Revision. Knowledge Engineering Process of acquiring knowledge from experts and building knowledge base –Narrow perspective Knowledge acquisition, representation,

Revision

Knowledge Engineering

• Process of acquiring knowledge from experts and building knowledge base– Narrow perspective

• Knowledge acquisition, representation, validation, inference, maintenance

– Broad perspective• Process of developing and maintaining

intelligent system

©2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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Knowledge Engineering Process

• Acquisition of knowledge– General knowledge or metaknowledge– From experts, books, documents, sensors, files

• Knowledge representation– Organized knowledge

• Knowledge validation and verification• Inferences

– Software designed to pass statistical sample data to generalizations

• Explanation and justification capabilities


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Development of a Real-Time Knowledge-lead to success.

• Problems with fermentation process– Quality parameters difficult to control– Many different employees doing same task– High turnover

• Expert system used to capture knowledge– Expertise available 24 hours a day

• Knowledge engineers developed system by:– Knowledge elicitation

• Interviewing experts and creating knowledge bases– Knowledge fusion

• Fusing individual knowledge bases– Coding knowledge base– Testing and evaluation of system

Introduction to Knowledge Management

• Knowledge management concepts and definitions. – Knowledge management

The active management of the expertise in an organization. It involves collecting, categorizing, and disseminating knowledge.

– Intellectual capital

The invaluable knowledge of an organization’s employees.

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Elicitation Methods

• Manual– Based on interview– Track reasoning process– Observation

• Semiautomatic– Build base with minimal help from knowledge

engineer– Allows execution of routine tasks with minimal

expert input• Automatic

– Minimal input from both expert and knowledge engineer


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Manual Methods

• Case analysis• Critical incident• User discussions• Expert commentary• Graphs and conceptual models• Brainstorming• Prototyping• Clustering of elements• Iterative performance review


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Semiautomatic Methods

• Repertory grid analysis– Personal construct theory

• Organized, perceptual model of expert’s knowledge• Expert identifies domain objects and their attributes• Expert determines characteristics and opposites for

each attribute• Expert distinguishes between objects, creating a grid

• Expert transfer system– Computer program that elicits information from

experts– Rapid prototyping– Used to determine sufficiency of available

knowledge


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Semiautomatic Methods, continued

• Computer based tools features:– Ability to add knowledge to base– Ability to assess, refine knowledge– Visual modeling for construction of

domain– Creation of decision trees and rules– Ability to analyze information flows– Integration tools


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Automatic Methods

• Data mining by computers• Inductive learning from existing

recognized cases• Neural computing mimicking human

brain• Genetic algorithms using natural

selection


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Evaluation, Validation, Verification

• Dynamic activities– Evaluation

• Assess system’s overall value

– Validation• Compares system’s performance to expert’s• Concordance and differences

– Verification• Building and implementing system correctly• Can be automated


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Artificial Intelligence Rules

• Advantages– Easy to understand, modify, maintain– Explanations are easy to get.– Rules are independent.– Modification and maintenance are relatively easy.– Uncertainty is easily combined with rules.

• Limitations– Huge numbers may be required– Designers may force knowledge into rule-based entities– Systems may have search limitations; difficulties in

evaluation


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Generating Explanations

• Static explanation– Preinsertion of text

• Dynamic explanation– Reconstruction by rule evaluation

• Tracing records or line of reasoning• Justification based on practical

associations• Strategic use of metaknowledge


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Uncertainty

• Probability Ratio– Degree of confidence in conclusion– Chance of occurrence of event

• Bayes Theory– Subjective probability for propositions

• Imprecise• Combines values

• Dempster-Shafer– Belief functions– Creates boundaries for assignments of

probabilities• Assumes statistical independence

Approaches toKnowledge Management

• Process approach to knowledge management attempts to organize organizational knowledge through formalized controls, processes and technologies – Focuses on explicit knowledge and IT

• Practice approach focuses on building the social environments or communities of practice necessary to facilitate the sharing of tacit understanding – Focuses on tacit knowledge and socialization

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Approaches to Knowledge Management

• Hybrid approaches to knowledge management – The practice approach is used so that a

repository stores only explicit knowledge that is relatively easy to document

– Tacit knowledge initially stored in the repository is contact information about experts and their areas of expertise

– Increasing the amount of tacit knowledge over time eventually leads to the attainment of a true process approach

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Information Technology (IT) in Knowledge Management

• The KMS cycle – KMS usually follow a six-step cycle:

