Chapter 12Knowledge Management and Collaborative Systems

Chapter 12:
Knowledge Management and Collaborative Systems
Business Intelligence and Analytics: Systems for Decision
Support
(10th Edition)
Support
(10th Edition)
Copyright © 2014 Pearson Education, Inc.
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Learning Objectives
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…)
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Learning Objectives
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
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Opening Vignette
Expertise Transfer System to Train Future Army Personnel
Background
Problem description
Proposed solution
Results
Answer & discuss the case questions...

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Opening Vignette…
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Questions for
the Opening Vignette
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?
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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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Introduction to
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”
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Introduction to
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Introduction to
Characteristics of knowledge
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

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Introduction to
Explicit and tacit knowledge
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…
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Introduction to
Explicit and tacit knowledge
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…
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Taxonomy of Knowledge

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Organizational Knowledge -
Learning and Transformation
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
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Organizational Knowledge -
Learning and Transformation
Organizational culture
The aggregate attitudes in an organization concerning a certain
issue (e.g., technology, computers, DSS)
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, …
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Process approach to knowledge management attempts to codify
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
Approaches to
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Approaches to
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
Hybrid at
80/20
to
50/50

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Approaches to
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 text-oriented

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Approaches to
A Comprehensive View to Knowledge Repository
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Information Technology (IT) in Knowledge Management
The KMS cycle
KMS usually follow a six-step cycle:
Create knowledge
Capture knowledge

Refine knowledge
Store knowledge
Manage knowledge
Disseminate knowledge
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The Cyclic Model of Knowledge Management
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Components of KMS
KMS are developed using three sets of core technologies:
Communication
Collaboration
Storage and retrieval
Technologies that support KM
Artificial intelligence
Intelligent agents
Knowledge discovery in databases
Web 2.0, …
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Characteristics of Groupwork
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 …
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Why Groupwork/Collaborate?

Review
Share Work
Share the Vision
Socialize
Build Consensus
Solve Problems
Make Decisions
Synergy
Share Information
Build Trust
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Group Decision-Making Process
Why? Because no one has all the

Experience
Knowledge
Resources
Insight, and
Inspiration
… to do the job alone.
Difficult decisions require group of people
Virtual teams?
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Groupwork
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Groupwork –
Process Gains and Losses
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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)
Supporting Groupwork –
Group Support Systems

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Lotus Notes / Domino Server
Includes Learning Space
Netscape Collabra Server
Microsoft NetMeeting
Novell Groupwise
GroupSystems
TCBWorks
WebEx
Groupware
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A Time/Place Communication Framework for Groupwork
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Tools for Indirect Support of Decision Making
Groupware products provide a way for groups to share resources
and opinions
Synchronous or Asynchronous
Examples
dropbox.com
drive.google.com
office.microsoft.com
…

See Table 12.5 for a list of examples
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Groupware…
Virtual Meeting Systems
webex.com, gotomeeting.com, Skype.com, …
GroupSystems (Groupsystems.com)
Collaborative Workflow
Web 2.0
Search, links, authoring, tags, extensions, signals
Wikis
Collaborative Networks
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Group Decision Support Systems
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
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Gains:
Parallelism
Triggering
Synergy
Structure
Record keeping

Loses:
Free-riding
Flaming
GDSS – Pros and Cons
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Decision room
Multiple-use facility
Web based
Facilities for GDSS

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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
The Decision Room
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IBM Corp.
Cool Decision Rooms
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US Air Force
Cooler Decision Rooms
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Murraysville School District Bus
Mobile Decision Rooms
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On-Demand Decision Rooms

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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
Very Few Organizations Use Decision Rooms
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End-of-Chapter Application Case
Solving Crimes by Sharing Digital Forensic Knowledge
Background
Problem description
Proposed solution
Results
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End of the Chapter
Questions, comments

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43
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.
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44

