1 information systems rafiqul islam software project manager edicte tech solutions

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INFORMATION SYSTEMS

Rafiqul IslamSoftware Project Manager

eDicte Tech Solutions

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Module-1

INFORMATION SYSTEMS – AN INTRODUCTION

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INFORMATION SYSTEMS

A definition of information systems is: Interrelated components working together to collect, process, store, and disseminate information to support decision-making, coordination, control, analysis, and visualization in an organization.

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DATA AND INFORMATION

DATA Raw material of information. Groups of nonrandom symbols that

represent quantities, actions, objects, etc. in the from of text, image, or voice.

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DATA AND INFORMATION

INFORMATION Processed data in meaningful form to the

recipient Of real or perceived value in the current

or prospective actions and decisions Having reusable resources A surprise or news value that reduces

uncertainty

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PHYSICAL SYSTEMS AND IS

Example: Inventory Monitoring System Information system: Flow of data such as

material requests, purchase requests, material delivery advices, and so on.

Physical system: Actual flow of materials from the stores to the production shops or from the suppliers to the stores.

Associated will be the materials handling systems that need to be optimized.

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PHYSICAL AND INFO. FLOWS

Information Flow

Supply Chain

Raw Materials

Finished Goods

Material

Manpower EquipmentMoney

Physical Flow

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FEEDBACK AND INFO. FLOWS

Info. Flow

Information Flow

ProcessInput Output

Physical Flow

Desired Output

Control Signal

Information Flow

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FEEDBACK AND INFO. FLOWS• Negative feedback loop control gives a

new idea about the definition of information.

• Information can be defined as a control signal or an error signal that can help make vital decisions about controlling the inputs of a physical system.

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CATEGORIES OF INFO. SYSTEMS

• Application software :: classical management information systems

• Real time software :: missile defense systems • Systems software :: operating systems • Embedded software :: radar navigation

packages • Communications software :: telephone

switching systems • Process control software :: refinery drivers.

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APPLICATION SOFTWARECriteria Non-Application

SoftwareApplication Software

Data Less Data Huge Data

Input Less input - usually organized

Huge input. Need effort to i) Collect ii) Organize iii) Maintain steady flow

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APPLICATION SOFTWARECriteria Non-Application

SoftwareApplication Software

Processing

High Processing of Data

Although less per record but it is high for all records

Algorithms

Large Number of Algorithms with high complexity

Less Number of Algorithms with less complexity

Output Less output - easy to organize

Huge output. Need effort to Organize and Disseminate

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APPLICATION SOFTWARE• Strong customer focus.

• Understand customer requirements

• Implement appropriate solution to integrate the business processes of the customer.

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APPLICATION SOFTWARECOMPLEXITIES OF DATA

Complexities of input data need be resolved

right at the requirements analysis stage. Many a time, analysts tends to underestimate

the complexities of a system The result is often a poorly designed software

Later, during implementation, the customer

brings in the complexities one after another and the software need to be redeveloped.

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CASE STUDYA SIMPLE INTEREST FORMULA IS ALL WE

NEED HERE!

When PQR industrial development corporation invited ABC consultants for developing a software for tracking its term loans - it all seemed too easy to the system analyst - Rohit Sarawagi.

Rohit, a fresh MBA from IIT Kharagpur, spent a few hours time with Mr. Patil, GM, Term Loans. "All we need here is a simple interest formula!" - exclaimed Rohit to his colleagues during a tea-time discussion.

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CASE STUDYTerm Loans are usually offered by PQR to

aspiring industrialists who need to pay back the loan in a number of instalments.

Schedules are usually prepared for the payment of the principal amount as well as the interests.

PQR wants ABC to develop a software for easy tracking of the term loans sanctioned to the borrowers.

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CASE STUDYAtul Pradhan, a colleague of Rohit, was not too

amused. He pointed out to Rohit that it is not always that the borrowers of Term Loans pay back in time.

PQR has to then charge interests on interests, known as penal interests, in addition to the normal payment charges.

And do they pay the penal interests on time? - Usually not, says Atul, the borrowers often ask for rescheduling of payments - often falter the new schedule as well, and invariably ask for rescheduling of the reschedule!

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CASE STUDYWhen Rohit met Mr. Patil the next day, Mr

Patil said - did I mention you about the court cases yesterday?

What about court cases? Oh, some naughty borrowers move to court

challenging the amount to be paid. Then we have a figure to be paid according to us, a figure as per them, and possibly a third figure negotiated by the court!

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CASE STUDYAnd do we have the rescheduling of the

negotiated payment figure of the rescheduled penal interest as well?

Why not? We may as well negotiate that in the court!

A SIMPLE INTEREST FORMULA IS ALL WE NEED HERE!

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Module-2

INFORMATION SYSTEMCLASSIFICATIONS

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STRUCTURE OF INFORMATION SYSTEMS

Based on Operating ElementsPhysical Components:

• Hardware• Software• Database• Procedures• Operating Personnel

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Based on Operating ElementsProcessing Functions:

• process transactions• maintain master files• produce reports• process inquiries• interactive support

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Based on Operating ElementsOutputs for users:

• transaction documents• preplanned reports• preplanned inquiry responses• ad-hoc reports• ad-hoc inquiries• user-machine dialogue results.

