1. Chapter 5 – Database Management Systems
Accounting Information Systems 8e
Ulric J. Gelinas and Richard Dull
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2. Learning Objectives
Describe the limitations of traditional application approaches to managing data.
Analyze the advantages gained by using the database approach to managing data.
Create normalized tables in a relational database.
Use entity-relationship diagrams in database design and implementation.
Explain the importance of advanced database applications in decision support and knowledge management. 2

3. Introduction to Databases
The enterprise database is at the heart of Accounting Information Systems (AIS).
The chapter discusses the major types of databases available and how organizations undertake database design for AIS.
Larger organizations store information in data warehouses in ways that let managers analyze it to gain important insights.
Many companies combine their data resources with decision support systems, executive information systems, group decision systems, and other advanced technology- based systems to improve decision making and operations.

4. Two Approaches To Business Event Processing
Applications Approach
Concentrates on the process being performed.
Data support the role of the programs that run in each application system.
Each application collects and manages its own data in separate, distinguishable files.
Data redundancy can cause inconsistencies among the same data in different files when information is updated in one file but not the others.
Storage costs are increased because the system must store and maintain multiple versions of the same data in different files.
Data residing in separate files are not shareable among applications.
Database Approach
Facts about events are stored in relational database tables instead of separate files.
Solves many of the problems caused by data redundancy.
Improves the efficiency of processing data by eliminating data redundancies and improving data integrity.
Enables the creation of integrated business information systems that include data about all of a company’s operations in one massive collection of relational tables.
Multiple users from throughout the organization can view and aggregate event data in a manner most conducive to their needs.

5. Two Approaches To Event Processing

6. Record Layouts under the Applications Approach to Business Event Processing

7. Problems with Applications Approach
Data redundancy - files stored may include redundant information increasing storage requirements and the risk of inconsistency.
Data in files is not shareable across applications because applications depend on a fixed record layout in data files.

8. Record Layout – Applications Approach
Each application stores all the data required for analysis.
Shows many redundancies across applications/files.
Data lacks integrity when the data stored by one application is inconsistent with data stored by another application.

9. Database Management Systems
A set of integrated programs designed to simplify the tasks of creating, accessing, and managing data.
Integrates a collection of files that are independent of application programs and are available to satisfy a number of different processing needs.
Database contains data related to all the organization’s applications in one place.
Supports normal data processing needs and enhances the organization’s management activities by providing data useful to managers.
Data is independent of the application that created the data (i.e., can be changed/used by other applications).
The term enterprise database is synonymously with database management system or DBMS.

10. Schemas and Subschemas

11. Relational Database Tables
The next slide shows a database with data stored in a relational structure.
This is most common type of database structure used in businesses today.
The data from three files are now stored in four tables:
CUSTOMERS (instead of the customer master data file)
INVENTORY_ITEMS (instead of the inventory master data file)
SALES_ORDERS
SALES_LINES (replace the sales order master data file)
The logical database view is how the data appear to the user to be stored.
This view represents the structure that the user must use to extract data from the database.

12. Record Layouts as Tables

13. Formulating a Query in SQL
Users can access the data in the tables
by:
Formulating a query.
Preparing a report using a report writer.
Including a request for data within an application program.

14. SQL – Query Example SELECT Cust_Code Cust_Name Cust_City
A query that uses the SQL SELECT command can return to the customers assigned to salesperson Garcia.
SELECT Cust_Code Cust_Name Cust_City
FROM CUSTOMERS
WHERE Salesperson = ‘Garcia’
There are two customers, STANS and WHEEL, who are assigned to salesperson Garcia.

15. Disadvantages of DBMS Expensive to implement.
If the DBMS fails, all the organization’s information processing halts.
Database recovery and contingency planning are more important than in the applications approach.
When more than one user attempts to access data at the same time, the database can face “contention” or “concurrency” problems.
Record locking can help mitigate such problems but are beyond the scope of this course.
Territorial disputes over who “owns” the data, such as who is responsible for data maintenance (additions/deletions/changes) to customer data.
Most companies that have adopted this approach have found it necessary to create a database administrator function to cope with these and other problems.

16. Logical Database Models
Hierarchical databases
Child records may only have one parent record.
Cannot sustain complex data structures.
Network databases
Overcomes problems of hierarchical.
Child record can have more than one parent record.
Eclipsed by relational databases.

17. Logical Database Models
Relational databases
Data logically organized into two dimensional tables.
Vast improvement over hierarchical or network database models.
Able to handle complex queries.
Allows only text and numerical data to be stored.
Does not allow the inclusion of complex object types such as graphics, audio, video, or geographic information.
Object oriented databases
Overcomes the limitations of relational databases.
Allow the storage of complex objects such as video clips.
An object can store attributes and instructions for actions that can be performed on the object or its attributes.

