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Management Information SystemsModule 4

Knowledge Management

Prof. George Mathew B.Sc., B.Tech, PGDCA, PGDM, MBA

Phone: 9400661717E-mail: mathewaiim@gmail.com

PROFESSOR

ALLAMA IQBAL INSTITUTE OF MANAGEMENT

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Knowledge

Knowledge is the ability of a person to understand the situation and act effectively.

Knowledge in the work place is the ability of people and organization to understand and act effectively.

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Knowledge

1.Knowledge as competence, for example, knowing how to use software or play a guitar.

2. Knowledge as acquaintance, for example, knowing your way around a city, which requires competence.

3. Knowledge as recognition, for example, recognizing information.

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What is Knowledge?Knowledge is a fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices and norms.

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Data

Information

Knowledge

Wisdom

Quick Definitions Wisdom

Application of knowledge Produce desired outcome

Knowledge

Information plus processing Relevant and personal

Information

Data plus meaning Data

Raw text, numbers, images

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Data

Information

Knowledge

Wisdom

Example Wisdom

How to reduce inventory

Knowledge

Inventory is too high Information

Balance sheet Data

General Ledger transactions

Knowledge Hierarchy

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Knowledge Work

Knowledge work is work that involves thinking, processing information, and formulating analysis, recommendations, and procedures.

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Knowledge Work

“Any creative systematic activity undertaken in order to increase the stock of knowledge of man, culture and society, and the use of this knowledge to devise new applications. It includes fundamental research … applied research … and experimental development work leading to new devices, products and processes”

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Knowledge Work

Types of Knowledge work

• Diagnosis and problem finding

• Planning and decision making

• Monitoring and control

• Organizing and scheduling

• Authoring and presentation

• Communication

• System Development

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IT Infrastructure for Knowledge Management

KNOWLEDGE MANAGEMENT IN THE ORGANIZATION

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Information versus Knowledge Workers

Information workers are those workers whose jobs involve the creation, collection, processing, distribution, and use of information.

Knowledge workers are a subset of information workers whose responsibilities are based on a specialized body of knowledge.

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• Computer-aided design (CAD)

• Virtual reality systems

• Virtual Reality Modeling Language (VRML)

• Investment workstations

Examples of Knowledge Work Systems

INFORMATION AND KNOWLEDGE WORK SYSTEMS

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• Groupware

• Intranets and Enterprise Knowledge Environments

• Enterprise information portals

• Teamware

Sharing Knowledge: Group Collaboration Systems and Enterprise Knowledge Environments

INFORMATION AND KNOWLEDGE WORK SYSTEMS

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Explicit and Tacit Knowledge

• Explicit knowledge – what is recorded; easily identified, articulated, shared and employed

• Tacit knowledge – personal; wisdom and experience; context-specific; more difficult to extract and codify

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Tacit vs. Explicit• Tacit

– 'that part of knowledge that is so deeply buried that you are unable to represent and share it'

• Explicit– Fully and clearly expressed; leaving nothing

implied. – Fully and clearly defined or formulated

• Once you capture knowledge it becomes explicit

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Learning

• To gain knowledge, comprehension, or mastery of through experience or study.

• The act, process, or experience of gaining knowledge or skill.

• Learning usually refers to knowledge that is gained by schooling and study: “Learning is not attained by chance, it must be sought for with ardor and attended to with diligence” (Abigail Adams).

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Learning

• Learning typically builds on previous knowledge and/or experience

• Learning domains– Mental skills– Growth in feelings or emotional areas– Manual or physical skills

• Learning experiences– Training– Education– Development

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“Management” of Knowledge Knowledge management is an integrated systematic approach to identifying, managing and sharing all of an enterprise’s information assets, including databases, documents, policies, and procedures, as well as previously unarticulated expertise and experience held by individual workers.

Fundamentally it is about making the collective information and experience of an enterprise available to individual worker.

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What is Knowledge Management? (2)

• Knowledge Management facilitates the capture, deployment, access and reuse of information and knowledge-typically using contemporary technology.

• Knowledge Management is the ongoing creation, capture, preservation and management of information. This gives employees, customers, partners and companies the resources needed to be more efficient and productive.