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Based on Decision Support

• Structured Programmable Decisions• Unstructured un-programmable decisions

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Based on Management Hierarchy

Level of Management

Function

Top/Senior Management

Long-range decisions Strategic Planning

Middle level Management

Carrying out plans and goals specified by the top management,

Management Control and Tactical Planning

Knowledge Management

Knowledge work Data work

Operational Management

Monitoring day-to-day activities

Operational Planning and Control

Transaction Processing

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STRUCTURE OF INFORMATION SYSTEMS Based on Organizational Functions Sales and Marketing Manufacturing Finance Accounting Human Resources Logistics Information Processing

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MIS TRIANGLE

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TPS: TRANSACTION PROCESSING SYSTEM

A computerized system that performs and records the daily routine transactions in the conduct of a business.Information Inputs: Transactions, EventsProcessing: Sorting, Listing, Merging, UpdatingInformation Outputs: Detailed Reports, Lists, SummariesUsers: Operations Personnel, Supervisors

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TPS: TRANSACTION PROCESSING SYSTEM

Examples

Order Tracking and Processing, Machine Control

Cash Management, Payroll, Account Payable

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OAS: OFFICE AUTOMATION SYSTEM

OAS serves the information needs at the knowledge level of an organization to aid data workers.

Information Inputs: Documents, Schedules Processing: Documents, Management,

Scheduling, Communication

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OAS: OFFICE AUTOMATION SYSTEM

Information Outputs: Documents, Schedules,

Mails Users: Clerical Workers

Examples Word Processing, Image Storage, E-

Calendars

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KWS: KNOWLEDGE WORK SYSTEM

KWS serves the information needs at the knowledge level of an organization to aid knowledge workers.

Information Inputs: Design Specifications, Knowledge Base

Processing: Modeling, Simulation

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KWS: KNOWLEDGE WORK SYSTEM

Users: Professionals, Technical Staff Examples

Knowledge Work Analysis, Engineering Workstations

Graphic Workstations, Managerial Workstations

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MIS: MANAGEMENT INFORMATION SYSTEM

MIS primarily serves planning, controlling, and decision-making at the management level.

Characteristics of MIS Reporting and Control Oriented Rely on existing corporate data and data flows Have little analytical capability Aid decision-making using past and present data

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Characteristics of MIS (continued) Relatively inflexible Internal rather than external orientation Information requirements are known and stable Development time is usually one to two years.

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Information Inputs: Summary Transaction Data, High Volume Data, Simple Models

Processing: Routine Reports, Simple Models, Low Level Analysis

Information Outputs: Summary and Exception Reports

Users: Middle-level Managers

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Examples

Sales Management, Inventory Control Annual Budgeting, Capital Investment

Analysis Relocation Analysis

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DSS: DECISION SUPPORT SYSTEM

DSS supports decision-making of the management level of an organization.

DSS Characteristics Incorporate both data and models Assist in decision processes that are semi-

structured, unique, or rapidly changing. Support, not replace, managerial judgement Improve the effectiveness and not

efficiency of making decisions.

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Information Inputs: Low Volume Data, Analytical Models

Processing: Interactive, Simulation, Analysis

Information Outputs: Special Reports, Decision Analysis, Responses to Queries

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Users: Professionals, Staff Managers Examples

Sales Region Analysis, Production Scheduling

Cost Analysis, Pricing and Profitability Analysis

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ESS: EXECUTIVE SUPPORT SYSTEM

ESS serves senior managers of an organization for the support of strategy level decision-making.

Information Inputs: External and Internal Aggregate Data

Processing: Graphics, Simulation, Interactive

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ESS: EXECUTIVE SUPPORT SYSTEM

Information Outputs: Projections, Responses to Queries

Users: Top/Senior Managers Examples

Sales Trend Forecasting, Operating Plan

Budget Forecasting, Profit Planning

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Module-3

INFORMATION SYSTEMS FOR COMPETITIVE

ADVANTAGE

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USING INFO SYSTEMS FOR COMPETITIVE ADVANTAGE

THEFIRM

TraditionalCompetitors

NewMarketEntrants

Suppliers Customers

SubstituteProducts /Services

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BASIC STRATEGIES IN COMPETITIVE FORCES MODEL

Product Differentiation: Creating unique new products and services that can be easily distinguished

Focused Differentiation: Creating new market niches by identifying a specific target for a product/service

Developing tight linkages to customers and suppliers: Creating ties with customers and suppliers to “lock” customers and tie suppliers

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BASIC STRATEGIES IN COMPETITIVE FORCES MODEL

Becoming the low cost producer: Producing goods and services at a lower price than competitors without sacrificing quality and level of service.

• Information systems can make them competitive

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LINKING WITH CUSTOMERS & SUPPLIERS

1. Prevailing Delivering Practice at Hospitals

Suppliers Inventory Delivery Hospital Hospital(Bulk Storage) System Storeroom Wards

2. Just-in-Time Supply Method

Suppliers Inventory More frequent Hospital Hospital(Bulk Storage) Deliveries Storeroom Wards

3. Stockless Supply Method

Suppliers Inventory Daily Deliveries Directly Hospital(Bulk Storage) to the Hospital Wards Wards

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ORGANIZATIONS AND INFORMATION TECHNOLOGY

Organizations InformationTechnology

Mediating Factors Environment Culture Structure Standard Procedures Politics Management

Decisions Chance

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MANAGEMENT, IT AND ORGANIZATION

BUSINESSSOLUTIONS

MANAGE-MENT

INFORMA-TIONTECHNOLOGY

BUSINESSCHALLENGES

INFORMATIONSYSTEM

ORGANIZA-TION

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CHALLENGES OF INFORMATION SYSTEMS

The strategic business challenge: How can businesses use information technology to design competitive and effective organizations?