Processed
Relevant and
Actionable
Relevant and actionable processed-data
Database
PHASE 5
DEPT 4
DEPT 3
DEPT 2
DEPT 1
PHASE 4PHASE 3PHASE 2PHASE 1
DEPLOYMENT CHART
1
2
3
4
5
Data
Information
Knowledge
W
i
s
d
o
m
KNOWLEDGEMANAGEMENTPLATFORM(KMP)
Human Experts
KNOWLEDGE PORTAL
(Web-based End User Interface)
Intelligent Broker
KNOWLEDGE REPOSITORY
(Knowledge / Information / Data Nuggets)
Web CrawlerData/Text Mining Tools
�

Manual
Entries
DIVERSE INFORMATION / DATA SOURCES
(Weather / Medical Info / Finance / Agriculture /
Industrial)
Ad hoc
Search
K
N
O
W
L
E
D
G
E
C
R
E
A
T
I
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N
K
N
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W
L
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D
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U
T
I

L
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Z
A
T
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JUN
1
5
Capture
Knowledge
Refine
Knowledge
Store
Knowledge
Manage
Knowledge
Disseminate
Knowledge
Create
Knowledge
1
2
3
4
5
6
Capture Knowledge
Refine Knowledge
Store Knowledge
Manage Knowledge

Disseminate Knowledge
Create Knowledge
1
2
3
4
5
6
9
9
#
#
*#
4
4

Ventana Portable setup
Chapter 11:
Automated Decision Systems and Expert Systems
Support
(10th Edition)
Support
(10th Edition)

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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 rule-based expert systems
(ES)
Understand the architecture of rule-based ES
Learn the knowledge engineering process used to build ES
(Continued…)
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Learning Objectives

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
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Opening Vignette…
InterContinental Hotel Group Uses
Decision Rules for Optimal Hotel Room Rates
Company background
Problem description
Proposed solution
Results
Answer & discuss the case questions...

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4
Questions for
the Opening Vignette
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?
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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
“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
Company background
Problem description
Proposed solution
Results

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Automated Decision-Making Framework
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Architecture of the Airline Revenue Management Systems

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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
Artificial Intelligence (AI)
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Make machines smarter (primary goal)
Understand what intelligence is
Make machines more intelligent & useful
Signs of intelligence…
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
AI Objectives
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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
Test for Intelligence
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The AI Field…
AI provides the scientific foundation for many commercial
technologies
The Disciplines and Applications of AI.

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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 …
AI Areas
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Anti-lock Braking Systems (ABS)
Automatic Transmissions
Video Camcorders
Appliances
Washers, Toasters, Stoves, …
Help Desk Software
Subway Control
…
AI is Often Transparent in Many Commercial Products
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Is a computer program that attempts to imitate expert’s
reasoning processes and knowledge in solving specific problems
Most Popular Applied AI Technology
Enhance Productivity
Augment Work Forces
Works best with narrow problem areas/tasks

Expert systems do not replace experts, but
Make their knowledge and experience more widely available,
and thus
Permit non-experts to work better
Expert Systems (ES)
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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
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

Important Concepts in ES
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Experts / Expertise
Degrees or levels of expertise
Ratio of non-experts to exper
Transferring Expertise
From expert to computer to nonexperts via acquisition,
representation, inferencing, transfer
Symbolic Reasoning / Inferencing
Deep Knowledge / Self Knowledge
Features and Concepts in ES

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Conventional vs. Expert Systems
Continued…
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Conventional vs. Expert Systems
…

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Expert System Helps in Identifying Sport Talents
Background
Problem description
Proposed solution
Results
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Applications of Expert Systems
Classical Applications
DENDRAL
Applied knowledge (i.e., rule-based reasoning)
Deduced likely molecular structure of compounds
MYCIN
A rule-based expert system
Used for diagnosing and treating bacterial infections
XCON
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, …
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Applications of Expert Systems

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Expert System Aids in Identification of Chemical, Biological,
and Radiological Agents
Questions for Discussion
How can CBR Advisor assist in making quick decisions?
What characteristics of CBR Advisor make it an expert system?
What could be other situations where such expert systems can
be employed?
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Structure of Expert Systems
Development Environment
Consultation Environment
Major Components
Knowledge acquisition subsystem
Knowledge Engineer
Knowledge Base
Inference Engine
User Interface
Blackboard (workplace)
Explanation subsystem (justifier)
Knowledge-refining system
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Structures of Expert
Systems

