EXTRAS. Table 2.2 First Generation Computers Computer Characteristics and Capabilities Trends and Development in Computer Hardware Room (Size)Transistors.

EXTRAS

Table 2.2 First Generation Computers

Computer Characteristics

and Capabilities

Trends and Development in Computer Hardware

Room (Size) Transistors

Once Component per circuit (Density)

Magnetic Drum (Main Memory)

Hundreds instructions per second (Speed)

Magnetic Drum, magnetic disk

(Secondary Storage)

Failure of circuits in days (Reliability)

Punched Cards (Input media)

Tens of thousands capacity in characters (Memory)

Punched cards, Printed reports (Output Media)

Cost slightly lower than the first generation

Table 2.4 Third Generation Computers

Computer Characteristics

and Capabilities

Trends and Development in Computer Hardware

Disk size mini computer Integrated semi-conductor circuits

Hundreds of thousands of components per circuit

Magnetic core

Tens of millions instructions per second

Magnetic Drum, Magnetic Tape

Failure of code in weeks Key to tape and disk (input media)

Hundreds of thousands capacity in characters

Printed reports/video displays (output media)

Cost lower than third generation

Table 2.3 Second Generation Computers

Computer Characteristics

and Capabilities

Trends and Development in Computer Hardware

Closet (Size) Transistors

Hundreds Component per circuit (Density)

Magnetic Core (Main Memory)

Thousands instructions per second (Speed)

Magnetic tape, Magnetic Tape

(Secondary Storage)

Failure of circuits in hours (Reliability)

Punched Cards, Paper Tape (Input media)

Thousand capacity in characters (Memory)

Punched cards, Printed reports (Output Media)

Cost Very high

Table 2.5 Fourth Generation Computers

Computer Characteristics

and Capabilities

Trends and Development in Computer Hardware

Typewriter size micro computer Large Scale Integrated (LSI) semi conductor circuits

Thousands components per circuit Magnetic disk, Floppy disk, Magnetic bubble optical disk.

Millions instructions per second Keyboard data entry, Direct input devices optical scanning.

Failure of circuits in weeks Video displays, audio responses, printed reports.

Hundreds of thousands capacities

Cost lower than second generation

Table 2.6 Fifth Generation Computers

Computer Characteristics

and Capabilities

Trends and Development in Computer Hardware

Credit card sized micro-processor semiconductor circuits

Very large scale integrated (VLSI)

Millions of components per circuit Use of Artificial Intelligence

Billions/Trillions instructions per second

Speech input, tactical input

Failure of code in years Graphics displays, voice responses.

Billions capacity in characters Parallel and massively parallel

Cost very low.

Operating System

Brief Details/Features

Dos A command-driven operating system for mainly 16-bit micro computer. PC-DOS for IBM and MS-DOS for IBM Compatible microcomputer. Most popular operating system. Does not allow multi-tasking. Memory limitations.

Windows 95 32 bit operating system. Faster to operate. Provides a streamlined GUI. Supports multi-tasking, multi-threading (ability to manage multiple independent tasks simultaneously) and powerful networking capabilities, including capability to integrate fax, e-mail and scheduling programs.

Windows NT Provides GUI, Has move powerful multitasking and memory management capability.

32 bit operating system for micorocumpter. Not tied to computer hardware based on Intel Micro-processors alone. Can provide mainframe-like computer power for new applications with massive memory and file management requirements.

OS/2 Operating System/2

Is robust operating system used with 32 bit IBM Personal System/ 2 micro-computer or IBM-Compatible micro-computers. OS/2 supports multi-tasking, accommodates large applications, allows applications to be run simultaneously, supports networked multimedia and pen computing applications. A macro protected system has its own GUI. Supports DOS applications and can run Windows and DOS applications at the same time in its own resizable windows.

UNIX Developed by Bell Laboratories in 1969. An interactive, multi-user, multi-tasking operating system. Highly supportive of communications and networking. Can run on many different kinds of computers and can be easily customized. Powerful but considered to be complex

LINUX This is a freely available operating system. It is now reported that IBM would be installing this system on their machines.

Table 3.1 Operating Systems and their features

Read Sort Collate Compare Store

Write Merge Delete Decide Display

Print Copy Enter Computer Etc.