1. Create knowledge

2. Capture knowledge

3. Improve (refine) knowledge

4. Store knowledge

5. Manage knowledge

6. Distribute (disseminate) knowledge

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• Components of KMS – KMS are developed using three sets of core

technologies:

1. Communication

2. Collaboration

3. Storage and retrieval– Technologies that support KM

• Artificial intelligence• Intelligent agents• Knowledge discovery in databases• Extensible Markup Language (XML)

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• Intelligent agents – Intelligent agents are software systems that

learn how users work and provide assistance in their daily tasks

– They are used to cause and identify knowledge • See ibm.com, gentia.com for examples

– Combined with enterprise knowledge portal to proffecienly disseminate knowledge

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Roles of People in Knowledge Management

• Chief knowledge officer (CKO)

The person in charge of a knowledge management effort in an organization– Sets KM strategic priorities– Establishes a repository of best practices– Gains a commitment from senior executives– Teaches information seekers how to better elicit it– Creates a process for managing intellectual assets– Obtain customer satisfaction information – Globalizes knowledge management

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• Skills required of a CKO include:– Interpersonal communication skills – Leadership skills – Business wisdom– Strategic thinking– Collaboration skills– The ability to institute effective educational

programs– An understanding of IT and its role in advancing

knowledge management

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• The CEO, other chief officers, and managers– The CEO is responsible for championing a

knowledge management effort – The officers make available the resources needed

to get the job done• CFO ensures that the financial resources are available• COO ensures that people begin to embed knowledge

management practices into their daily work processes• CIO ensures IT resources are available

– Managers also support the KM efforts by providing access to sources of knowledge

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• Community of practice (CoP)

A group of people in an organization with a common professional interest, often self-organized for managing knowledge in a knowledge management system– See Application Case 11.7 as an example

of how Xerox successfully improved practices and cost savings through CoP

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Ensuring the Success of Knowledge Management

Efforts • Useful applications of KMS

– Finding experts electronically and using expert location systems • Expert location systems (know-who)

Interactive computerized systems that help employees find and connect with colleagues who have expertise required for specific problems—whether they are across the county or across the room—in order to solve specific, critical business problems in seconds

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Efforts • Causes of knowledge management failure

– The effort mainly relies on technology and does not address whether the proposed system will meet the needs and objectives of the organization and its individuals

– Lack of emphasis on human aspects– Lack of commitment– Failure to provide reasonable incentive for

people to use the system…

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Efforts • Factors that lead to knowledge

management success – A link to a firm’s economic value, to

demonstrate financial viability and maintain executive sponsorship

– A technical and organizational infrastructure on which to build

– A standard, flexible knowledge structure to match the way the organization performs work and uses knowledge

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management success – A knowledge-friendly culture that leads

directly to user support– A clear purpose and language, to

encourage users to buy into the system– A change in motivational practices, to

create a culture of sharing– Multiple channels for knowledge transfer

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management success – A significant process orientation and

valuation to make a knowledge management effort worthwhile

– Nontrivial motivational methods to encourage users to contribute and use knowledge

– Senior management support

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Agents

• Can act on own or be empowered• Can make some decisions• Can decide when to initiate actions• Unscripted actions• Designed to interact with other agents, programs,

or humans• Automates repetitive, narrowly defined tasks• Continuously running process• Must be believable• Should be transparent• Should work on a variety of machines• May be capable of learning


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Successful Intelligent Agents

• Decision support systems• Employee empowerment for customer

service• Automation of routine tasks• Search and retrieval of data• Expert models


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Classifications Intelligent Agents

• Organization agents– Task execution for processes or applications

• Personal agents– Perform tasks for users

• Private or public agents– Used by single user or many

• Software or intelligent agents– Ability to learn

•Franklin and Graesser’s

autonomous agents


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Characteristics Intelligent Agents

• Agency– Degree of measurable autonomy– Ability to run asynchronously

• Intelligence– Degree of reasoning and learned behavior

• Mobility– Degree to which agents move through networks

and transmit and receive data• Mobile agents

– Nonmobile are two dimensional– Mobile are three dimensional


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Advantages and Limitations Intelligent

Agents• Advantages:

– Easy to understand– Systems and modules easily integrated– Saves development time and expense

• Allows for incremental and rapid development– Updates automatically– Resources reuse

• Limitations:– Oversimplified graphical representation– Needs additional tools– Incorrect definitions– Information may be incorrect or inconsistent– Security