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26
Diagnosing Heart Diseases by Signal Processing
List the major components involved in building SIPMES and
briefly comment on them.
Do expert systems like SIPMES eliminate the need for human
decision making?
How often do you think that the existing expert systems, once
built, should be changed?
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Knowledge Engineering (KE)
A set of intensive activities encompassing the acquisition of
knowledge from human experts (and other information sources)
and converting this knowledge into a repository (commonly
called a knowledge base)
The primary goal of KE is to
help experts articulate how they do what they do, and
to document this knowledge in a reusable form
Narrow versus Broad definition of KE?
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28
The Knowledge Engineering Process

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Difficulties in KE
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Knowledge Engineering
Knowledge Validation/Verification

Evaluation is a broad concept - its objective is to assess an ES’s
overall value
Validation versus Verification
Validation is the part of evaluation that deals with the
performance of the system
Verification is building the system right or substantiating that
the system is correctly implemented to its specifications
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Knowledge Representation in ES
Expert knowledge must be represented in a computer-
understandable format and organized properly in the knowledge
base
The most common/popular way to represent human knowledge:
Production rules
Condition-Action pairs
IF … THEN … ELSE …

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IF premise, THEN conclusion
IF your income is high, THEN your chance of being audited by
the IRS is high
Conclusion, IF premise
Your chance of being audited is high, IF your income is high
Inclusion of ELSE
IF your income is high, OR your deductions are unusual, THEN
your chance of being audited by the IRS is high, ELSE your
chance of being audited is low
More complex rules…
Forms of Production Rules
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Knowledge and Inference Rules
Knowledge rules (declarative rules), state all the facts and
relationships about a problem
Knowledge rules are stored in the knowledge base
Inference rules (procedural rules), advise on how to solve a
problem, given that certain facts are known
Inference rules contain rules about rules (metarules)
Inference rules become part of the inference engine
Example:
IF needed data is not known THEN ask the user
IF more than one rule applies THEN fire the one with the
highest priority value first
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Inferencing in ES
Inference is the process of chaining multiple rules together
based on available data
Forward chaining
A data-driven search in a rule-based system.
If the premise clauses match the situation, then the process
attempts to assert the conclusion.
Backward chaining
A goal-driven search in a rule-based system.
It begins with the action clause of a rule and works backward
through a chain of rules in an attempt to find a verifiable set of
condition clauses.
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Inferencing with Rules:
Forward and Backward Chaining
Firing a rule
When all of the rule's hypotheses (the “if parts”) are satisfied, a
rule said to be FIRED

Inference engine checks every rule in the knowledge base in a
forward or backward direction to find rules that can be FIRED
Continues until no more rules can fire, or until a goal is
achieved
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Goal-driven: Start from a potential conclusion (hypothesis),
then seek evidence that supports (or contradicts with) it
Often involves formulating and testing intermediate hypotheses
(or sub-hypotheses)
Inferencing – Backward Chaining
Investment Decision: Variable Definitions
A = Have $10,000
B = Younger than 30
C = Education at college level
D = Annual income > $40,000
E = Invest in securities
F = Invest in growth stocks
G = Invest in IBM stock

Knowledge Base
Rule 1: A & C -> E
Rule 2: D & C -> F
Rule 3: B & E -> F (invest in growth stocks)
Rule 4: B -> C
Rule 5: F -> G (invest in IBM)
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Data-driven: Start from available information as it becomes
available, then try to draw conclusions
Which One to Use?
If all facts available up front - forward chaining
Diagnostic problems - backward chaining
Inferencing – Forward Chaining
FACTS:
A is TRUE
B is TRUE

