Transcript Chapter 15: KNOWLEDGE-BASED INFORMATION SYSTEMS

Chapter 15:
KNOWLEDGE-BASED
INFORMATION
SYSTEMS
What is Knowledge?
• Data: Raw facts, e.g., Annual Expenses = $2
million
• Information: Data given context, e.g., current
annual expenses of $2 million are $1 million
above last year’s annual expenses of $1 million
• Knowledge: Information given meaning &
application, e.g., Additional expenses were used
for R&D which resulted in a new product line
which will increase our revenues by 50%. Can
use this to set next years budget and provide
accurate revenue forecasts.
Knowledge ctd.
• Organizational Learning: using
organizational experience to create new
SOPs, processes, & products/services
• Knowledge Management: The set of
processes developed in an organization to
create, gather, store, maintain, and
disseminate the firm’s knowledge
• Chief Knowledge Officer (CKO): Senior
executive in charge of knowledge
management program
Process of Organizational Learning (Hine et al. 1998)
KEY
KS: Knowledge Sources (1..n)
M: Managers (1..p)
In: Individual Manager's Interpretations (1..p)
Co: Consesus Knowledge (1..q)
C: Conflict Knowledge (1..r)
: Unresolveable Conflict
Consensus Set
Co1
Co2
Organizational
Memory
Coq
KS1
KS2
M1
In1
KS3
M2
In2
Global
Interpretation +

Organizational
Decision-Making
Conflict Set
C1
KS4
KSn
Mp
Inp
Address
Conflict
C2
Cr
Environmental Distribution
Scanning
of Knowledge
Systems Structural
Perspective
Development of
Individual
Environmental
Interpretations
Development of Shared Understanding
Interpretive Perspective
Types of Knowledge
• Explicit Knowledge
– Structured internal knowledge. Has been documented
and in computer files & databases, policy & procedure
manuals, etc.
• Tacit Knowledge
– Expertise and experience of organizational members
that has not been formally documented
• Organizational Memory
– The stored learning from an organization’s history, used
for decision-making and other purposes
Knowledge Work and
Productivity
SHARE
KNOWLEDGE
DISTRIBUTE
KNOWLEDGE
GROUP
COLLABORATION
SYSTEMS
OFFICE
AUTOMATION
SYSTEMS
ARTIFICIAL
INTELLIGENCE
SYSTEMS
KNOWLEDGE
WORK
SYSTEMS
CAPTURE,
CODIFY
KNOWLEDGE
CREATE
KNOWLEDGE
Artificial Intelligence (AI)
• Computer-based systems that try to mimic human
intelligence
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Natural Language
Robotics
Artificial Vision
Expert Systems
Others (neural nets, genetic algorithms)
Business Interests in AI
• Store information in organizational memory
so expertise not lost when personnel turns
over
• Create a mechanism not subject to feelings,
fatigue, worry, crisis
• Eliminate routine / unsatisfying jobs
• Solutions to problems that are too complex for
humans to do in short periods of time
Natural Language Processing
• List names and addresses of clients from the
midwest whose year to date order total is less than
$50,000
• Show me the names and addresses of our
midwestern clients whose year to date balance is
less than $50,000
• List names and addresses of clients with a year to
date balance of less than $50,000 who are
headquartered in the midwest
SELECT Name, Address FROM Client
WHERE Region = “Midwest”
AND YTD_Ord < 50000
Expert Systems
• A knowledge-intensive program that captures the expertise of
humans in limited domains of knowledge
• Rule - based expert system
– AI system based on IF - THEN statements
• Knowledge base
– Model of human knowledge
• Inference engine
– Search through rule base can be either
• Forward chaining: Take input, search rules for answer
• Backward chaining: begin with goal; seek information until goal
is achieved or not.
• Expert System Shell
– Programming environment of expert system
Examples of Expert Systems
• CLUES
– Loan underwriting
• Blue Cross Blue Shield
– Medical underwriting system
• United Nations
– Calculates employee salaries
• XCON
– Mainframe configuration
Case-Based Reasoning (CBR)
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uses database of cases and solutions
User describes problem
System searches database for similar cases
System asks more questions
Finds closest fit and retrieves solution
Modify solution as required
Stores problem and new solution in case base
Other Intelligent Techniques
• Neural Networks
– Software attempts to emulate brain processes
• Fuzzy Logic
– Tolerates ambiguity using nonspecific terms within if-then rules
(for example: if truck is close to loading dock then sound
warning)
• Genetic Algorithms
– Uses models of organisms to promote evolution of solution
• Intelligent Agents/bots
– Carries out specific, repetitive tasks