SOLUTION: Texas A&M University Kingsville Aspects of Knowledge Management Discussion

Business Intelligence and Analytics:
Systems for Decision Support
(10th Edition)
Chapter 12:
Knowledge Management and
Collaborative Systems
Learning Objectives







12-2
Define knowledge and describe the different
types of knowledge
Describe the characteristics of KM
Describe the KM cycle
Describe the technologies that can be used in a
knowledge management system (KMS)
Describe different approaches to KM
Understand the basic concepts and processes of
groupwork, communication, and collaboration

(Continued…)
Copyright © 2014 Pearson Education, Inc.
Learning Objectives





12-3
Describe how computer systems facilitate
communication and collaboration in an enterprise
Explain the concepts and importance of the
time/place framework
Explain the underlying principles and capabilities
of groupware (group support systems—GSS)
Understand how the Web enables collaborative
computing and group support of virtual meetings
Describe the role of emerging technologies in
supporting collaboration
Copyright © 2014 Pearson Education, Inc.
Opening Vignette
Expertise Transfer System to Train
Future Army Personnel





12-4
Background
Problem description
Proposed solution
Results
Answer & discuss the case questions…
Copyright © 2014 Pearson Education, Inc.
Opening Vignette…
12-5
Copyright © 2014 Pearson Education, Inc.
Questions for
the Opening Vignette
1.
2.
3.
12-6
What are the key impediments to the use
of knowledge in a knowledge
management system?
What features are incorporated in a
knowledge nugget in this
implementation?
Where else could such a system be
implemented?
Copyright © 2014 Pearson Education, Inc.
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
12-7
Copyright © 2014 Pearson Education, Inc.
Introduction to
Knowledge Management

Knowledge is



information that is contextual, relevant, and
actionable
understanding, awareness, or familiarity
acquired through education or experience
anything that has been learned, perceived,
discovered, inferred, or understood.
In a knowledge management system,
“knowledge is information in action”
12-8
Copyright © 2014 Pearson Education, Inc.
Introduction to
Knowledge Management
Data
Processed
Information
Relevant and
Actionable
Knowledge
DEPLOYMENT CHART
Database
PHASE 1
PHASE 2
PHASE 3
PHASE 4
PHASE 5
DEPT 1
DEPT 2
DEPT 4
1
2
3
4
5
Relevant and actionable processed-data
12-9
Copyright © 2014 Pearson Education, Inc.
Wisdom
DEPT 3
Introduction to
Knowledge Management

Characteristics of knowledge





12-10
Extraordinary leverage and increasing returns
Fragmentation, leakage, and the need to
refresh
Uncertain value
Uncertain value of sharing
Knowledge-based economy
The economic shift from natural resources to
intellectual assets
Copyright © 2014 Pearson Education, Inc.
Introduction to
Knowledge Management

Explicit and tacit knowledge



12-11
Explicit (leaky) knowledge
Knowledge that deals with objective,
rational, and technical material (data,
policies, procedures, software, documents,
etc.)
Easily documented, transferred, taught,
and learned
Examples…
Copyright © 2014 Pearson Education, Inc.
Introduction to
Knowledge Management

Explicit and tacit knowledge





12-12
Tacit (embedded) knowledge
Knowledge that is usually in the domain of
subjective, cognitive, and experiential
learning.
It is highly personal and hard to formalize.
Hard to document, transfer, teach, & learn
Involves a lot of human interpretation
Examples…
Copyright © 2014 Pearson Education, Inc.
Taxonomy of Knowledge
12-13
Copyright © 2014 Pearson Education, Inc.
Organizational Knowledge Learning and Transformation


12-14
Learning organization
An organization capable of learning from
its past experience, implying the existence
of an organizational memory and a means
to save, represent, and share it through its
personnel
Organizational memory
Repository of what the organization knows
Copyright © 2014 Pearson Education, Inc.
Organizational Knowledge Learning and Transformation

Organizational culture
The aggregate attitudes in an
organization concerning a certain issue
(e.g., technology, computers, DSS)




12-15
How do people learn the “culture”?
Is it explicit or implicit?
Can culture be changed? How?
Give some examples of corporate culture:
Microsoft, Google, Apple, HP, GM, …
Copyright © 2014 Pearson Education, Inc.
Approaches to
Knowledge Management

Process approach to knowledge management
attempts to codify organizational knowledge
through formalized controls, processes and
technologies