18. Object-Oriented Database Model

19. Elements/Parts of a Relational Database
Tables - place to store data
Queries - retrieve data
Forms - on-screen presentations of data collected by queries
Reports - printed lists and data summaries collected by queries

20. Comparison of Database and Spreadsheet
Cell can contain only one data type.
Each row in the database must be unique and include a unique identifier (primary key or composite key).
Columns store one attribute.
Spreadsheet
Cell can contain text, numbers, formula or graphic.
Rows need not be unique.
Columns often store dissimilar attributes.

21. Classifying and Coding Data
Types of coding
Sequential
Block
Significant digit
Hierarchical
Mnemonic

22. Sequential Coding Assigns numbers in chronological sequence.
Limited flexibility
Additions can be made only at the end of a sequence.
Deletions result in unused numbers unless the numbers are recycled.
Codes tell nothing about the object’s attributes.
Examples include
Student ID numbers
Example based on employee ID codes:
st hired
nd hired

23. Block Coding
Groups of numbers are dedicated to particular characteristics of the objects being identified.
Universal product code example:
Mfg Product
Code Code
Employee ID code example:
fabrication department
assembly department

24. Significant Digit Coding
Assigns meanings to specific digits.
Significant digit coding works well for inventory items.
Also works well for employee ID codes.
The following slide shows examples using inventory and employee ID codes.

25. Significant Digit Coding
Example based on an Inventory item
Product group
Part or subassembly
Unique item identifier
Warehouse
Example based on employee ID codes
Work center
Exempt or nonexempt
Pay rate code
Unique employee identifier

26. Hierarchical Data Coding
Like significant digit codes, hierarchical codes attach specific meaning to particular character positions.
Items are ordered in descending order where each successive rank order is a subset of the rank above it.
Reading from left to right in a hierarchical code, each digit is a subcategory of the digit to its left.
A 5 digit postal code is an example of hierarchical data coding.

27. Hierarchical Data Coding
Example based on postal zip code
Section of country
Region within section
Locality (town within region)
Example based on employee ID codes
Company division
Unique employee ID
Plant
Department

28. Mnemonic Data Coding College course numbering:
AC340 - Accounting Information Systems
EN101 - English Composition
Example based on employee ID codes:
F M C
Female
Married
Caucasian
Unique employee ID

29. Coding Examples

30. Database Normalization
Rules for database normalization based on set theory.
Failure to normalize results in anomalies/errors that otherwise might occur when adding, changing, or deleting data stored in the database.
There are 6 normal forms, but the first 3 are generally considered sufficient.
To function properly, a database should obtain the third normal form.
Normal forms are inclusive, which means that each higher normal form includes all lower normal forms.
That is, a table in 3NF is in 1NF and in 2NF.

31. Unnormalized Relation
Table contains repeating groups
Sales order line items repeat

32. Functional Dependence and Primary Keys
An attribute (column in a table) is functionally dependent on a second attribute (or a collection of other attributes), if a value for the first attribute determines a single value for the second attribute at any time.
If functional dependence exists, the first attribute determines the second attribute.
A primary key contains a value that uniquely identifies a specific row in a table.
A candidate attribute (a column or collection of columns) in a table is that table’s primary key if:
All attributes in the table are functionally dependent on the candidate attribute.
No collection of other columns in the table, taken together, has the first property.

33. Relation in First Normal Form (1NF)
A table is in first normal form (1NF) if it doesn’t contain repeating groups.
Rows are now key dependent, uniquely identified by a primary key.
The primary key for the table is a combination of SO_Number and Item_Number.
A primary key formed by the combination of two or more columns is called a composite primary key.

34. Problems with First Normal Form (1NF)
Includes the following functional dependencies:
Item_Number functionally determines Item_Name. Therefore, item names are repeated several times. This data redundancy should be eliminated.
Cust_Code functionally determines Cust_Name.
The combination of SO_Number and Item_Number together functionally determine Item_Name, Qty_Ordered, Cust_Code, and Cust_Name.
These functional dependencies cause several problems called update anomalies.

35. Update Anomalies
Update. Updating an item name requires updating multiple records. Each row in which any item appears must be changed if the description is updated.
Inconsistent data. An item can have several different names in different rows of the table.
Additions. Adding a new inventory item to the database is problematic because a sales order number is required before an item can be inventoried. This is impossible for a business, that wants information about inventory in its database before accepting sales orders.
Deletions. Deleting an inventory item from the database (by deleting its row) could cause the table to lose sales order information.
Because the attribute, Item_Name is dependent on a portion of the primary key, Item_Number, not on the entire key, a problem called a partial dependency exists. Second normal form eliminates partial dependencies.