The globalization challenge: How can firms understand the business and system requirements of a global economic environment?

The information architecture challenge: How can organizations develop an information architecture that supports their business goals?

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The information system investment challenge: How can organizations determine the business

value of information systems?

The responsibility and control challenge: How can organizations design systems that

people can control and understand? How can organizations ensure that the

information systems are used ethically and in a socially responsible manner?

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ORGANIZATIONS AND INFORMATION TECHNOLOGY

Organizations InformationTechnology

Mediating Factors Environment Culture Structure Standard Procedures Politics Management

Decisions Chance

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MANAGEMENT, IT AND ORGANIZATION

BUSINESSSOLUTIONS

MANAGE-MENT

INFORMA-TIONTECHNOLOGY

BUSINESSCHALLENGES

INFORMATIONSYSTEM

ORGANIZA-TION

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The strategic business challenge: How can businesses use information technology to design competitive and effective organizations?

The globalization challenge: How can firms understand the business and system requirements of a global economic environment?

The information architecture challenge: How can organizations develop an information architecture that supports their business goals?

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The information system investment challenge: How can organizations determine the business value of

information systems?

The responsibility and control challenge: How can organizations design systems that people can

control and understand? How can organizations ensure that the information systems

are used ethically and in a socially responsible manner?

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Module-4

CONCEPT OFINFORMATION

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CONCEPT OF INFORMATION

Information • Information is data that has been

processed into a form that is meaningful to the recipient and is of real or perceived value in current or prospective actions/decisions.

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CHARACTERISTICS OF INFORMATION

• It is Reusable• Does not lose value over time• May give value by addition of credibility

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DIMENSIONS IN THE USE OF INFORMATION

• Information Presentation• Information Transmission• Information Interpretation• Information Use

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TRANSMISSION OF INFORMATION

Source Transmitter Encoder

Channel Receiver Decoder

Destination

Noise and Distortion

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INFORMATION INTERPRETATION

• Information Reduces Uncertainty • Even partial information may assist in

understanding. • Information has a ‘surprise’ or ‘news’ value.

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INFORMATION REDUCES UNCERTAINTY

Example: • In an interview, there are 10 equally likely

possible candidates. So, Information content of positively identifying the best candidate is:

• I = log210 = 3.32.• A message comes that only 4 candidates have

cleared the preliminary round.

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INFORMATION REDUCES UNCERTAINTY

• So, New Information content: • I = log24 = 2.0.

• Thus, Information value of the partial information (message):

• 3.32 – 2.0 = 1.32.

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REDUNDANCY IN INFORMATION

• Sometimes redundancy is very useful in information transmission.

• Example:• This cla** is held at SOM Build**g at 13.0*

ho**s.

• It is not necessary to decode every letter of the message.

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• Human eyes are so much accustomed to observing redundancy in information, that sometimes Redundancy may be misleading:

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• Often, we may read the above as INDIA, but in reality, it may be just this!!

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• Hence, One should be careful about possible introduction of errors!

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INFORMATION PRESENTATION

• Interpretation of received messages are subject to misinterpretation /misunderstanding.

• The capacity of human to process information is limited (Information Overload).

• Methods to increase the sending/receiving efficiency of a system are needed.

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CASE STUDY:: E-BUSINESS AT DOMINO

• DOMINO as in Domino's Pizza• A worldwide enterprise with 5,600 stores and 18

distribution and supply centers, • Domino's relies on its 1,200 independent franchise

chain learned how to deliver timely information as well.

• Objective: Getting information to the right people in the right order.

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E-BUSINESS AT DOMINO• Domino's relies on its franchisees all over the

world to carry the company's commitment through to customers.

• A small headquarters staff in Ann Arbor, Michigan, manages the performance of each franchise directly, through travelling consultants and through field managers.

• Delays in reporting procedures led to franchise owners getting conflicting information from different sources, which in turn was upsetting the pizza chain's strict quality control efforts.

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E-BUSINESS AT DOMINO• The company needed a solution that would let

mobile managers document their plans with franchisees and simultaneously share the results with headquarters.

• Opportunity: Domino's knew the solution was to go online.

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E-BUSINESS AT DOMINO• A customized groupware application could eliminate

duplication of efforts and keep networked users informed.• A global intranet would provide an opportunity to

automate critical business processes, like financial reporting.

• Franchise managers could upload their performance information to Domino's central server and view how they rank against the worldwide operations.

• Challenges: Serving timely information to a thousand hungry clients.

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E-BUSINESS AT DOMINO• Ultimately, Domino's intranet would give 1,000

employees access to more than 50 applications and over 2,000 Web pages.

• The company needed a system easy to set up and maintain, yet scalable to add functionality as new applications and users came online.

• Domino's needed a system with security features to control exactly what information could be viewed by whom.

• The need was immediate – no time to look for the perfect solution for years together.

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E-BUSINESS AT DOMINO• Adding ingredients one at a time, Domino's chose

a spreadsheet-based groupware application with a Web server.

• The company phone directory and newsletter were the first to go online, while its core application, Contact Log, was being adapted from off-the-shelf sales force automation software.

• Contact Log tracks interactions between field consultants and owners, and stores associated documents. It is supported by interactive product information database.