• Knowledge Management has referred to efforts to capture, store, and deploy knowledge using a combination of information technology and business processes.

The Knowledge Management LandscapeEnterprise knowledge management software includes sales of content management and portal licenses, which have been growing at a rate of 35 percent annually, making it among the fastest-growing software applications.To transform information into knowledge, a firm must expend additional resources to discover patterns, rules, and contexts where the knowledge works. Wisdom is thought to be the collective and individual experience of applying knowledge to the solution of problems.

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Explicit knowledge

specific know-how to design, build, sell and support products and services

Tacit knowledge

individual and collective skills enabling the organization to act, adapt, and evolve

Tangible knowledge components

data, procedures, plans, models, algorithms, documents of analysis and synthesis

Intangible knowledge components

abilities, professional skills, private knowledge, organizational culture, history of the organization, contexts of decisions, etc.

Knowledge in Organizations

Dimensions of knowledgeThe most important dimensions of knowledge are:

Knowledge is a firm asset: Knowledge is an intangible asset requiring organizational resources and whose value increases as it is shared.

Knowledge has different forms: Knowledge includes craft, skills, procedures, and understanding of causality

Knowledge has a location: Knowledge resides with individuals, social groups, is difficult to transfer or extract

• Knowledge is situational: Knowledge may be applicable only in certain contexts or situations

Organizational learningOrganizational learning is the process of gaining experience through data collection, measurement, experiment, and feedback and using these experiences to create new business processes and management decision making.

Knowledge managementKnowledge management is a set of processes to create, store, transfer, and apply knowledge in the organization. Knowledge management increases the ability of the organization to learn from its environment and to incorporate knowledge into its business processes.

The knowledge management value chain includes four main steps that add value to raw data and information as it is transformed into usable knowledge:

Knowledge management value chain

1. Knowledge acquisition

2. Knowledge storage

3. Knowledge dissemination

4. Knowledge application

1. Knowledge acquisition

Knowledge may be acquired by building repositories of documents, enabling systems for expert advice, analyzing data for patterns, or by using knowledge workstations. An effective knowledge system requires systematic data from a firm's transaction processing systems and data from external sources.

2. Knowledge storage: Storing knowledge in databases and document management systems

3. Knowledge dissemination: Knowledge may be transferred to managers and employees via portals, e-mails, search engines, collaborative office systems, training programs, and other means.

4. Knowledge application: New knowledge must be built into an organizations processes and application systems and become a part of decision-making systems.

There are three major types of knowledge management systems:

• Enterprise-wide knowledge management systems

• Knowledge work systems

• Intelligent techniques

Enterprise-Wide Knowledge Management Systems

Enterprise-wide knowledge management systems are firmwide efforts to collect, store, distribute, and apply digital content and knowledge.

They use an array of technologies for storing structured and unstructured content, locating employee expertise, searching for information, disseminating knowledge, and using data from key corporate systems.

There are three major categories of enterprise-wide KMS:

1.Structured knowledge systems

2.Semi Structured knowledge systems

3.Knowledge networks

Structured knowledge systems provide databases and tools for organizing and storing structured knowledge that exists in formal documents.

Semistructured knowledge systems provide databases and tools for organizing and storing semistructured knowledge, such as e-mail, brochures, or rich media, that is not in a formal document or report. Such systems provide a database and technical infrastructure that tracks, stores, and organizes semistructured content.

Structured and semistructured knowledge systems provide knowledge repositories. Taxonomies (schemes for classifying information) and tagging are used to classify repository content so that it is easier to access.Knowledge network systems try to turn tacit, unstructured knowledge into explicit knowledge that can be shared in a database. To disseminate tacit knowledge, knowledge network systems may provide directories and tools for locating firm employees with special expertise, or provide solutions to commonly found problems in a central knowledge database.

Technologies used for supporting knowledge management systems include:

Enterprise knowledge portals to external information and internal resources

Collaboration technologies, including e-mail, chat, discussion groups, conferencing

Consumer Web technologies, such as blogs, wikis, and social bookmarking

Learning management systems (LMS): LMS provide tools for managing, delivering, tracking, and assessing various types of employee learning and training.