Practice approach focuses on building the social
environments or communities of practice
necessary to facilitate the sharing of tacit
understanding

12-16
Focuses on explicit knowledge and IT
Focuses on tacit knowledge and socialization
Copyright © 2014 Pearson Education, Inc.
Approaches to
Knowledge Management

Hybrid approaches to knowledge
management

Hybrid
at
80/20
to
50/50


12-17
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
Copyright © 2014 Pearson Education, Inc.
Approaches to
Knowledge Management


12-18
Best practices
In an organization, the best methods
for solving problems. These are often stored
in the knowledge repository of a knowledge
management system
Knowledge repository is the actual
storage location of knowledge in a
knowledge management system. Similar in
nature to a database, but generally textoriented
Copyright © 2014 Pearson Education, Inc.
KNOWLEDGE MANAGEMENT PLATFORM (KMP)
12-19
KNOWLEDGE PORTAL
(Web-based End User Interface)
Human Experts
Intelligent Broker
KNOWLEDGE REPOSITORY
(Knowledge / Information / Data Nuggets)
KNOWLEDGE CREATION
A
Comprehensive
View to
Knowledge
Repository
KNOWLEDGE UTILIZATION
Approaches to
Knowledge Management
Web Crawler
Data/Text Mining Tools
DIVERSE INFORMATION / DATA SOURCES
(Weather / Medical Info / Finance / Agriculture / Industrial)
Copyright © 2014 Pearson Education, Inc.
Ad hoc
Search
JUN
1
5
Manual
Entries
Information Technology (IT) in
Knowledge Management

The KMS cycle

12-20
KMS usually follow a six-step cycle:
1. Create knowledge
2. Capture knowledge
3. Refine knowledge
4. Store knowledge
5. Manage knowledge
6. Disseminate knowledge
Copyright © 2014 Pearson Education, Inc.
Information Technology (IT) in
Knowledge Management
The Cyclic Model
of Knowledge
1
Create
Management
Knowledge
Capture
Knowledge
2
Refine
Knowledge
6
Disseminate
Knowledge
Store
Knowledge
Manage
Knowledge
12-21
Copyright © 2014 Pearson Education, Inc.
5
3
4
Information Technology (IT) in
Knowledge Management
Components of KMS



12-22
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
◼ Web 2.0, …
Copyright © 2014 Pearson Education, Inc.
Characteristics of Groupwork







12-23
Groupwork → the work done by two or more
people together
A group performs a task
Members may be located in different places
Group members may work at different times
Group members may work for the same
organization or for different organizations
A group can be permanent or temporary
A group can be at one managerial level or span
several levels …
Copyright © 2014 Pearson Education, Inc.
Why Groupwork/Collaborate?
Make Decisions
Build Trust
Synergy
Share the Vision
Share Work
Build Consensus
12-24
Review
Share Information
Solve Problems
Socialize
Copyright © 2014 Pearson Education, Inc.
Group Decision-Making Process

Why? Because no one has all the







12-25
Experience
Knowledge
Resources
Insight, and
Inspiration
… to do the job alone.
Difficult decisions require group of people
Virtual teams?
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Groupwork
12-26
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Groupwork –
Process Gains and Losses
12-27
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Supporting Groupwork –
Group Support Systems
▪ Goal: to support groupwork
▪ Increase benefits / decrease losses
▪ Based on traditional methods
▪ Nominal Group Technique
“Individuals work alone to generate ideas which are
pooled under guidance of a trained facilitator”
▪ Delphi Method
“A structured process for collecting and distilling
knowledge from a group of experts by means of
questionnaires”
▪ Electronic Meeting System (EMS)
12-28
Copyright © 2014 Pearson Education, Inc.
Groupware
▪ Lotus Notes / Domino Server
Includes Learning Space






12-29
Netscape Collabra Server
Microsoft NetMeeting
Novell Groupwise
GroupSystems
TCBWorks
WebEx
Copyright © 2014 Pearson Education, Inc.
A Time/Place Communication
Framework for Groupwork
12-30
Copyright © 2014 Pearson Education, Inc.
Tools for Indirect Support of
Decision Making



Groupware products provide a way for
groups to share resources and opinions
Synchronous or Asynchronous
Examples





12-31
dropbox.com
drive.google.com
office.microsoft.com

See Table 12.5 for a list of examples
Copyright © 2014 Pearson Education, Inc.
Groupware…