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E-BUSINESS AT DOMINO• The second phase of Domino's intranet

development includes an online financial reporting system, linking directly to the company's data warehouse.

• Domino's Pizza envisions dozens of future phases, which will continue to add more mission critical applications to the intranet.

• • Results: Any way you slice it, Domino's is saving

time and money.

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E-BUSINESS AT DOMINO• Domino's applications allow employees to check

calendars, view policies, and download mission critical information.

• The system contains a detailed document library for an easy access to a broad range of company information, such as corporate accounting procedures and calendar.

• The most popular application on the intranet is an online discussion forum that covers topics from product distribution issues to human resources management.

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INFORMATION INTERPRETATION

• Information Reduces Uncertainty

• Even partial information may assist in understanding.

• Information has a ‘surprise’ or ‘news’ value.

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INFORMATION PRESENTATION

Discretion on Info. Content & Distribution• Objective: Avoid undesirable effects & reduce

workloads.• Message Delay: Avoid overload.• Message Filtering or modification: Modify by

summarization, and Block certain data by filtering.

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DISCRETIONS ON INFO. CONTENT & DISTRIBUTION

• Uncertainty bias: Reduce data transmission. Remove recipient from the contact of detailed data.

• Presentation Bias: Bias by order and grouping. Bias by selection of the limits, Bias by the selection of the Graphic layout (Choice of Scale/Graphics/Size/Color).

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PRESENTATION BIAS• Order and Grouping: Presentation Bias can be

introduced by a suitable choice of order and grouping, such as Alphabetic Order, Order by rate of return, Order by rate of return within industry.

• Choice of limits: Use of too low or too wide limits can bias the viewer.

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PRESENTATION BIAS• Choice of Graphics: • Choice of Scale: to affect the perception of

differences in trend charts.• Choice of Graphic: Visual difference comparison

is difficult with trend charts, relatively easy with superimposed lines, and in-between for bar charts.

• Choice of Size: to minimize differences• Choice of Color: Red to draw attention

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QUALITY OF INFORMATION• Information may be presented and transmitted

efficiently and interpreted correctly, but it may not be used effectively.

• Quality of information is determined by how it motivates human action and contributes to effective decision-making.

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DECISION-MAKER PERCEPTION

Decision-maker Perception for Quality of Information

• Utility of Information• Information Satisfaction• Error and Bias

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UTILITY OF INFORMATION• Form utility• Time Utility• Place Utility (Physical Accessibility)• Possession Utility (Organization Location)

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COST & VALUE OF INFORMATION

• Information has a cost and a value• One can increase value by increasing accuracy and

utility• One can reduce cost by decreasing accuracy and

utility

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INFORMATION SATISFACTION

• Contribution of a particular item of information is difficult to find in the context of improvement in decision-making.

• So measure the degree of satisfaction of the decision-maker with the output of the information system.

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ERROR AND BIAS• High quality rather that quantity of information is

needed• Error and Bias reduce the quality• Bias is caused by the ability of the individuals to

exercise discretion in information presentation• Detected bias can be adjusted• Errors cannot be adjusted by the

decision maker.

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ERROR CAUSES• Incorrect Data measurement and collection• Wrong processing procedures• Less or non processing of data• Wrong recording/correcting• Incorrect master file• Mistakes in procedure• Deliberate Falsification

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AVOIDING ERRORS

• Internal control• Internal/External Auditing• Addition of confidence limits• User instructions in measuring or processing of

data

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BIAS• Handled by procedures to detect and measure it

and to adjust for it.• Bias in Information may occur in:

• Data Acquisition• Processing of Information• Related to Output• Related to Feedback

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BIASES RELATED TO DATA ACQUISITION

• Availability Bias: Frequency of well-publicized events are usually overestimated.

• Selective Perception: Own experience bias – what one expected to see. People usually downplay or disregard conflicting evidence.

• Frequency Bias: Absolute number of successes are more important than their relative number.

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BIASES RELATED TO DATA ACQUISITION

• Concrete Information Bias: People rely more on concrete Information rather than on statistical.

• Illusory correlation: People usually choose inappropriate variables for prediction.

• Data Presentation Bias: Order effects/ mode of presentation /mixture of qualitative and quantitative/logical data display.

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INFORMATION PROCESSING BIASES

• Inconsistency: People are sometimes inconsistent in their processing of information.

• Conservation: Decision-makers are often conservative.

• Non-linear extrapolation: Decision-makers are unable to visualize exponential growth/decay (which may be non-linear and dramatic!)

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Heuristics to reduce mental efforts: • Rule of thumb• Anchoring and adjustment• Representativeness• Law of small numbers• Justifiability

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Bias Due to Decision Environment: • Complexity• Emotional Stress• Social Pressure

Bias from Information Sources • Consistency• Data Presentation

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BIASES RELATED TO OUTPUT

• Response Mode• Question Format• Scale Effects• Wishful thinking• Illusion of control

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BIASES RELATED TO FEEDBACK

Outcome irrelevant learning structure: • Personnel selection: No information of the

rejected candidates• Gambler’s Fallacy: Misperception of chance

Fluctuations• Success/Failure Attribution: Success to Hard

work/ Failure to Hard Luck.

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BIASES RELATED TO FEEDBACK

• Logical Fallacy in Recall: Eyewitness testimony may be wrong!

• Hindsight Bias: Decision-makers are usually not surprised about past happiness or good results, they can always find plausible explanations for them.