Knowledge Work Systems Knowledge workers perform three key roles that are critical to the organization and to the managers who work within the organization:

Keeping the organization current in knowledge

Serving as internal consultants regarding the areas of their knowledge

Acting as change agents

Knowledge workers rely on traditional office systems but often require highly specialized knowledge work systems with powerful graphics, analytical tools, and communications and document management capabilities.

These systems require great computing power, access to external databases, easy-to-use interfaces, and optimization for the specific tasks to be performed.

Examples of knowledge work systems include:

CAD/CAM systems: Computer-aided design (CAD) and Computer-aided manufacturing (CAM) systems automate the creation and revision of designs, using computers and sophisticated graphics software. They provide engineers, designers, and factory managers with precise manufacturing control over industrial design and manufacturing

Virtual reality systems: These use interactive graphics software to aid drug designers, architects, engineers, and medical workers by presenting precise, three-dimensional simulations of objects.

Investment workstations: These are high-end PCs used in the financial sector to analyze trading situations instantaneously and facilitate portfolio management.

Intelligent TechniquesIntelligence techniques may be used for:

Capturing individual and collective knowledge and extending a knowledge base, using artificial intelligence and database technologies

Capturing tacit knowledge, using expert systems, case-based reasoning, and fuzzy logic

Knowledge discovery, or discovering underlying, hidden patterns in data sets, using neural networks and data mining

Generating solutions to highly complex problems, using genetic algorithms

Automating routine tasks, using intelligent agents

Artificial intelligence (AI) is the effort to develop computer-based systems (both hardware and software) that behave as humans, with the ability to learn languages, accomplish physical tasks, use a perceptual apparatus, and emulate human expertise and decision making.

Expert systems:Capture tacit knowledge in a very specific, limited domain of human expertise

Support highly structured decision making

Model human knowledge as a set of rules called the knowledge base

Use an inference engine to search through the knowledge base.

In forward chaining, the inference engine begins with information entered by the user to search the knowledge base for a conclusion.

In backward chaining, the system begins with a hypothesis and asks the user questions to confirm or disprove the hypothesis.

Require input from both human experts for defining the knowledge base and knowledge engineers, who translate the knowledge into a set of rules

Inference engineAn inference engine works by searching through the rules and “firing” those rules that are triggered by facts gathered and entered by the user.

Basically, a collection of rules is similar to a series of nested IF statements in a traditional software program; however, the magnitude of the statements and degree of nesting are much greater in an expert system.

Case-based reasoning (CBR):

Stores cases (descriptions of past experiences) in a database for later retrieval

Searches the database for cases with similar characteristics to a new case to find and apply appropriate solutions

Rely on continuous expansion and refinement by users

Fuzzy logic systems:Use rule-based logic to represent imprecise or ambiguous values used in human or linguistic categorization, such as defining and comparing terms such as "hot, warm, cool, cold" for use in a temperature control system.

Provide solutions to problems requiring expertise that is difficult to represent in the form of crisp IF-THEN rules

Neural networks:Find patterns and relationships in massive amounts of data that would be too complicated and difficult for a human being to analyze.

"Learn" patterns by sifting through data, searching for relationships, building models, and correcting over and over again the model's own mistakes.

Use a large number of sensing and processing nodes that continuously interact with each other

May be sensitive and not perform well with too little or too much data

Are used in science, medicine, and business primarily to discriminate patterns in massive amounts of data.A neural network uses rules it “learns” from patterns in data to construct a hidden layer of logic. The hidden layer then processes inputs, classifying them based on the experience of the model. In this example, the neural network has been trained to distinguish between valid and fraudulent credit card purchases.

Genetic algorithms:Find optimal solutions to a problem by examining a very large number of possible solutions for that problem.

Use adaptive, evolutionary conceptual models to change and reorganize components of possible solutions to create viable solutions, test their fitness, and discard unlikely solutions

Use processes such as fitness, crossover, and mutation are to "breed" solutions.

Are useful for dynamic and complex business problems involving hundreds or thousands of variables, such as problems involving engineering design optimization, product design, and monitoring industrial systems.

Hybrid AI systems:

Integrate genetic algorithms, fuzzy logic, neural networks, and expert systems are being developed to take advantage of the best features of each technology.