Plot Transfer Create Perform

Table 3.2 Data Processing Steps

Data Collection

Data Collation

Data Conversion

Data Written in Documents

Data in Machine Readable Form

Input Unit

Memory CPU

Processed Data in Internal Form

Output Unit

Data Transformed to a readable form

Fig. 3.3

Data Processing Steps

Fig. 4.2 Decision making at different levels of organization

Transaction Planning

Operational Planning

Tactical

Planning

Policy

Planning

(Strategic)

Unstructured

Structured

Step Detail

1. Recognizing and defining the situation

Some stimulus indicates that a decision must made. The stimulus may be positive or negative

2. Identifying alternatives

Both obvious and creative alternatives are desired. In general, the more significant the decision, the more alternatives should be generated.

3. Evaluating alternatives

Each alternative is evaluated to determine its feasibility, its satisfactoriness and its consequences.

4. Selecting the best alternative

Consider all situational factors and choose the alternative that best fits the manager’s situation.

5. Implementing the chosen alternative

The chosen alternatives is implemented into organizational system.

6. Follow up and evaluation

At sometime in the future, the manager should ascertain the extent to which the alternative chosen in step 4 and implemented in step 5 has worked.

Table 4.1Steps in decision making process as illustrated by Griffin

Fig 5.1 Data Processing

Stored Data

Processing (Processor)Input (Data) Output (Information)

Table 5.2

Difference between planning and control information

Planning Information Control Information

It covers the whole organization

It is concerned with small, specific part of organization.

It has a longer time span It has a shorter time span

It looks for and analyses trends/patterns

It looks for specific details for functional activity.

Used for working futuristic trends/forecast.

Used for assessing actual performance vis-à-vis budgeted.

Fig. 5.5 Human information processing mechanism and decision-making process

DataStorage

Stored FramesOf Reference

Mental ProcessingInput Data Decisions

Receptors

Effectors

ProcessorMemoryEnvironment

Fig 5.7 Human Information Processing System

DSS Provides Answers to questions

Raw data and status access What is?

General Analysis capabilities What is? or Why?

Casual Models i.e. forecasting, diagnosis.)

Why? What if?

Solutions suggestions, evaluation What is best? What is good enough?

Solution selection

Table 6.1 Summarizations of DSS approach

Representations Conceptualization of information used in making decisions, such as graphs, charts, lists, reports and symbols.

Operations Logical and mathematical manipulations of data such as assigning risks and values, simulating alternatives etc.

Memory Aids Data bases, views of data, work space, libraries and other capabilities to refresh/update memory

Control Aids Capability which allows user to control the DSS activities like software permitting use control of memory representations, operations, training, tutorials, menus, function keys, help commands etc.

Table 6.2 Core capabilities of DSS

Table 6.4 Decision and type of system required

Decision Type of system required

Selection of vendor Inquiry System

Procurement Inquiry System

Pricing Data analysis

Selection of vendor based on price, quality, performance

Information analysis system

Selection of capital asset Return on investment analysis system

Inventory rationalization Valuation of inventory and accounting system

Management of inventory within various financial and stocking constraints

Inventory optimization model

Fig. 6.6 The AI Onion Model

ProblemSolving and

Planning

HeuristicSearch

Modelling andRepresentation of Knowledge

Common SenseReasoning and

Logic

AILanguageAnd Tools

Natural Language Processing

Expert Systems

ComputerVision

Desired Performance

Implement Course Correcting Programme

Programme forCorrecting Action

Analyse Causes forDeviation

actual Performance

Actual Performance Measurement

Actual Vs Standard Performance Compared

Identify Deviation

Fig. 7.4 Usefulness of Feed back

DetermineBusiness

Objectives

CASE TOOLSCAN CASE TOOLSCAN NOT

Automate number of manual tasks involved in systems Development.

Automatically provide a functional, relevant system.

Promote standardization based on a single methodology.

Easily interface with databases and fourth-generation languages.

Promote greater consistency and co-ordination during a development project.

Automatically force analysts to use a prescribed methodology when one dose not exist

Generate a large portion of documentation for a system, such as a data flow diagram, data models and or other specifications.