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Module-5

SOFTWARE DEVELOPMENT MODELS

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Software Development Life Cycle

Planning

Analysis

Design

Implementation

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Waterfall Models Planning + Feasibility

Requirement Analysis

System Analysis

System Design

Testing + Implementation

Coding

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Prototyping Identify Basic Information Requirements: Basic NeedsScope of ApplicationEstimated Cost

InitialPrototype

UserSatisfied ?

Develop the Initial Prototype

Use Prototype and Refine Requirements

OperationalPrototype

WorkingPrototype

EnhancedWorking

Prototype

NoYes

Revise and EnhancePrototypeUse Prototype as

Application Specification

Use Prototype as Application

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Software Development Models

Waterfall Models• Lack of iteration• Poor requirement gathering• Phase containment of errorsPrototyping• Quick user review• Quick development• Low development discipline

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Software Development Models

Rapid Application Development• Phased Development• Prototyping• Throwaway Prototyping

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Spiral Model

Planning Risk Analysis

CustomerEvaluation

Engineering

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The Planning Stage• Project Initiation• Feasibility Analysis• Project Management

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Feasibility Analysis• Technical Feasibility• Economical Feasibility• Organizational Feasibility

Project Management• Work Plan• Scheduling• Staffing• Controlling

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BPA/BPI/BPRBusiness Process Automation• Problem Analysis• Root Cause Analysis

Business Process Improvement• Duration analysis• Activity Based Costing• Benchmarking

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BPA/BPI/BPRBusiness Process Reengineering• Outcome Analysis• Technology Analysis• Activity Elimination• Proxy Benchmarking• Process Simplification

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System Analysis• System Study• BPA/BPI/BPR• Requirement Analysis• Use Case Modeling• Structural Modeling• Behavioral Modeling• Data Flow Modeling• Decision Analysis

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System Design• System Architecture Design• Hardware and Software, Computing

Architecture• User Interface Design• Input, Output, Forms, Control, Security,

Procedures• DBMS Design• Class and Methods Design• Program Design

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Implementation• Coding• Testing• Unit Testing, Integration Testing, System

Testing – alpha, beta etc.• Documentation• Installation• Planning, Testing, Changeover• Maintenance

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Requirement Analysis Basic Process of System Analysis• Understand the existing system• Identify improvements• Develop a conceptual framework for the

new system

Study ofStakeholders – Needs – Constraints –

Alterables

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Requirement Analysis • What problems to tackle?• Why solve the problem?• What are the solutions?• What complexities to resolve?• What are the inputs and the outputs?

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Requirement Analysis It is important to:• Understand requirements• Resolve anomalies, conflicts, and

inconsistencies• Organize requirments

The idea is to obtain what the system must do and not how to do it.

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The System Analyst The system analyst must possess:• Knowledge of the organization• Ability to grasp abstractions, group them

logically, and synthesize solutions• Systems thinking ability• Communication skills• Sound hardware and software knowledge

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Requirement Analysis Five Commonly-used techniques• Interviews• Joint Application Design• Document Analysis• Observations• Questionnaires

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Interviewing • Selecting interviewees.• Designing questions

• Open-ended• Closed-ended• Probing questions

• It is important to be very specific to operational management level interviews

• Take copious notes – be brief and attentive – watch body language – define your goal – define your belief and confirm.

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Joint Application Design• Can reduce the scope creep• Structured group process with 10-20 users• Used technology - white boards – flip

charts, computer etc.• Top down approach• Uses Nominal Group Technique with the

help of a facilitator• Should be carefully prepared – should

have a formal agenda

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Joint Application DesignRole of facilitator• Stick to agenda• Help understand technical terms• Record the group’s input publicly• Should always be neutral• Should not participate

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Joint Application DesignThings to note• Avoid Domination of individuals• Non-contributors are encouraged• Do not allow side discussions• Agenda merry-go-round be avoided• Violent agreement be avoided• Unresolved conflicts – structure the issue• True conflict – postpone• Use humour• Finally prepare JAD post-session report

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Questionnaires• Should be unambiguous• Preliminary opinions be sought• Provide anonymity to the respondents• Avoid biased or suggested items• Prepare reports

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Document Analysis and Observations

Document analysis• Format documents – forms, reports, and

manuals• Informal documents – both blank and

completed forms Observation• Remember not to interrupt the process • Look for anomalies

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Comparing TechniquesCriteria Interviews Joint

Application Design

Questionn-aire

Document Analysis

Observa-tions

Type of Information

Existing, Improved,

New

Existing, Improved,

New

Existing, Improved

Existing Existing

Depth of Information

High High Medium Low Low

Breadth of Information

Low Medium High High Low

User Involvement

Medium High Low Low Low

Cost Medium Low to Medium

Low Low Low to Medium

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Module-6

SYSTEM ANALYSISAND DESIGN

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DFD CASE STUDYBuying and Selling CompanyCustomers:• place orders for itemsThe Company:• keeps record of its regular customers• Names and Addresses• History of Purchases

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DFD CASE STUDYWhen an order is receivedAccounts department checks • credit-worthiness of the customer from past

recordsBad credit-worthiness• order is rejected along with a rejection message. Good Credit-worthiness• Items ordered are checked against a list of items

that the company deals with.