Intelligent agents:Are software programs that work in the background without

direct human intervention

Carry out specific, repetitive, and predictable tasks

Use a limited built-in or learned knowledge base to accomplish tasks or make decisions on the user's behalf

Are used in agent-based modeling applications used to model or simulate the behavior of consumers, stock markets, and supply chains and to predict the spread of epidemics

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Intellectual Capital (IC) and Knowledge Management

Intellectual Capital (IC) is defined as a body of knowledge, which is well structured in text and numeric form and has commercial and economic value significance to the person or organization.

‘Knowledge and Intellectual Capital’ is a set made of information, ability, experience, and wisdom which gives the organization a comparative advantage and expertise.

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Data, Information, Knowledge

• Data – raw facts; numbers

• Information – data in context; readily captured in documents and databases

• Knowledge – information plus experience to act upon

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Seven Levers of Knowledge Management• Customer Knowledge - the most vital

knowledge

• Knowledge in Products - ‘smarts’ add value

• Knowledge in People - but people ‘walk’

• Knowledge in Processes - know-how when needed

• Organizational Memory - do we know what we know?

• Knowledge in Relationships - richness and depth

• Knowledge Assets - intellectual capital

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•Technology

• Process

• People

Three Pillars of KnowledgeManagement

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– Create knowledge repositories.

– Improve knowledge access.

– Enhance the knowledge environment.

– Manage knowledge as an asset.

Objectives of Knowledge Management

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1. Create knowledge

2. Capture knowledge

3. Refine knowledge

4. Store knowledge

5. Manage knowledge

6. Disseminate knowledge

The Knowledge Management Cycle

KMS Architecture

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Expert Systems and Knowledge-based Systems

Artificial Intelligence (AI) system

Artificial Intelligence is a software technique applied to the non-numeric data expressed in terms of symbols, statements and patterns.

It uses the method of symbolic processing, social and scientific reasoning, and conceptual modeling for solving the problems.

Artificial Intelligence (AI) system

AI system falls in three categories:

1. Expert systems (knowledge based)

2. Natural language( native languages) systems

3. Perception systems (vision, speech, touch)

What are Expert Systems ?

A decision-making and/or problem-solving package of computer hardware and software that can reach a level of performance comparable to (or even exceeding that of) a human expert in some specialized and usually narrow problem area.

– “..an intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solution.”

– The area of expert systems is a very successful approximate solution to the classic AI problem of programming intelligence.

– Thus, an expert system emulates the decision-making ability of a human expert.

What is an Expert System?

– There is no general purpose problem solver yet developed, but expert systems function well in restricted domains.

– Initially, expert systems were specifically those which contained expert knowledge, obtained from human experts. The term now covers any system which uses expert system technology (e.g. languages, programs, or hardware designed to aid in the development and execution of expert systems.)

– The knowledge contained in an expert system can be expertise, public knowledge, domain

or specialised task knowledge, and may be obtained from public or private media, as well as from knowledgeable persons or experts.

– Thus the terms expert system, knowledge-based system and knowledge-based expert system, are often used synonymously.

Knowledge-base

Inference Engine

User Expertis

e

Facts

Expert System

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Expert Systems?

• An Expert System (ES) is a system that employs human knowledge captured in a computer to solve problems that ordinarily require human expertise

• ES imitate the expert’s reasoning processes to solve specific problems

• Expert Systems is a kind of Knowledge-based Systems (KBS)

Characteristics of ES• used by nonexperts

• quality of recommendations comparable to that of human experts

• applied to recurring problems

• applied to tactical, low-level problems

• applied to fairly structured tasks

• knowledge base is separated from programs

• explain the reasoning

inference engine

world model

knowledge base

user

Expert System Architecture

knowledge base editor

preceptors

explanation subsystem

explanation subsystem

Important Expert System Components

User Interface

InferenceEngine

KnowledgeBase

Reasoning (Thinking). Makes logical deductions based upon the knowledge in the KB.