Knowledge Base
Rule 1: A & C -> E
Rule 2: D & C -> F
Rule 3: B & E -> F (invest in growth stocks)
Rule 4: B -> C
Rule 5: F -> G (invest in IBM)
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Inferencing Issues
How do we choose between BC and FC
Follow how a domain expert solves the problem
If the expert first collect data then infer from it
=> Forward Chaining
If the expert starts with a hypothetical solution and then
attempts to find facts to prove it => Backward Chaining
How to handle conflicting rules
IF A & B THEN C
IF X THEN C
Establish a goal and stop firing rules when goal is achieved

Fire the rule with the highest priority
Fire the most specific rule
Fire the rule that uses the data most recently entered
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Inferencing with Uncertainty
- Theory of Certainty
Certainty Factors and Beliefs
Uncertainty is represented as a Degree of Belief
Express the Measure of Belief
Manipulate degrees of belief while using knowledge-based
systems
Certainty Factors (CF) express belief in an event based on
evidence (or the expert's assessment)
1.0 or 100 = absolute truth (complete confidence)
0 = certain falsehood
CFs are NOT probabilities
CFs need not sum to 100

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Combining Certainty Factors
Combining Several Certainty Factors in One Rule where parts
are combined using AND and OR logical operators
AND
IF inflation is high, CF = 50 percent, (A), AND
unemployment rate is above 7, CF = 70 percent, (B), AND
bond prices decline, CF = 100 percent, (C)
THEN stock prices decline
CF(A, B, and C) = Minimum[CF(A), CF(B), CF(C)]
=> The CF for “stock prices to decline” = 50 percent
The chain is as strong as its weakest link

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OR
IF inflation is low, CF = 70 percent, (A), OR
bond prices are high, CF = 85 percent, (B)
THEN stock prices will be high
CF(A, B) = Maximum[CF(A), CF(B)]
=> The CF for “stock prices to be high” = 85 percent
Notice that in OR only one IF premise need to be true
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Combining two or more rules
Example:
R1:IF the inflation rate is less than 5 percent,
THEN stock market prices go up (CF = 0.7)
R2:IF unemployment level is less than 7 percent,
THEN stock market prices go up (CF = 0.6)
Inflation rate = 4 percent and the unemployment level = 6.5
percent
Combined Effect
CF(R1,R2) = CF(R1) + CF(R2)[1 - CF(R1)]; or
CF(R1,R2) = CF(R1) + CF(R2) -
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Explanation
Human experts justify and explain their actions

… so should ES
Explanation: an attempt by an ES to clarify reasoning,
recommendations, other actions (asking a question)
Explanation facility = Justifier
Explanation Purposes…
Make the system more intelligible
Uncover shortcomings of the knowledge bases
Explain unanticipated situations
Satisfy users’ psychological and/or social needs, …
Explanation as a Metaknowledge
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Two Basic Explanations
Why Explanations - Why is a fact requested?
How Explanations - To determine how a certain conclusion or
recommendation was reached
Some simple systems - only at the final conclusion
Most complex systems provide the chain of rules used to reach
the conclusion

Explanation is essential in ES
Used for training and evaluation
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Problem Areas Suitable For Expert Systems
11-‹#›

Development of ES
Defining the nature and scope of the problem
Identifying proper experts
Acquiring knowledge
Knowledge engineer
Selecting the Building Tools
Shells versus Complete Development
Coding the system
Evaluating and Launching the System
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A Popular Expert System Shell

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Clinical Decision Support System for Tendon Injuries
Research other expert systems in other domains and list a few of
them.
Why is important to evaluate the expert systems before they are
put into use?
11-‹#›

Interpretation systems
Prediction systems
Diagnostic systems
Repair systems
Design systems
Planning systems
Monitoring systems
Debugging systems
Instruction systems
Control systems, …
Problem Areas Addressed by ES
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End of the Chapter

Questions, comments
11-‹#›
51
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.