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DFD CASE STUDYItems not found on the list• Send regret message to the customer. Items found on the list• ordered items are checked for availability in the

inventory held

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DFD CASE STUDYItems are not available in required quantity• items along with the customer order details are

stored separately as pending orders Items are available in required quantity• Items are sent to the customer along with a

packing slip and an invoice. • Inventory is updated with the sales amount• Generate sales statistics

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DFD CASE STUDYThe purchase department:• issues item requests periodically. • Check against the pending orders• determine total quantity required for each items.• Check The supplier details The purchase department• generate purchase orders for the items to suppliers.

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DFD CASE STUDYThe management• Makes queries with regard to statistics of sales and

purchases.

The Buying and Selling Company• Answers to management queries

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DFD CASE STUDY

Item Order

Invoice +Delv. Adv.

Orders Query

Item Req.

Rej. MsgStatistics

Customers Buy & Sell Information

System

Suppliers

Manage-ment

Purchase Dept.

The Context Diagram

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Rej. Msg.

Customer File

Inventory

Item File

Accepted Order

Accept Order

CustomersOrders Customer

History

DFD CASE STUDY

Process: Accept Order

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Customer File

Rej. Msg.

Rej. Msg.

Checked Order

Credited Order

InventoryItem File

Accepted Order

Check Credits

Customers OrdersCustomer History

Check Items

Accept Order

DFD CASE STUDY

Detailed DFD for Accept Order

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DFD CASE STUDY

Process: Process Order

Invoice + Delivery Advice

Pending Orders

Inventory

Accepted Order

Process Order Sales

Statistics

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DFD CASE STUDY

Processes: Process Item Req. & Make Purchase Order

Supplier List

Pending Orders

Item Order

Item Req.

Process Item Req.

Supp-liers

Purchase Dept.

Make Purchase Orders

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DFD CASE STUDY

Process: Handle Query

Statistics

Management

Sales Statistics

Query

Handle Query

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TOP LEVEL DFD

Rej. Msg.

Supplier List

Pending Orders

Statistics

Inventory

Item Order

Item File

Invoice + Delivery Advice

Accepted Order

Accept Order

Process Order

Customers ManagementOrders

Customer History

Sales Statistics

Query

Item Req.

Process Item Req.

Suppliers

Customer File

Handle Query

Purchase Dept.

Make Pur. Orders

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DFD CASE STUDY: QUESTIONS

Consider the Detailed DFD of Accept Order

Find Attributes of:• Orders Rejected Order• Credited Order Checked Order• Accepted Order

Find Attributes of:• Customer File Customer History• Item File Inventory

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DFD CASE STUDY: QUESTIONS

Consider the Detailed DFD of Accept Order

Find Process Details for: • Check Credits• Check Items• Accept Order

Write Structured English statements.

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DATA DICTIONARY A collection of data element definitions. Important precursor to database design. Important fields:• Data element number • Data element name • Short description • Security classification of the data element • Related data elements • Field name(s) • Code format (Data type, size, input masks) • Null value allowed/Not allowed

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DATA DICTIONARYImportant fields (continued):• Default value • Allowed values for validation• Database table references • Definitions & References • Source of the data • Validity dates • History references • External references • Document Version • Document Date

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STRUCTURED ENGLISHCheck credit-worthinessIf the customer is credit-worthyThen Check Items ordered against a list of items

If the items are found on the list Then

Check availability of items in inventory If Items are available in required quantity Then

Items are sent to customer along with a packing slip and an invoice. Else

items with the order details are stored as pending orders

Endif Else

Send regret message to the customer. Endif

Elseorder is rejected along with a rejection message.

Endif

143

STRUCTURED ENGLISH

Structured English Statements are shown for some of the processes of the DFD Case Study.

CHECK CREDIT

Check credit-worthinessIf the customer is credit-worthyThen

Check Items Else

send rejection message. Endif

144

STRUCTURED ENGLISHCHECK ITEMSCheck Items ordered against a list of items If the items are found on the listThen

Accept OrderElse

Send regret message to the customer. Endif ACCEPT ORDERCheck the item in the inventory heldIf found

ThenAccept OrderElse

Update Inventory Accept Order

Endif

145

STRUCTURED ENGLISHPROCESS ORDER Check availability of items in inventory heldIf Items are available in required quantityThen

Items are sent to the customer along with a packing slip and an invoice

Update Sales StatisticsElse

items along with the order details are stored separately as pending orders

Endif

146

Module-7

DATABASE CONCEPTS

147

DATABASE CONCEPTS• Database is a collection/single storage of all related data

files.• Database is a mechanized, centrally-controlled, collection

of data. • In a Database, Data are organized and stored in a manner

to promote shareability, availability, evolvability, and data integrity.

148

WHY DATABASE• Problems of File Based Systems

• Data redundancy and inconsistency• Difficulty in accessing data• Data isolation• Concurrent access anomalies• Security problems• Integrity problems

149

DATABASE CHARACTERISTICS• Co-ordinated Updation• High Quality and Recency of Data• Data Security• Data Compatibility• No Data Duplication• Logical Concept

150

DATABASE CHARACTERISTICSCriteria

Conventional

FilesRelational Databases

Ease of Use Easy ComplexData Duplication Present Reduced

Data Sharing None Shared DataUpdate Anomaly Inconsistency NoneData Integration Absent IntegratedInitiating Change Hard Easy

Flexibility & Scalability

None Present

Cost Less HighDesign Reqmts. Less High

151

DB, DBMS, & DBA• DATABASE (DB)• A mechanized, centrally-controlled, collection of data.

• DATABASE MANAGEMENT SYSTEM • A SOFTWARE to manage the database Collection of

interrelated data and a set of programmes to access that data

• DATABASE ADMINISTRATOR (DBA)• A person representing organizational authority over the

Database.