Contains the domain knowledge

A facility for the user to interact with the Expert System

The Architecture of Knowledge-based Expert System

Knowledge Base/ Rules Facts

Inference Engine

Knowledge AcquisitionSubsystem

ExplanationSubsystem

UserInterface

Expert or

Knowledge EngineerUser

Structure of an Expert System

Userinterface

Recommendedaction

Explanationfacility

Knowledgerefinement

Documented knowledge

User

Knowledgeengineer

Expertknowledge

Inference enginedraws conclusions

- Interpreter - Scheduler - Consistency enforcer

Blackboard (workplace)Plan AgendaSolution Problem

description

Knowledge base Facts: What is known about the domain area Rules: Logical reference (e.g. between symptomes and causes)

Facts aboutthe specificincident

Knowledgeacquisition

Consultation Environment Development Environment

All ES Components

• Knowledge Acquisition Subsystem

• Knowledge Base

• Inference Engine

• User Interface

• Blackboard (Workplace)

• Explanation Subsystem (Justifier)

• Knowledge Refining System (most ES do not have a Knowledge Refinement Component)

Knowledge Base• The knowledge base contains the domain

knowledge necessary for understanding, formulating, and solving problems

• Two Basic Knowledge Base Elements– Facts: Factual knowledge is that knowledge of

the task domain that is widely shared, typically found in textbooks or journals, and commonly agreed upon by those knowledgeable in the particular field.

– Heuristics: Heuristic knowledge is the less strictly defined, relies more on empirical data, more judgmental

Knowledge Acquisition Subsystem

• Knowledge acquisition is the accumulation, transfer and transformation of problem-solving expertise from experts and/or documented knowledge sources to a computer program for constructing or expanding the knowledge base

• Requires a knowledge engineer

Explanation subsystem

An appropriate explanation subsystem and user interface should be constructed, to help user query expert system and understand the result.

Inference Engine

• A computer program that carries out the

automatic reasoning with knowledge.

• It is often associated with a method of reasoning

for a special scheme of knowledge representation

• Known as the control structure or the rule

interpreter

Inference EngineInference Engine compares the user’s information with the knowledge in knowledge base, and derives whatever conclusions may logically follow.

There are two common inference method:

Backward-chaining: Inference Engine guesses a conclusion and then attempts to prove that this guess is correct.

Forward-Chaining: Inference Engine compares the facts with the IF part of a rule, and fires a conclusion from the THEN part of that rule.

According to the goal of tech watch, Our Expert system will chose the Forward-Chaining as the main inference Engine.

User Interface

There are variety of interface techniques available today, such as Web-based, graphics, natural language, We can construct an expert system based on Web.

User Interface

• User enters:– Instructions– Information

• Expert system provides:– Solutions– Explanations of

• Questions• Problem solutions

}Menus, commands, natural language, GUI

Types of Expert Systems

• Expert Systems vs. Knowledge-based Systems

• Rule-based Expert Systems

• Frame-based Systems

• Hybrid Systems

• Model-based Systems

• Real-time Expert Systems

Expert Systems Versus Knowledge-based Systems

• An expert system is one whose behavior is so sophisticated that we would call a person who performed in a similar manner an expert.

• Knowledge-based systems: Systems that use knowledge to effectively and efficiently perform tasks (also known as advisory systems, knowledge systems, intelligent systems). These systems do not really need to behave as experts. Knowledge for KBS can be acquired from different resources.

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Latest ES Developments

• Many tools to facilitate the construction of ES at a reduced cost

• Dissemination of ES in thousands of organizations

• Extensive integration of ES with other computer-based information systems (CBIS)

• Increased use of expert systems in many tasks

• Use of ES technology to facilitate IS construction

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• The object-oriented programming approach in knowledge representation

• Complex systems with multiple knowledge sources, multiple lines of reasoning, and fuzzy information

• Use of multiple knowledge bases

• Improvements in knowledge acquisition

• Larger storage and faster processing computers

• The Internet to disseminate software and expertise.

