Download Design and Development of DSS

Building KnowledgeDriven DSS and
Mining Data
We can organize knowledge
and make it available for
business uses
Knowledge driven DSS
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Knowledge-driven DSS, suggestion DSS,
rule-based DSS and intelligent DSS are
overlapping terms for DSS built using artificial
intelligence technologies
KDSS provide suggestions for business
decision makers
Building KDSS and Mining Data, D. J. Power
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KDSS/Expert Systems
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Three Components
 Knowledge
of symptoms and indicators related to
a particular topic or domain
 Understanding of the relations among symptoms
and of problems and solutions within that domain
 “Skill” or methods for solving some of the
problems

A KDSS/ES can give the reasoning behind a
conclusion it has reached
Building KDSS and Mining Data, D. J. Power
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Data Mining and Knowledge
Discovery

Helps companies discover hidden
relationships and patterns in their data
 Some
feel Data Mining can help a company gain
a competitive advantage

Knowledge Discovery is the broader term
Building KDSS and Mining Data, D. J. Power
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Al Vocabulary
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Knowledge Acquisition – The extraction and
formulation of knowledge derived from various
sources, especially from experts
Development environment – software for creating
and maintaining a knowledge base and software
called an inference engine
Inference engine processes a set of rules
Domain expert – person who has expertise in the
task for which a specific system is being
developed
Building KDSS and Mining Data, D. J. Power
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Knowledge Base
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A knowledge base is a collection of organized
facts, rules, and procedures
Knowledge base has a description of the
elements in the process along with their
characteristics, functions, and relationships
Also contains rules about actions to
implement as a result of certain events
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Characteristics of KDSS
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Aids/assists managers
in problem solving
Use knowledge stored
as rules, frames, or
probabilities
People interact with a
Knowledge-Driven DSS
when performing a
specific task

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Knowledge-Driven DSS
end Expert Systems
base recommendations
on human knowledge
and assist in performing
very limited tasks
KDSS does NOT “think”
Building KDSS and Mining Data, D. J. Power
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Components of a KDSS
Inference
Engine Model
Component
Database or
Knowledge base
Component
Communications
Component
User interface
component
Building KDSS and Mining Data, D. J. Power
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Knowledge-Driven vs. ModelDriven DSS
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Knowledge-Driven DSS based on Expert
System technologies attempts to reason
about an input using its knowledge base and
logical rules for problem solving
Model-Driven DSS has a sequence of
predefined instructions (a model) for
responding to a change in inputs
Building KDSS and Mining Data, D. J. Power
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Comparing KDSS to MDSS
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Knowledge-Driven DSS = Knowledge base +
Inference Engine (rule processor)

Model-Driven DSS = Structured Data +
Quantitative Models
Building KDSS and Mining Data, D. J. Power
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Knowledge-Driven DSS Design
and Development

Involves a development team and a domain
expert creating a knowledge base for a target
user group
 Waterman
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proposed a widely accepted approach:
Identification of domain
Conceptualization
Formalization
Implementation
Testing
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Choosing a project – Telephone
test
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Can a domain expert solve the problem and
support decision making using a telephone
exchange with a decision maker?
Ask structured rather than open-ended
questions
“Yes” the telephone exchange works; then a
KDSS can be developed
“No” development of a KDSS will likely be
unsatisfactory
Building KDSS and Mining Data, D. J. Power
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Rules

Interconnected and nested IF-THEN statements
IF INCOME > $45,000 (condition)
AND IF SEX = “M” (condition)
THEN ADD to Target list (action)
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A rule is a formal way of specifying a recommendation,
directive, or strategy, expressed in an IF premise THEN
conclusion structure
Rules are relationships rather than instructions
Chaining
Forward – start from known facts looks at
IF statements to find matches to user’s
responses
 Backward – start with a hypothesized
solution looks at THEN statements works
backwards to find the starting conditions
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Chaining
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You want to find a flight from CC to Beijing,
China:
Forward
 Start
by listing all flights leaving CC and mark
intermediary cities. Look for flights out of
intermediaries until you find all paths to Beijing. You
are working forward toward your goal. This is a datadriven

Backward
 Start
with flights that arrive in Beijing and work
backwards to eventually find a chain to CC, TX. This
is a goal-driven
Advantages and Limitations of
Rules
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Rules are easy to understand
Explanations are easy to provide when
knowledge is stored as rules
Modification and maintenance are relatively
easy
Can easily combine uncertainty with knowledge
Major limitation
 The
most important complex knowledge is difficult to
represent using rules
 Using rules does not work for all applications
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Knowledge-Driven DSS
Examples
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TAXADVISOR
 Designed
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to assist with large estate planning
XCON
 Developed
to configure VAX 11/780 computer
system

Customer Support Intelligent System
 Designed
by Compaq to provide expert diagnosis
and recommendations to computer users who
were having computer problems
Building KDSS and Mining Data, D. J. Power
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Data Mining

Predictive
 Can
be used to forecast explicit values, based on
patterns determined from known results

For example, from a database of customers who have already
responded to a particular offer, a model can be built that predicts
which prospects are likeliest to respond to the same offer.
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Descriptive
 Describe
patterns in existing data and are
generally used to create meaningful subgroups
such as demographic clusters

Once a descriptive model is identified it may be used for target
marketing or other decision support tasks
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Data Mining Tools
Case-Based Reasoning - similarity
 Data Visualization – graphical presentation
 Fuzzy Query and Analysis – “close”
 Genetic Algorithms – optimization
 Neural Networks – learn patterns from data
Data Mining techniques are NOT fundamentally
different from earlier quantitative and statistical
analysis techniques – they are still used

Building KDSS and Mining Data, D. J. Power
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Data Mining Process

As defined by the Gartner Group
 Select
and prepare the data to be mined
 Qualify the data via cluster and feature analysis
 Select one or more data mining tools
 Apply the data mining tool
 Apply the knowledge discovered to the
company’s specific line of business to achieve a
business goal
Building KDSS and Mining Data, D. J. Power
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Data Mining Examples
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Credit risk analysis
Siemens Technical Customer support – case
based reasoning
Building KDSS and Mining Data, D. J. Power
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Evaluating Development
Packages
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Cost
Scalability
Security
Development Features
Ease of Installation and Use
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