152

DBMS OBJECTIVES • Availability • Shareability• Evolvability• Data Independence• Data Integrity

153

DBMS OBJECTIVES • Availability: Data should be available when the user wants

to use it for:• Efficient storage, updating & retrieval of data.• Purposeful information retrieval. • Shareability: Data shared across applications. • No data is owned by an application.• Minimisation of unplanned redundancy. • Evolvability: Database should evolve with time as

applications and query needs changes with time.

154

DATA INDEPENDENCE• Data Independence • Users of Database establish their view of the data and its

structure without regard to the actual physical storage of the data.

• This is achieved by • Separating data from the programs• Providing facilities for different user views• Separating logical design and physical design

155

DATA INDEPENDENCE• Data Abstraction• Physical level: How the data is actually stored• Conceptual level: What data & their relationship• View Level: Part of the actual data

• Physical Data Independence• Ability to modify physical scheme without causing application

programs to be rewritten.

• Logical Data Independence• Ability to modify conceptual scheme without causing the

application programs to be rewritten.

156

DATA INTEGRITY• DBMS needs to establish:• uniform high level of data accuracy and

consistency. • Validation rules are to be applied to the database. • The information obtained from the stored data

must be in an integrated form to be useful for managing, planning, controlling, or decision making.

157

DBMS • DataBase Management System (DBMS)

• A software system that makes the database operational – It performs the following functions:

• Defining a database schema• Creating a new database• Revising an existing database• Controlling a database

158

DBMS • A DBMS should have facilities to:• Retrieve data• Generate reports• Revising Data Definitions• Updating Data, and• Building Applications• DBMS, a door to the physical database, defines:• All accesses and Access language facilities• Data validation and authorization checks• Query handing

159

DBA • Database Administrator (DBA)• The organizational function that exercises control over the database.

• Role of DBA• Carrying out DBMS instructions and facilities • Granting Authorization for data access• Defining Storage Structures & Access Methods • Definition, Creation, Redefinition, and Restructuring of Database• Implementing Integrity Controls.

160

DATA MANAGEMENT Database Query Language

Utility/Facility

Database DefinitionDatabase Creation

Database RedefinitionDatabase Restructure

Integrity Controls

Database ProgrammingLanguage Interface

Database

Application Programs

Non-programming Users

Programming Users

DBA

161

DATA DICTIONARY• Repository of information about data. Sometimes it is simply called

Schema.

• Name of the data item and Source of the data.• Description – picture, range, edit, and validation criteria, security,

owners, number of occurrences. • Impact analysis: users, screens, programs, people.• Keywords for categorisation and searching of data.

Facilities of a data dictionary• Report Facility: Detailed reporting • Control Facility: Authority and documentation• Excerpt Facility: Test Data, Source code

162

DATABASE LANGUAGESData Definition Language (DDL)• To specify a database scheme

Data Manipulation Language (DML)• To retrieve, insert into, delete or modify data on a database

Procedural DML• Specify what data and how to get it

Nonprocedural DML• specify what data only.

163

DATABASE ORGANIZATIONS• Flat File Approach• Hierarchical• Network• Relational

• Flat File Approach• A flat-file consists of records without repeating groups.

A table of records for rows and attributes for columns.

164

HIERARCHICAL• In a hierarchical database the data is organized in a tree

structure. • Each parent record may have multiple child records, but

any child may only have one parent. • The parent-child relationships are established when the

database is first generated, which makes later modification more difficult.

• High processing efficiency but the response to query may be very slow if query is not in the same structure.

165

NETWORKA network database is similar to a hierarchical database

except that a child record (a "member") may have more than one parent (an "owner").

• Parent-child relationships must be defined before the database is put into use.

• Addition or modification of fields requires the relationships to be redefined.

• Relationships between entities are represented by multiple pointers as well as link nodes.

• Complexity is high.

166

RELATIONALIn a relational database the data is organized in tables that

are called "relations."

• Tables are usually depicted as a grid of rows ("tuples") and columns ("attributes").

• A set of tables/rows represent unique records/ entities and columns represent attributes.

• Links between tables can be established at any time algebraically provided the tables have a field in common.

• Relationships are represented by common data values in different relations.

167

COMPARISON• In hierarchical/network structure, the connections and

relationships are in the data structure with the help of connections and access paths.

• Relational structure is flexible and useful for ad-hoc queries, complex DDL/DML are not required.

• For predefined relationships, access paths are more efficient than general table operations. So for predefined queries, hierarchical/network structures are competitive.

168

EXAMPLE – CASE 1.GRADE RECORD.• STUDENT-ID String 9.• STUDENT-NAME String 25.• COURSES-TAKEN (repeats 10)• SEMESTER String 4. • COURSE-NO String 5.• GRADE String 1.

• The record takes up 134 bytes. • There are 34 bytes for the student number and name plus 10 bytes for

each of the 10 courses. • Student ID is a unique key for this record.

169

EXAMPLE – CASE 2.GRADE RECORD.• STUDENT-ID String 9.• STUDENT-NAME String 25.• SEMESTER String 4. • COURSE-NO String 5.• GRADE String 1.

• This record consists of 44 bytes.• If a student takes ten courses then there will be a total of 10 records, for

a total of 440 bytes. • Student ID plus Semester and course-no would constitute a unique key.