36. Two steps to get from 1NF to 2NF
Create a new table for each subset of the table that is partially dependent on a part of the composite primary key.
This step results in two new tables, one with SO_Number as its primary key (SALES_ORDERS table) and another with Item_Number as its primary key (INVENTORY_ITEMS table).
Place each of the non-key attributes that are dependent on a part of the composite primary key into the table that now has a primary key that is the field on which the non-key attribute is partially dependent.
The Item_Name field is partially dependent on the Item_Number field portion of the composite primary key, so it would be moved into the new INVENTORY_ITEMS table.

37. Relations in Second Normal Form (2NF)
To obtain second normal form, partial dependencies must be eliminated by creating new tables.

38. Third Normal Form (3NF)
To obtain third normal form, transitive dependencies must be eliminated.
A transitive dependency exists when a non- key field depends on another non-key field which in turn depends on the key. For example, customer name depends on customer code which depends on sales a order number.
A table is in third normal form if it is in second normal form and transitive dependencies are eliminated.

39. Transitive Dependencies
Some customer names are repeated several times. This transitive dependency causes update anomalies:
Update. Changing any customer name could require multiple changes. The user would have to change each row in which any customer appears. Changing Wheelaway’s name would require changing three rows in the SALES_ORDERS table.
Inconsistent data. Nothing in this design prevents users from entering several different names for a single customer.
Additions. A new customer can’t be added unless the customer already has a sales order. Internal control dictates that an authorized customer should exist before a sales order can be created for that customer.
Deletions. If a user deletes a sales order, the name of a customer might be erased from the database.

40. Relations in Third Normal Form (3NF)
Customer information moved to “Customers” table

41. Entity-Relationship Models (REA)
Popular data modeling approach.
Entities and relationships are determined through systems analysis.
Common accounting entities include:
Resources: thing the company owns.
Events: occurrences related to resources.
Agents: people or organizations that participate in events.

42. Entity-Relationship Models (REA)
Entity-relationship diagram
Entity-Relationship Models (REA)

43. Entity Relationship Models (REA)
Identify Entities
Categories of entities
Resources
Events
Agents
Locations
Identify relationships that connect the entities
shown in the E-R diagram as connecting lines with diamonds that describe the nature of the relationship.
Create tables and relationships
the analyst continues the data modeling process transforming the data model into a logical design for the database.

44. Characteristics of Relationships
Relationships between entities are determined by analyzing the system.
The cardinality is the degree to which each entity participates in the relationship, e.g., 1:N (pronounced one-to-many).

45. Create the E-R Diagram in 5 Steps
Create a logical model of the database:
Create tables for each entity.
Determine primary keys.
Determine attributes.
Implement relationships through primary keys and relationships.
Define relationship tables.
Implement the database using an available DBMS.

46. Relational Database

47. Decision Support Systems: DSS
Information systems that assist managers with unstructured decisions by retrieving data and generating information.
Possesses interactive capabilities.
Can answer ad-hoc inquires.
Provide data modeling facilities such as spreadsheets.
Supports non-recurring, relatively unstructured decision making.

48. Executive Information System: ESS
Information systems, often considered a subset of DSS, that combine information from the organization and the environment.
Organizes and analyzes the information in a form suitable for managers to make decisions.

49. Decision Support Systems and Executive Information Systems

50. Group Support Systems: GSS
Computer based systems that support collaborative intellectual work such as
Idea generation
Elaboration
Analysis
Synthesis
Decision making
GSS use technology to solve the time and place dimension problems associated with group work.
Also known as GDSS or Group Decision Support Systems.

51. Expert Systems
Expert systems (ES): information system that emulates the problem solving techniques of human experts.
Neural networks (NN): computer hardware and software systems that mimic the human brain’s ability to recognize patterns or predict outcomes using less-than complete information.

52. Excel Decision Model Example

53. Intelligent Agents
An intelligent agent is a software program that may be integrated into DSS, EIS or other software tools (such as word processing, spreadsheet, or database packages).
Sometimes called “bots,” short for “robots.”

54. Data Warehouses
Data warehousing is the use of information systems to collect, organize, integrate, and store entity-wide data to provide users with easy access to large quantities of varied data from across the organization.
Data mining is the exploration, aggregation, and analysis of data in the data warehouse using analytical tools and exploratory techniques.

55. Knowledge Management Systems
Knowledge management is the process of capturing, storing, retrieving, and distributing the knowledge of the individuals in an organization for use by others in the organization to improve the quality and efficiency of decision making across the firm.
Knowledge refers to information that has been formatted and distributed in accordance with an organization’s standards.