can be used to ‘patch’ third party applications
to the DBMS

Query Language
The query capability permits end users and
professional programmers to access data in the
database without the need for conventional
programs.
Can be an internal control issue since users may be
making an ‘end run’ around the controls built into
the conventional programs
IBM’s structured query language (SQL) is a fourth-
generation language that has emerged as the
standard query language.
Adopted by ANSI as the standard language for all
relational databases

Database Conceptual Models
Refers to the particular method used to
organize records in a database
A.k.a. “logical data structures”
Objective: develop the database efficiently so
that data can be accessed quickly and easily
There are three main models:
hierarchical (tree structure)
network
relational
Most existing databases are relational. Some
legacy systems use hierarchical or network
databases.

The Relational Model
The relational model portrays data in the form
of two dimensional ‘tables’.
Its strength is the ease with which tables may
be linked to one another.
A major weakness of hierarchical and network
databases
Relational model is based on the relational
algebra functions of restrict, project, and join.

RESTRICT – filtering out rows,
such as the dark blue
PROJECT – filtering out columns,
such as the light blue
X1 X1
X2 X2
X3 X3
Y1
Y1
Y1 Y1
Y1
Y2 Y2 Y2
Y3
Z1 Z1
Z2 Z2
Z3 Z1
JOIN – build a new table or data set from multiple existing tables
Relational Algebra

Associations and Cardinality
Association – the labeled line connecting two
entities or tables in a data model
Describes the nature of the between them
Represented with a verb, such as ships, requests, or
receives
Cardinality – the degree of association between
two entities
The number of possible occurrences in one table that
are associated with a single occurrence in a related
table
Used to determine primary keys and foreign keys

“Crow’s Feet” Cardinalities
(1:0,1)
(1:1)
(1:0,M)
(1:M)
(M:M)

Properly Designed Relational Tables
Each row in the table must be unique in at least
one attribute, which is the primary key.
Tables are linked by embedding the primary key
into the related table as a foreign key.
The attribute values in any column must all be of
the same class or data type.
Each column in a given table must be uniquely
named.
Tables must conform to the rules of
normalization, i.e., free from structural
dependencies or anomalies.

Three Types of Anomalies
Insertion Anomaly: A new item cannot be
added to the table until at least one entity uses a
particular attribute item.
Deletion Anomaly: If an attribute item used by
only one entity is deleted, all information about
that attribute item is lost.
Update Anomaly: A modification on an attribute
must be made in each of the rows in which the
attribute appears.
Anomalies can be corrected by creating
additional relational tables.

Advantages of Relational Tables
Removes all three types of
anomalies
Various items of interest
(customers, inventory, sales) are
stored in separate tables.
Space is used efficiently.
Very flexible – users can form ad
hoc relationships

The Normalization Process
A process which systematically splits
unnormalized complex tables into smaller
tables that meet two conditions:
all nonkey (secondary) attributes in the table are
dependent on the primary key
all nonkey attributes are independent of the other
nonkey attributes
When unnormalized tables are split and reduced to
third normal form, they must then be linked
together by foreign keys.

Steps in Normalization
Unnormalized table with
repeating groups
First normal
form 1NF
Second normal
form 2NF
Third normal
form 3NF
Higher normal
forms
Remove
repeating
groups
Remove
partial
dependencies
Remove
transitive
dependencies
Remove
remaining
anomalies

Accountants and Data Normalization
Update anomalies can generate conflicting and
obsolete database values.
Insertion anomalies can result in unrecorded
transactions and incomplete audit trails.
Deletion anomalies can cause the loss of
accounting records and the destruction of audit
trails.
Accountants should understand the data
normalization process and be able to determine
whether a database is properly normalized.

Six Phases in Designing Relational
Databases
1. Identify entities
• identify the primary entities of the
organization
• construct a data model of their
relationships
1. Construct a data model showing
entity associations
• determine the associations between
entities
• model associations into an ER diagram

3. Add primary keys and attributes
• assign primary keys to all entities in the
model to uniquely identify records
• every attribute should appear in one or more
user views
3. Normalize and add foreign keys
• remove repeating groups, partial and
transitive dependencies
• assign foreign keys to be able to link tables
Databases

5. Construct the physical database
• create physical tables
• populate tables with data
5. Prepare the user views
• normalized tables should support all
required views of system users
• user views restrict users from have access
to unauthorized data
Databases

Distributed Data Processing (DDP)
Data processing is organized around several
information processing units (IPUs) distributed
throughout the organization.
Each IPU is placed under the control of the end
user.
DDP does not always mean total decentralization.
IPUs in a DDP system are still connected to one
another and coordinated.
Typically, DDP’s use a centralized database.
Alternatively, the database can be distributed,
similar to the distribution of the data processing
capability.

Distributed Data
Processing
Site CSite BSite A
Centralized
Database
Central
Site

The data is retained in a central location.
Remote IPUs send requests for data.
Central site services the needs of the remote IPUs.
The actual processing of the data is performed at the
remote IPU.
Centralized Databases in DDP
Environment

Advantages of DDP
Cost reductions in hardware and data entry tasks
Improved cost control responsibility
Improved user satisfaction since control is closer
to the user level
Backup of data can be improved through the use of
multiple data storage sites

Disadvantages of DDP
Loss of control
Mismanagement of resources
Hardware and software incompatibility
Redundant tasks and data
Consolidating incompatible tasks
Difficulty attracting qualified personnel
Lack of standards

Data Currency
Occurs in DDP with a centralized database
During transaction processing, data will
temporarily be inconsistent as records are
read and updated.
Database lockout procedures are
necessary to keep IPUs from reading
inconsistent data and from writing over a
transaction being written by another IPU.

Distributed Databases: Partitioning
Splits the central database into segments
that are distributed to their primary users
Advantages:
users’ control is increased by having data stored
at local sites
transaction processing response time is
improved
volume of transmitted data between IPUs is
reduced
reduces the potential data loss from a disaster

The Deadlock Phenomenon
Especially a problem with
partitioned databases
Occurs when multiple sites lock each other
out of data that they are currently using
One site needs data locked by another site.
Special software is needed to analyze and
resolve conflicts.
Transactions may be terminated and restarted.

The Deadlock Phenomenon
A,B
E, F
C,D
Locked A, waiting for C
Locked C, waiting for E
Locked E, waiting for A

Distributed Databases: Replication
The duplication of the entire
database for multiple IPUs
Effective for situations with a high
degree of data sharing, but no
primary user
Supports read-only queries
Data traffic between sites is
reduced considerably.

Concurrency Problems and Control
Issues
Database concurrency is the presence of
complete and accurate data at all IPU sites.
With replicated databases, maintaining
current data at all locations is difficult.
Time stamping is used to serialize
transactions.
Prevents and resolves conflicts created by updating
data at various IPUs

Distributed Databases and the
Accountant
The following database options impact the
organization’s ability to maintain database integrity,
to preserve audit trails, and to have accurate
accounting records.
Centralized or distributed data?
If distributed, replicated or partitioned?
If replicated, totally or partially replication?
If partitioned, what allocation of the data segments
among the sites?

James hall ch 9

More Related Content

What's hot (20)

Viewers also liked (14)

Similar to James hall ch 9 (20)

Recently uploaded (20)

James hall ch 9

Editor's Notes