Latest ES Developments

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Basic Concepts of Expert Systems

• Expertise

• Experts

• Transferring Expertise

• Inference Rules

• Explanation Capability

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• Expertise is the extensive, task-specific knowledge acquired from training, reading and experience

- Theories about the problem area

- Hard-and-fast rules and procedures

- Rules (heuristics) of what to do in a situation

- Global strategies

- Meta-knowledge (knowledge about knowledge)

- Facts about the problem area

• Enables experts to be better and faster than non-experts

Expertise

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Some Facts about Expertise

• Expertise is usually associated with a high degree of intelligence, but not always with the smartest person

• Expertise is usually associated with a vast quantity of knowledge

• Experts learn from past successes and mistakes

• Expert knowledge is well-stored, organized and retrievable quickly from an expert

• Experts can call up patterns from experience

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Human Expert Behaviors

• Recognizing and formulating the problem

• Solving the problem quickly and properly

• Explaining the solution

• Learning from experience

• Restructuring knowledge

• Breaking rules

• Determining relevance

• Degrading gracefully (awareness of limitations)

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Transferring Expertise

• Objective of an expert system

– To transfer expertise from an expert to a computer system and

– Then on to other humans (non-experts)

• Activities

– Knowledge acquisition

– Knowledge representation

– Knowledge inference

– Knowledge transfer to the user

• Knowledge is stored in a knowledge base

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The Knowledge Engineer

• Helps the expert(s) structure the problem area by interpreting and integrating human answers to questions, drawing analogies, posing counter-examples, and bringing to light conceptual difficulties

• Usually also the System Builder

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Participants

Tool builder

Vendor

Expert

Systembuilder

Supportstaff

End-user

Tools,languages

Documentedknowledge

Expert system

KnowledgeEngineer

Build

Build

BuildProvide

Use

Use

Test

AcquireKnowledge

Cooperate

Connect

Use

Supporttasks

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ES Shell

• Tool to build expert systems that includes all generic components of an expert system but do not include knowledge

• Examples of expert system shell

– EMYCIN from MYCIN

– RESOLVER (was EXSYS)

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Problems and Limitations of Expert Systems

• Knowledge is not always readily available

• Expertise can be hard to extract from humans

• Each expert’s approach may be different, yet correct

• Hard, even for a highly skilled expert, to work under time pressure

• Users of expert systems have natural cognitive limits

• ES work well only in a narrow domain of knowledge

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• Most experts have no independent means to validate their conclusions

• The vocabulary of experts is often limited and highly technical

• Knowledge engineers are rare and expensive

• Lack of trust by end-users

• Knowledge transfer is subject to a host of perceptual and judgmental biases

• ES may not be able to arrive at conclusions

• ES sometimes produce incorrect recommendations

Problems and Limitations of Expert Systems

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Types of Expert Systems

• Expert Systems vs. Knowledge-based Systems

• Rule-based Expert Systems

• Frame-based Systems

• Hybrid Systems

• Model-based Systems

• Real-time Expert Systems

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Expert Systems Versus Knowledge-based Systems

An expert system is one whose behavior is so sophisticated that we would call a person who performed in a similar manner an expert.

Knowledge-based systems: Systems that use knowledge to effectively and efficiently perform tasks (also known as advisory systems, knowledge systems, intelligent systems). These systems do not really need to behave as experts. Knowledge for KBS can be acquired from different resources.

Artificial Intelligence

• Behavior by a machine that, if performed by a human being, would be considered intelligent

• Theory of how the human mind works (Mark Fox)

Signs of Intelligence

• Learn or understand from experience

• Make sense out of ambiguous or contradictory messages

• Respond quickly and successfully to new situations

• Use reasoning to solve problems

More Signs of Intelligence• Deal with perplexing situations• Understand and infer in ordinary,

rational ways• Apply knowledge to manipulate the

environment• Think and reason• Recognize the relative importance of

different elements in a situation

Major AI Areas

• Expert Systems

• Natural Language Processing

• Speech Understanding

• Robotics and Sensory Systems

• Computer Vision and Scene Recognition

• Intelligent Computer-Aided Instruction

• Neural Computing

Areas of Artificial IntelligenceAreas of Artificial Intelligence

ExpertExpertsystemssystems AIAI

hardwarehardware

RoboticsRobotics

PerceptivePerceptive systemssystems (vision,(vision, hearing)hearing)

NeuralNeuralnetworksnetworks

NaturalNatural languagelanguage

Learning

Artificial IntelligenceArtificial Intelligence

Major Branches of AI (1)