11-‹#›
52
Questions / Answers
Psychology
Philosophy
Logic
Sociology
Human Cognition
Linguistics
Neurology
Mathematics
Management Science
Information Systems
Statistics
Engineering
Robotics
Biology
Human Behavior
Pattern Recognition
Voice Recognition
Intelligent tutoring
Expert Systems
Neural Networks
Natural Language Processing
Intelligent Agents
Fuzzy Logic
Game Playing
Computer Vision
Automatic Programming

Genetic Algorithms
Machine Learning
Autonomous Robots
Speech Understanding
The AI
Tree
Computer Science
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Inference Engine
Working
Memory
(Short Term)
Explanation
Facility

Knowledge
Refinement
Blackboard (Workspace)
External Data
Sources
(via WWW)
Knowledge
Engineer
Human
Expert(s)
Other Knowledge
Sources
Knowledge
Elicitation
Information
Gathering
Knowledge
Base(s)
(Long Term)
User
User
Interface
Facts
Questions
/ Answers
Rule
Firings
Knowledge
Rules
Inferencing
Rules
Facts
Data /
Information
Refined
Rules

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Knowledge
Acquisition
Knowledge
Representation
Knowledge
Validation
Inferencing
(Reasoning)
Explanation &
JustificationFeedback loop (corrections and refinements)
Raw
knowledge
Codified
knowledge
Validated
knowledge
Meta
knowledge
Problem or
Opportunity
Solution

B
D
C
and
or
C&D
FG
B&E
and
B
EA&C
and
C
A
B
R4
R2
R3
R5
R1
R47
65
4
21

3
1, 2, 3, 4: Sequence of rule firings
R1, R2, R3, R4, R5: Rules
A, B, C, D, E, F, G: Facts
Legend
B
D
C
and
or
C&D
FG
B&E
and
B
EA&C
and
C
A
B
R4
R2
R3
R5
R1

R4
2
4
1
1
3
1, 2, 3, 4: Sequence of rule firings
R1, R2, R3, R4, R5: Rules
A, B, C, D, E, F, G: Facts
Legend
Paper Section 1: Reflection and Literature Review
Using Microsoft Word and Professional APA format, prepare a
professional written paper supported with three sources of
research that details what you have learned from chapters 11
and 12. This section of the paper should be a minimum of two
pages.
Paper Section 2: Applied Learning Exercises
In this section of the professional paper, apply what you have
learned from chapters 11 and 12 to descriptively address and
answer the problems below. Important Note : Dot not type the
actual written problems within the paper itself.
1. Search to find and possibly test some applications of
artificial intelligence and ES. Consider a fictional or real
organizational work environment for which you are currently

part of or what to be part of and think about a decision-making
problem that requires some expertise (but is not too
complicated) for this type of work environment. Based on
research, experience and or understanding of this work
environment, identify the problems that are supported or can
potentially be supported by rule-based systems. Some possible
example areas could include, and is not limited to, selection of
suppliers, selection of a new employee, job assignment,
computer selection, market contact method selection,
determination of admission into graduate school and or specific
ones more suited to the established work environment based on
experience and or desire.
2. How does knowledge management support decision-making?
Identify products or systems on the Web that help organizations
accomplish knowledge management. Start with brint.com and
knowledgemanagement.com. Try one out and report your
findings and learning experience.
3. Important Note: With limited time for a college class,
perfection is not expected but effort to be exposed to various
tools with attempts to learn about them is critical when
considering a career in information technology associated
disciplines.
Important Note : There is no specific page requirement for this
section of the paper but make sure any content provided fully
addresses each problem.

Paper Section 3: Conclusions
After addressing the problems, conclude your paper with details
on how you will use this knowledge and skills to support your
professional and or academic goals. This section of the paper
should be around one page including a custom and original
process flow or flow diagram to visually represent how you will
apply this knowledge going forward. This customized and
original flow process flow or flow diagram can be created using
the “Smart Art” tools in Microsoft Word.
Paper Section 4: APA Reference Page
The three or more sources of research used to support this
overall paper should be included in proper APA format in the
final section of the paper.
Paper Review and Preparation to submit for Grading
Please make sure to proof read your post prior to submission.
This professional paper should be well written and free of
grammatical or typographical errors. Also remember not to
plagiarize!!!!!!!!!!!!

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