Virtual Meeting Systems




GroupSystems (Groupsystems.com)
Collaborative Workflow
Web 2.0



12-32
webex.com, gotomeeting.com, Skype.com, …
Search, links, authoring, tags, extensions,
signals
Wikis
Collaborative Networks
Copyright © 2014 Pearson Education, Inc.
Group Decision Support Systems




12-33
It is an interactive computer-based system
that facilitates the solution of
semistructured or unstructured problems
by a group of decision makers
Goal – support group decision making
A specially designed IS to enhance
collaborative decision processes
It encourages generation of ideas, freedom
of expression, and resolution of conflicts
Copyright © 2014 Pearson Education, Inc.
GDSS – Pros and Cons
Gains:






Parallelism
Anonymity →
Triggering
Synergy
Structure
Record keeping
Loses:
9#
▪ Free-riding
▪ Flaming
12-34
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Facilities for GDSS
▪ Decision room
▪ Multiple-use facility
▪ Web based
12-35
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The Decision Room






12-36
12 to 30 networked personal computers
Usually recessed into the desktop
Server PC
Large-screen projection system
Breakout rooms
Need a trained facilitator for success
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Cool Decision Rooms
IBM Corp.
12-37
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Cooler Decision Rooms
US Air Force
12-38
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Mobile Decision Rooms
Murraysville School District Bus
12-39
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On-Demand Decision Rooms
4
12-40
Copyright © 2014 Pearson Education, Inc.
Very Few Organizations Use
Decision Rooms
▪ High Cost
▪ Need for a Trained Facilitator
▪ Requires Specific Software Support for
Different Cooperative Tasks
▪ Infrequent Use
▪ Different Place / Different Time Needs
▪ May Need More Than One
12-41
Copyright © 2014 Pearson Education, Inc.
End-of-Chapter Application Case
Solving Crimes by Sharing Digital
Forensic Knowledge




12-42
Background
Problem description
Proposed solution
Results
Copyright © 2014 Pearson Education, Inc.
End of the Chapter

12-43
Questions, comments
Copyright © 2014 Pearson Education, Inc.
All rights reserved. No part of this publication may be reproduced,
stored in a retrieval system, or transmitted, in any form or by any
means, electronic, mechanical, photocopying, recording, or otherwise,
without the prior written permission of the publisher. Printed in the
United States of America.
12-44
Copyright © 2014 Pearson Education, Inc.
Business Intelligence and Analytics:
Systems for Decision Support
(10th Edition)
Chapter 11:
Automated Decision Systems
and Expert Systems
Learning Objectives





Understand the concept and applications of
automated rule-based decision systems
Understand the importance of knowledge in
decision support
Describe the concept and evolution of rulebased expert systems (ES)
Understand the architecture of rule-based ES
Learn the knowledge engineering process
used to build ES
(Continued…)
11-2
Copyright © 2014 Pearson Education, Inc.
Learning Objectives



11-3
Explain the benefits and limitations of
rule-based systems for decision support
Identify proper applications of ES
Learn about tools and technologies for
developing rule-based DSS
Copyright © 2014 Pearson Education, Inc.
Opening Vignette…
InterContinental Hotel Group Uses
Decision Rules for Optimal Hotel Room
Rates





11-4
Company background
Problem description
Proposed solution
Results
Answer & discuss the case questions…
Copyright © 2014 Pearson Education, Inc.
Questions for
the Opening Vignette
1.
2.
3.
11-5
Describe the challenges faced by IHG
during development of their retail price
optimization system.
Besides the hotel business in the
hospitality industry, explain at least three
other areas where an optimization model
could be used.
What other methods could be used to
solve IHG’s price optimization problem?
Copyright © 2014 Pearson Education, Inc.
Automated Decision Systems



A relatively new approach to supporting
decision making
a.k.a. Decision Automation Systems (DAS)
Often a rule-based system that provides a
solution in a functional area


11-6
“If only 70 percent of the seats on a flight from
LA to NY are sold 3 days prior to departure,
offer a discount of x to nonbusiness travelers”
Applies to repetitive/structured decisions
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Application Case 11.1
Giant Food Stores Prices the
Entire Store




11-7
Company background
Problem description
Proposed solution
Results
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Automated Decision-Making
Framework
11-8
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Architecture of the Airline
Revenue Management Systems
11-9
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Artificial Intelligence (AI)