– Perceptive system• A system that approximates the way a human sees,

hears, and feels objects

– Vision system• Capture, store, and manipulate visual images and

pictures

– Robotics• Mechanical and computer devices that perform tedious

tasks with high precision

– Expert system• Stores knowledge and makes inferences

Major Branches of AI (2)

– Learning system• Computer changes how it functions or reacts to

situations based on feedback

– Natural language processing• Computers understand and react to statements

and commands made in a “natural” language, such as English

– Neural network• Computer system that can act like or simulate

the functioning of the human brain

Artificialintelligence

Robotics

Visionsystems

Learningsystems

Natural languageprocessing

Neural networks

Expert systems

Natural Language Processing & Voice Technologies

• Natural language processing (NLP): Communicatng with a computer in English or whatever language you may speak.

• Natural language understanding/speech (voice) recognition: The ability of a computer to comprehend instructions given in ordinary language, via the keyboard or by voice.

• Natural language generation/voice synthesis: Technology that enables computers to produce ordinary language, by “voice” or on the screen, so that people can understand computers more easily.

Speech technologies• Automatic speech recognition (ASR)• Text-to-speech synthesis (TTS)• Dialog systems

Language processing technologies• Machine translation:• Information extraction• Information retrieval, question answering• Text classification, spam filtering, etc…

Natural Language

Vision and Artificial Intelligence

The hardness of vision became clearer when the Artificial Intelligence community tried to design computer programs to do vision.

Examples of Vision Tasks

• Visual Inference:

• (1) Estimating Shape.

• (2) Segmenting Images.

• (3) Detecting Faces.

• (4) Detecting and Reading Text.

• (5) Parsing the full image – detect and recognize all objects in the image, understand the viewed scene.

Vision and Artificial Intelligence

Vision involves both the acquisition and processing of visual information. AI powered technologies have made possible such astounding achievements as vehicles that are able to safely steer themselves along our superhighways, and computers that can recognize and interpret facial expressions.

What is RoboticsRobotics is the study of the design, construction and use of robots.

"A reprogrammable, multifunctional manipulator designed to move material, parts, tools, or specialized devices through various programmed motions for the performance of a variety of tasks" .

An automatic device that performs functions normally ascribed to humans or a machine in the form of a human.

Neural Networks

• Neural networks is a system of programs and data structures that approximates the operation of the human brain.

• Neural networks are particularly good at recognizing subtle, hidden and newly emerging patterns within complex data as well as interpreting incomplete inputs.

Fuzzy Logic

• Fuzzy logic deals with the uncertainties by simulating the process of human reasoning, allowing the computer to behave less precisely and logically than conventional computers do. – Involves decision in gray areas.– Uses creative decision-making processes.

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Applications of AI to Internet

Technology Application

Intelligent agents Assist Web browsing

Intelligent agents Assist in finding information

Intelligent agents Assist in matching items

Intelligent agents Filter e-mail

Intelligent agents Access databases, summarize information

Expert systems Improve Internet security

Intelligent agents Match queries to users with canned answers to FAQ

WWW robots (spiders) Conduct information retrieval and discovery; smart

Intelligent agents search engines (meta-search)

Expert systems Intelligent browsing of qualitative databases

Expert systems Browse large documents (knowledge decomposition)

Intelligent agents Monitor data and alert for actions (for example, looking for

Web site changes), monitor users and usage.

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Quality with Information Systems

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What Is Quality?What Is Quality?Quality is defined as excellence or fitness. Quality is defined as excellence or fitness. Perfect quality is very costly and virtually impossible. Quality should be within acceptable limits as defined by the organization. The totality of features and characteristics of a product or service that bear on its ability to satisfy specified or implied needs .For the producer quality is conformance to specifications and absence of variation from specification .For the customer quality is physical quality, quality of service, psychological quality

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Quality in Information systems

Information Quality=

{Understandability, Reputation,• Reliability, Timeliness,• Availability, Price, Conciseness• Consistency, Coverage,• Response time, Density,• Completeness, Amount,• Accuracy, Relevancy, ... }

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Information system Quality characteristic

• Complete Data• Accurate data• Precise Data• Understandable Output• Timely Output• Relevant Output• Meaningful Output• User friendly operation• Error resistant operation• Authorized use• Protected system and operations

Ref: Gordon B. DavisRef: Gordon B. Davis

Software quality assurance

It can be defined as a” set of systematic activities providing evidence of the ability of the software process to produce a software product that is fit to use”

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Information systems quality

• data quality,

• information quality,

• system quality as well as

• model, method and process quality.

