Transaction processing systems
A transaction is a record of an event that signifies a business exchange
A transaction processing system is a basic business system that support the
functions of recording, monitoring, and evaluating the basic activities of the
business (Figure 13.1)
Manufacturing -- purchasing, receiving, shipping, materials, labor costing,
equipment, quality control, process control, NC machines, robotics, inventory
Marketing -- sales, telemarketing, order processing, point-of-sale systems,
credit authorization
Finance/Accounting -- accounts receivable, accounts payable, general ledger,
payroll, cash management, loan processing, check processing, securities trading
Human resources -- personnel record keeping, applicants tracking, positions
listing, training and skills, benefits
Office automation systems
Data work involves the use, manipulation, or dissemination of information
Knowledge work involves the creation of new information that requires
independent judgment and creativity (Figure 14.7)
Office work involves the coordination and integration of workers in different
functional areas of a firm
An office automation system is any application of information technology
that increases the productivity of office workers:
document management
word processing
desktop publishing
communication
scheduling
data management
project management
4 functions of management: planning, organizing, leading, controlling
(Figure 16.2)
3 roles of a manager: interpersonal, informational, decisional (Table 16.1)
3 types of management support systems
MIS: summarize and report on the basic operations of a company to support
solution of structured problems (Fig.16.5)
DSS: provide data and models interactively to support the discussion and
solution of semistructured problems (Fig. 16.7)
EIS: serve the information needs of managers at the highest organizational
levels by combining data from both internal and external sources to support
solution of unstructured problems (Fig. 16.9)
In-class activities:
Answer each of the following questions with respect to each of the business
information systems that you learned in this class:
Where does the system obtain its data?
What does the system do with the data?
What problems does the system solve?
What difference does the system make?
Artificial intelligence (Fig.15.1)
Components of an expert system:
Knowledge base (production rules)
Inference engine (forward and backward chaining)
User interface (expert system shell)
In-class activity: pg.594 ex.#1
Hands-on activity: pg.594 ex.#3
Knowledge Reasoning
Forward Chaining Data à Goal
e.g., Knowing that Exam.2 is on December 18
How to prepare for it?
Backward Chaining Goal à Data
e.g., Reaching the goal: Get an A in INFO 902
What needs to be done?
E.g., Rules:
Rule1: IF client is an engineer, doctor, or lawyer
THEN client belongs to a high paying
profession.
Rule2: IF client is a successful business owner
THEN client has high earnings.
Rule3: IF client belongs to a high paying profession
THEN client has high earnings.
Rule4: IF client has high earnings
THEN client is a low credit risk.
Rule5: IF client has had credit for less than 3
years
THEN client’s credit history is very
low
Rule6: IF client’s credit history is very low
AND client has been unemployed for more than
half of his/her adult years
THEN client has a high credit risk.
Fact1: Sue is 25 years old
Fact2: Sue has 1 year of credit history
Fact3: Sue has been unemployed for 3/5 of her adult years
Fact4: Sue is a dentist
|
Forward Chaining: |
Backward Chaining: |
|
Facts: Fact2 & Fact3 |
Goal: Is Sue’s credit risk high? |
|
Fact2 Matches Rule5 New Fact1: Sue’s credit history is very low New Fact1 & Fact3 Matches Rules6 |
Goal Matches Rule6 New Goal1: Is Sue’s credit history very low? New Goal2: How long has Sue been unemployed? New Goal2 Matches Fact3 New Goal1 Matches Rule5 New Goal3: How long has Sue had credit? New Goal3 Matches Fact2 |
|
Conclusion: Sue has a high credit risk |
Conclusion: Sue’s credit risk is high |