Artificial intelligence (AI)


A subfield of computer science, concerned
with symbolic reasoning and problem solving
AI has many definitions…
Behavior by a machine that, if performed by a
human being, would be considered intelligent
◼ “…study of how to make computers do things
at which, at the moment, people are better
◼ Theory of how the human mind works

11-10
Copyright © 2014 Pearson Education, Inc.
AI Objectives

Make machines smarter (primary goal)
Understand what intelligence is
Make machines more intelligent & useful

Signs of intelligence…







11-11
Learn or understand from experience
Make sense out of ambiguous situations
Respond quickly to new situations
Use reasoning to solve problems
Apply knowledge to manipulate the environment
Copyright © 2014 Pearson Education, Inc.
Test for Intelligence
Turing Test for Intelligence
◼ A computer can be
considered to be smart
only when a human
interviewer, “conversing”
with both an unseen
human being and an
unseen computer, can
not determine which is
which.
– Alan Turing
11-12
Copyright © 2014 Pearson Education, Inc.
Questions / Answers
Intelligent tutoring
The AI Field…

AI provides
the scientific
foundation
for many
commercial
technologies
Speech Understanding
Natural Language Processing
Voice Recognition
Automatic Programming
Machine Learning
Applications
Computer Vision
Neural Networks
Genetic Algorithms
Game Playing
Expert Systems
Fuzzy Logic
The AI
Tree
Mathematics
Computer Science
Philosophy
Human Behavior
Disciplines
The
Disciplines
and
Applications
of AI.
Intelligent Agents
Autonomous Robots
Neurology
Engineering
Logic
Robotics
Management Science
Information Systems
Sociology
Statistics
Psychology
Pattern Recognition
Human Cognition
Linguistics
Biology
AI Areas

Major…
Expert Systems
◼ Natural Language Processing
◼ Robotics and Sensory Systems
◼ Computer Vision and Scene Recognition
◼ Intelligent Computer-Aided Instruction
◼ Automated Programming, Neural Computing


Additional…
Fuzzy Logic, Genetic Algorithms
◼ Game Playing, Intelligent Software Agents …

11-14
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AI is Often Transparent in Many
Commercial Products




Anti-lock Braking Systems (ABS)
Automatic Transmissions
Video Camcorders
Appliances




11-15
Washers, Toasters, Stoves, …
Help Desk Software
Subway Control

Copyright © 2014 Pearson Education, Inc.
Expert Systems (ES)


Is a computer program that attempts to imitate
expert’s reasoning processes and knowledge in
solving specific problems
Most Popular Applied AI Technology




Works best with narrow problem areas/tasks
Expert systems do not replace experts, but


11-16
Enhance Productivity
Augment Work Forces
Make their knowledge and experience more widely
available, and thus
Permit non-experts to work better
Copyright © 2014 Pearson Education, Inc.
Important Concepts in ES

Expert
A human being who has developed a high level of
proficiency in making judgments in a specific domain

Expertise
The set of capabilities that underlines the
performance of human experts, including




11-17
extensive domain knowledge,
heuristic rules that simplify and improve approaches to
problem solving,
meta-knowledge and meta-cognition, and
compiled forms of behavior that afford great economy
in a skilled performance
Copyright © 2014 Pearson Education, Inc.
Features and Concepts in ES

Experts / Expertise
Degrees or levels of expertise
◼ Ratio of non-experts to experts → 100 to 1


Transferring Expertise



11-18
From expert to computer to nonexperts via
acquisition, representation, inferencing,
transfer
Symbolic Reasoning / Inferencing
Deep Knowledge / Self Knowledge
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Conventional vs. Expert Systems
Continued…
11-19
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Conventional vs. Expert Systems

11-20
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Application Case 11.2
Expert System Helps in Identifying
Sport Talents




11-21
Background
Problem description
Proposed solution
Results
Copyright © 2014 Pearson Education, Inc.
Applications of Expert Systems

Classical Applications

DENDRAL



MYCIN




11-22
A rule-based expert system
Used for diagnosing and treating bacterial infections
XCON


Applied knowledge (i.e., rule-based reasoning)
Deduced likely molecular structure of compounds
A rule-based expert system
Used to determine the optimal information systems
configuration
New applications: Credit analysis, Marketing, Finance,
Manufacturing, Human resources, Science and
Engineering, Education, …
Copyright © 2014 Pearson Educ …
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