Organizational functions for quality assurance

Top management information system quality assurance Information systems executive quality assurance User quality assurance

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Elements of Data Quality

Data Quality

Timeliness

Consolidation - Reduces Burden

Common Collections

Effectiveness

Multiple Use - Sharing of Data

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Quality Assurance in IS

Some Quality assurance are :

• Quality assurance in application development

• Application design for quality

• Maintenance of application quality

• Maintenance of Data quality

• Post audit evaluation of information system applications.

Quality assurance in application

development The development cycle is designed to support quality assurance in terms of developing a system that meets requirements.Development quality assurance procedures can be operated in – Cooperative mode

Eg; structured walk through for programmers– Adversary mode– Or a combination

Application design for quality

Application designed with high regard for quality assurance in operation tend to have low error rates

Maintenance of Application Quality

It refers to either repairs of errors or enhancements. If repairs are not made, user may loose the confidence in it. If enhancements are not made, users may switch to alternatives. The Maintenance process cycle includes

Identification Analysis Performing change Testing

Maintenance of Data quality

Maintenance of Data quality includes continous

inputs of resources, the ability of an organization to maintain data quality depends on both organizational factors and data factors

Post audit evaluation of information system

applications

It is a review by the task group.

It reviews the objectives and cost benefit representations made on behalf of the project and compares them with actual performance and actual costs

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Quality information systemsIt requires :• commonly-agreed data model, data structure &

metadata• common (standardized) XML schema• common multilingual glossary• Common thesaurus• multilingual and users’ friendly search engine.• efficient data production workflow supporting the

organizational diversity • the development & deployment of added value

services to users• synchronous data exchanges between the

organization portals

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Legacy Systems

Legacy systems are older information system applications that have become crucial to the day-to-day operation of a business and that may use technologies considered old or outdated by current standards.

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Traditional tools and methodologies for quality assurance

• Librarian: This function maintains the custody of programs, files, and documentation

• Process control: Logging in and checking out, checking progress of jobs, maintaining error logs, following up error correction etc.

• Access control: Control of physical access to the system

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Traditional tools and methodologies for quality assurance

• Database administration: control over access to and use of databases

• Backup and Recovery: Preparing backup copies of programs, files and databases

• Application Development quality Assurance: Review of controls planned for application and review of adequacy of testing during development.

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Traditional IS Services Organization

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Contemporary IS Services Organization

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New Approaches to Quality Assurance in Information System• Total quality management (TQM)

• Continuous process improvement (CPI)

• Globalization of the economy

• Information technology problems and opportunities– Year 2000 problem (Y2K)– Euro currency directive– Enterprise resource planning (ERP)– Electronic commerce (EC or E-commerce)

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Total Quality Management• Total quality management (TQM) is a comprehensive

approach to facilitating quality improvements and management within a business.

• Information systems quality standards:– ISO 9001, Quality systems – Model for quality

assurance in design/development, production, installation, and servicing.

– Capability Maturity Model (CMM) is a framework to assess the maturity level of an organization’s information systems development and management processes and products. It consists of five levels of maturity as measured by a set of guidelines called the key process areas.

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Business Process Redesign

Business process redesign (BPR) is the study, analysis, and redesign of fundamental business processes to reduce costs and/or improve value added to the business.

– Usually complemented by continuous process improvement

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Continuous Process Improvement

Continuous process improvement (CPI) is the continuous monitoring of business processes to effect small but measurable improvements to cost reduction and value added.

– Essentially the opposite of business process redesign; however,

– CPI can and frequently does complement BPR.

Technology of Information System

(Information of Information System)

IT Infrastructure IT Infrastructure and and

Emerging Technologies

IT Infrastructure IT Infrastructure and and

Emerging Technologies

Role of Tools in DW Application

BI Environment and Generation of BI Database