170

EXAMPLE – CASE 3.• GRADE RECORD. STUDENT-RECORD.• STUDENT-ID String 9. STUDENT-ID String 9.• SEMESTER String 4. STUDENT-NAME String 25.• COURSE-NO String 5.• GRADE String 1.

• The repetition of the student’s name is eliminated. • Student ID would serve as a common key. • Grade record is now 19 bytes and the Student record is now 34 bytes. • A student taking 10 courses would need 10 Grade records, for a total of 190

bytes.

171

E-R DIAGRAM – EXAMPLE

M1

MORDER

VALUEDATE

NO.

PRICE-QTD.

O-S SUPPLIER

O-P

NAME

PRICEQTY-ORD.

ADDR

NN

PART

S-P

N

QOH NO.

NO.

NAME

172

E-R DIAGRAM – EXAMPLE

1

N

PassengerName

Addr.Id. No. Phone

HAVE

Flight

Departure

Are Of

Date

SourceDest.

No.

Freq. Arr Time

Dep Time

N

1

Personnel

Are InM

N

SalName

Addr.ID

173

DESCRIBING DATA• Data abstraction - Data model is abstract• Data models do not relate to flow of data

• Levels of Data description

• First level: Conceptual Data Model Development using Entity-Relationship Diagramming

• Second Level: Normalization of the data elements

• Normalized Data Model is then converted to a Physical Database.

174

E-R DIAGRAMMING • Entity: Distinct things in an enterprise

• Relationships: Meaningful interaction between Entities

• Attributes: Properties of entities and relationships

MSTUD. SUB.STSUB

Name ID

ADDR

NameID

N

175

E-R DIAGRAMMING

Strong vs. Weak Entity Set• Account is a Strong Entity as its existence is not dependent on other

entities.

• Transaction is a Weak Entity as its existence depends on the existence of Account as well.

A/C Trans.LOG

Name Bal.

ID

DateID

176

E-R DIAGRAMMING

Generalization• Pilot and Engineer are generalizations of Entity Employee.

• ISA depicts generalizations

Employee

Pilot

Name Salary

Engineer

ExpertiseFlying Hrs ISA

177

E-R DIAGRAMMING Aggregation

Ternary Relationship

Employee

Equipment

work

Project

use

Customer

Branch

HAS Account

178

NORMALIZATION• First Normal Form (1NF) or Flat File • No Composite attributes, No repeating groups • Every attribute is single and describes one property.

Example: • GRADE-RECORD. Not in 1NF• STUDENT-ID String 9.• STUDENT-NAME String 25.• COURSES-TAKEN (repeats 10 times).• SEMESTER String 4. • COURSE-NO String 5.• GRADE String 1.

179

NORMALIZATIONRemoving composite/repetitive attributes, we get

GRADE-RECORD. This is in 1NF• STUDENT-ID String 9. Is it in 2NF?• STUDENT-NAME String 25.• SEMESTER String 4. • COURSE-NO String 5.• GRADE String 1.

Second Normal Form (2NF) • Remove Non-full or partial dependency from 1NF. • No non-key field is a fact about a subset of a key.• Every non-key field is fully dependent on the key.

180

NORMALIZATION• The Grade Record is not in 2NF.• KEY? Student-ID + Semester + Course-No.• Student-Name is a fact of Student-ID only!

• To make it in 2NF, we have to split the tables into:

• GRADE RECORD. STUDENT-RECORD.• STUDENT-IDString 9. STUDENT-ID String 9.• SEMESTER String 4. STUDENT-NAME String 25.• COURSE-NO String 5.• GRADE String 1.

181

NORMALIZATIONThird Normal Form (3NF)

• Remove transitive dependency from 2NF. • No non-key field depends on another non-key field.

• Are they in 3NF?

• Yes! Because no transitive dependency is present!

• GRADE RECORD. STUDENT-RECORD.• STUDENT-ID String 9. STUDENT-ID String 9.• SEMESTER String 4. STUDENT-NAME String 25.• COURSE-NO String 5.• GRADE String 1.

182

NORMALIZATION3NF Example

• STUDENT-RECORD.• STUDENT-ID String 9. (Key Element)• STUDENT-NAME String 25.• Dept-ID String 2• Dept-Name String 25.

• No repeating groups – 1NF• No partial dependency – 2 NF• Transitive dependency? – Yes! Not in 3NF

• How to bring it in 3NF?

183

NORMALIZATION• 3NF Example: TO bring it to 3NF, we should have,

• STUDENT-RECORD. DEPT.-RECORD.• STUDENT-ID String 9. Dept-ID String 2.• STUDENT-NAME String 25. Dept-Name String 25.• Dept-ID String 2.

Another Example• SUPPLIER-NUMBER, SUPPLIER-NAME, SUPPLIER-ADDRESS,

PART-NUMBER, PART-NAME, QUANTITY-ON-HAND, PRICE-QUOTED, ORDER NUMBER, ORDER DATE, ORDER-VALUE, PRICE, and QUANTITY-ORDERED.

184

NORMALIZED TABLESORDER NUMBER ORDER NUMBERORDER DATE SUPPLIER-NUMBERORDER-VALUE

ORDER NUMBERPART-NUMBER PART-NUMBERPART-NAME QUANTITY ORDEREDQUANTITY-ON-HAND PRICE

SUPPLIER-NUMBER SUPPLIER-NUMBERSUPPLIER-NAME PART-NUMBERSUPPLIER-ADDRESS PRICE-QUOTED