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Management Issues regarding Intelligent

Agents• Expense• Security• Systems integration and flexibility• Hardware and software requirements• Agent accuracy• Agent learning• Invasion of privacy• Competitive intelligence and industrial intelligence• Other ethical issues • Heightened expectations• Systems acceptance

Knowledge

• Explicit knowledge– Objective, rational, technical– Policies, goals, strategies, papers, reports– Codified (organized)– Leaky knowledge

• Tacit knowledge– Subjective, cognitive, experiential learning– Highly personalized– Difficult to formalize– Sticky knowledge (when the one want to keep for himself

or when it turns to be a hidden weapon)

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Organizational Learning

• Learning organization– Ability to learn from past– To improve, organization must learn– Issues (terminologies)

• Meaning, management, measurement– Activities

• Problem-solving, experimentation, learning from past, learning from acknowledged best practices, transfer of knowledge within organization

– Must have organizational memory, way to save and share it• Organizational learning

– Develop new knowledge– Corporate memory history

• Organizational culture– Pattern of shared basic assumptions based on the previous

culture.

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Decision Support Frameworks

• A structured decision (Programmed) is one in which the phases of the decision-making process (intelligence: Searching for conditions that call for decisions. Design: Inventing, developing and analyzing possible courses of action.and choice: Selecting a course of action from those available.)

have standardized procedures, clear objectives, and clearly specified input and output. There exists a procedure for arriving at the best solution .(SIMON’S Idea)

• An unstructured decision (Unprogrammed)is one where not all of the decision-making phases are structured and human plays an important role. (SIMON’S Idea)

• A semistructured decisionhas some, but not all, structured phases where standardized procedures may be used in combination with individual judgment. By intuition Gorry and Scott Morton

Turban, Aronson, LiangSauter

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Emerging Technologies

• Grid computing– Cluster computing power in an organization and utilize unused cycles

for problem solving and other data processing needs.• Improved GUIs

– Due to improvements in web, expectations have risen.• Model-driven architectures with code reuse

– Software reuse and machine generated software by the computer aided software engineering tools has become prevalent.

• M-based and L-based wireless computing– As cellular phones and wireless pc cards are getting less expesive, m-

commerce is evolving. Ex: FedEx uses mobile computer to track shipping packages and analyze patterns

• Intelligent agents: – help users and assist in e-commerce negotiations.

• Genetic algorithms, heuristics and new problem-solving techniques– Distributed as part of Java middleware and other platforms.


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Models Used for DSS

• Iconic– Small physical replication of system, it may be

three dimensional such as that of an airplane, car, or production line. Or two-dimensional such as photographs.

• Analog– Behavioral representation of system– May not look like system Ex. Stock market charts that represent the price

movements of stocks. Animations, videos, and movies.• Quantitative (mathematical)

– Demonstrates relationships between systems used in management science.

The benefits of Models

• Model manipulation is much easier.• Models enable the compression of time.• The cost of modeling is cheaper.• The cost of making mistake over trial and error is

much less.• Risk could be estimated.• Mathematical model use for massive products.• Can model large and extremely complex systems

with possibly infinite solutions• Enhance and reinforce learning, and enhance

training.


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Mathematical Model

• Identify variables• Establish equations describing their relationships• Simplifications through assumptions• Balance model simplification and the accurate

representation of reality.

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson, 6th edition,Copyright 2001, Prentice Hall, Upper Saddle River, NJ


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Decision Support Systems

• Intelligence Phase– Automatic

• Data Mining– Expert systems, CRM, neural networks

– Manual• OLAP• KMS

– Reporting• Routine and ad hoc


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• Design Phase– Financial and forecasting models– Generation of alternatives by expert

system– Business process models from CRM,

ERP, and SCM


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• Choice Phase– Identification of best alternative– Identification of good enough alternative– What-if analysis– Goal-seeking analysis– May use KMS, GSS, CRM, ERP, and

SCM systems


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• Implementation Phase– Improved communications– Collaboration– Training– Supported by KMS, expert systems,

GSS


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DSS Models

• Algorithm-based models• Statistic-based models• Linear programming models• Graphical models• Quantitative models• Qualitative models• Simulation models


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Major Modeling Issues

• Problem identification– Environmental scanning and analysis– Business intelligence tools // they can help

identifying the problem by scanning for them.