Radically transform the systems Analysis and Design Process.

Table 8.1 Capabilities of CASE Tools

Computer BasedPersonnel System

Employees Name Address Position Marital Status Date of Joining

Payroll Grade/Scale Income Tax Professional Tax Misc Net Salary

Benefits Group Insurance Medicaim ESI PF Pension

PersonnelApplicationProgramm

Database Management

System

PayrollApplication

Program

Benefits ApplicationProgram

Personnel Dept.

BenefitsDept.

EstablishmentDept.

Table 9.1 DBMS : IIIustration

Fig. 10.3 Flow of data inside the Data Wherehouse

TIME SAVINGS

FOR DATA SUPPLIERS AND FOR USERS

MORE AND BETTER INFORMATION

BETTER DECISIONS

IMPROVEMENT OF BUSINESS

PROCESSES

SUPPORT FOR THE ACCOMPLISHMENT

OF

STRATEGIC BUSINESS OBJECTIVES

EASY TO MEASURE LOCAL IMPACT

HARD TO MEASURE GLOBAL IMPACT

Fig. 10.4 Benefits from data Warehousing

Table 12.2 Quality Factors

Aesthetics Conformance Correctness Durability Efficiency

Extendibility Integrity Inter-operability

Maintainability Openness

Perception /Perceived quality

Portability Reliability Reusability Security

Serviceability Survivability Testability Understandability

(Comprehensibility)

Usability

User-Friendliness

Alternative High Sales (Probability : 0.40)

Low Sales (Probability : 0.60)

Activity A + Rs. 45,000 - Rs. 10,000

Activity B + Rs. 80,000 - Rs. 25,000

Activity C + Rs. 30,000 - Rs. 5,000

The Pay Off Matrix

Capturing Data from an event/transaction has to be recorded

Verifying Data has to be checked/validated for correctness

Classifying Data has to be placed in specific categories

Arranging/Sorting

Data has to be placed in specific categories

Summarizing Data elements have to be combined/aggregated

Calculating Arithmetical/Logical calculations/computations have to be carried out

Storing Data has to be placed in some storage media

Retrieving Specific data elements have to be searched for and accessed

Dissemination/ Communication

Data has to be transmitted from one place (device) to another (user)

Information Process

Newell – Simon Model

Long Term Memory

Short Term Memory

Elementary Processor

Interpreter

Processor

Input Output

Steps in defining a proposed information architecture in Business Systems Planning

Iteration

Measureresults

Understand Situation

Develop ModelInitiate appropriate action

Understand analysis

Cyclical functioning of Data Mining

Quality Profile Model

Transcendental

Properties

(not quantifiable)

Quality factors

(objectively measurable)

Merit Indices

(Subjectively measurable)

Quality Metrics

(Quantifiable)

Quality Attributes (indicates presence or absence of a

property)

Quality Ratings

(Quantifications of value judgement)

Data Mining Association CaseConsider sitting in an English pub and buying a pint of beer but not a bar meal. While servicing the request, the barkeep asks if you are interested in a bag of chips as well. Why would the keep ask such a question? Because it is the goal of the keep, in some regards, to be profitable and maximize the amount of revenue per transaction. By asking if you wanted chips, the barkeep may make a bigger tip or the bar may make more revenue. The barkeep knew to ask you this question, and knew there was a good chance (a high probability) that you would also take the chips. The barkeep had this knowledge from experience, specifically from previous interactions with customers.Similarly, the association rule finding algorithm is trained on historical data, i.e. past transactions. The data contains checkout information and a list of products that were purchased in each transaction, perhaps along with other information (volume, sale amount, although in many cases just the presence or absence of a product in a transaction is sufficient). While training, the algorithm may identify a relationship (a form of an association) between beer and no bar meals, and predict you are more likely to buy crisps (US. chips) over someone not identified with that relationship.Typically the relationship will be in the form of a rule such as:

IF {beer, no bar meal} THEN {crisps} The probability that a customer will buy beer without a bar meal (i.e. that the antecedent is true) is referred to as the support for the rule. The conditional probability that a customer will purchase crisps is referred to as the confidence of the rule.