• Identify variables and relationships– Influence diagrams– Cognitive maps

• Forecasting– Fueled by e-commerce– Increased amounts of information available

through technology


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MSS Mathematical Models

– Decision variables describe alternative choices they could be people, time and schedules.

– Uncontrollable variables are outside decision-maker’s control these factors con be fixed, in which case they are called parameters and they can vary.

– Fixed factors are parameters. – Intermediate outcomes produce intermediate

result variables.– Result variables are dependent on chosen

solution and uncontrollable variables.


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Mathematical Programming optimization

linear programmingLP Characteristics: • A limited quantity of economic resources is available

for allocation.• The resources are used in the production of products

or services. • There are two or more ways in which the resources

can be used. Each is called a solution or a program.• Each activity (product or service) in which the

resources are used yields a return in terms of the stated goal.

• The allocation is usually restricted by several limitations & requirements called constraints.


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linear programmingLP allocation model is based on the following rational economic assumptions:

• Return from different allocation can be measured & compared.

• The return from any allocation is independent of other allocations.

• The total return is the sum of the returns yielded by the different activities.

• All data are known with certainty.• The resources are to be used in the most economical

manner.


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linear programming


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The most common optimization models can be solved by a variety of mathematical programming methods, they are:


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Data Warehouse


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Data Warehouse

Data Warehouse Design

Star schema

The data warehouse is based on the concept of dimensional modeling.

Dimensional modeling is a retrieval based model that supports high volume query access.

The star schema is the means of implementing the dimensional modeling.

©2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th

Edition, Turban, Aronson, and Liang5-56


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Data Warehouse Development

• Data warehouse implementation techniques – Top down– Bottom up– Hybrid– Federated

• Projects may be data centric or application centric• Implementation factors

– Organizational issues– Project issues– Technical issues

• Scalable = expandable. • Flexible


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Data Warehouse


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Data Marts

Data marts is a subset of a data warehouse, typically consisting of a single subject area (e.g., marketing, personnel…).

Data mart can be either dependent or independent


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Data Marts

• Dependent is : – Created from warehouse by replication. It is

copied from data warehouse, it is built after the building of warehouse.• Functional subset of warehouse

• Independent– Scaled down, less expensive version of data

warehouse– Designed for a department or SBU– Organization may have multiple data marts

• Difficult to integrate


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Business Intelligence and Analytics

• Business intelligence– Acquisition of data and information for

use in decision-making activities• Business analytics

– Models and solution methods• Data mining

– Applying models and methods to data to identify patterns and trends

DASHBOARDS

Dashboard provides the managers with exactly the information they need in the correct format at the correct time. BI systems are the foundation of dashboard, dashboards and scorecards measure and display what is important.

It provide a real time view of data.


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OLAP

• Activities performed by end users in online systems– Specific– User can ask open-ended questions– Query generation

• SQL– Ad hoc reports– Statistical analysis– Building DSS applications

• Modeling and visualization capabilities• Special class of tools // using SQL is helpful but not sufficient

for OLAP here a special class of tools is used, known as :-– DSS/BI/BA front ends– Data access front ends– Database front ends– Visual information access systems


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OLAP

The rules to evaluate OLAP on are : - 1. Accessibility.2. Transparency.3. Multimedia conceptual view.4. Consistence reporting performance.5. Client – server architecture.6. Generic dimensionality.7. Multi- user support.8. Flexible reporting.9. Intuitive data manipulation.10.Unlimited dimension & aggregation level.11. Unrestricted cross dimensional operation.


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Data Mining

Data mining solving these classes of problems– Classification– Clustering– Association– Sequencing // like association but over a period of

time.– Regression // form of estimation.– Forecasting– Others

• Hypothesis (we assume a situation & start investigation)or discovery driven (it come from the facts).


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Agile Development

• Rapid prototyping• Used for:

– Unclear or rapidly changing requirements

– Speedy development• Heavy user input• Incremental delivery with short time

frames• Tend to have integration problems


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DSS Prototyping

• Advantages– User and management involvement– Learning explicitly integrated– Prototyping bypasses information requirement– Short intervals between iterations– Low cost– Improved user understanding of system

• Disadvantages– Changing requirements– May not have thorough understanding of benefits and

costs– Poorly tested– Dependencies, security, and safety may be ignored– High uncertainty– Problem may get lost– Reduction in quality– Higher costs due to multiple productions


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DSS


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DSS


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Documents

Revision. Knowledge Engineering Process of acquiring knowledge from experts and building knowledge base –Narrow perspective Knowledge acquisition, representation,