SEN-455Knowledge Based Management System

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Department of Computer Science | Bahria University

SEN-455

Knowledge Based

Management System

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Chapter Objectives

• The Concept of Learning

• Data Visualization

• Neural Networks

The Basics

Supervised and Unsupervised Learning Business Applications

• Association Rules

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The “Context” of Learning

• The “value added” collaborative intelligence

layer of KM architecture

• Relevant technologies are:

– Artificial Intelligence – Experts Systems – Case-Based Reasoning – Data Warehousing – Intelligent agents – Neural Networks

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The “Process” of Learning

• A process of filtering and

transforming data into valid and useful knowledge.

• Automate via technology tools:

– provide a collaborative

learning environment for participants

– enhance their ability to

understand the processes / tasks they are dealing with

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The “Goals” of Learning

• Final goal is to improve the

qualities of communication and decision making

• Ways to achieve these goals:

– Verify hypotheses (formed from

accumulated knowledge)

– Discover new patterns in data

– Predict future trends and

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Learning from Data

• Build learning models that automatically

improve with experience.

• Top-down approach

– Generate ideas

– Develop models

– Validate models

• Bottom-up approach

– Discover new (unknown) patterns

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Predicting health of a patient needs measurements.

• Height • Weight

• Systolic blood pressure • Diastolic blood pressure • Enzyme levels

• Blood sugar levels

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height weight BP enzyme Health?

70 64 3 1 1 23 86 5 0 1 56 49 5 1 0 50 88 3 0 0 12 50 1 0 1 56 66 2 1 0 … … … … … … … … … … … … … … … … … … … … 56 1 5 0 0 Class, or “label” “Features” “Examples”

Historical data in health records for example.

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Department of Computer Science | Bahria University Training data + labels Predicted Labels Model Learning algorithm Testing Data (no labels) New person!

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Department of Computer Science | Bahria University Training data + labels Predicted Labels Model Learning algorithm Testing Data (no labels) New person! TRAINING PHASE TESTING PHASE

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Predicting health.

Quite a hard problem even for trained professional!

Need to QUANTIFY performance of our algorithms.

Model Learning algorithm

Learning Algorithms make

Mistakes

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Top-down approach (Example)

• Start with a hypothesis derived

from observation or prior knowledge

• “Tourists visiting Egypt earn an

annual income of at least $50,000”

• Hypothesis tested by querying

database followed by analysis

• If tests not supportive, hypothesis

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Bottom-up approach (Example)

• No hypothesis to test

• “Find unknown buying patterns

by analyzing the shopping basket”

• “ … showed married males, age

21 to 27, shopped for diapers also brought action movies cds.

• “store decided to stack cds cases

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Data Visualization

• Explore visually for trends in

data useful for making decision

• Exploring data includes:

– Identify key attributes

and their distribution

– Extract interesting

grouping of data subsets

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Example of Data Visualization

(John Snow and the Cholera outbreak in London, 1845)

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Model

• Modeled after human brain’s

network

• Simulate biological

information processing via networks of interconnected neurons

• Neural networks are analog

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Neurons – The Basic Elements

• The neuron receives inputs,

determines their weights (strengths), sums the

combined inputs, and compares the total to a

threshold (transfer function)

• If total is greater than

threshold, the neuron fires (sends an output)

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A Neuron Model

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Learning in Neural Network

• Supervised

– The NN needs a teacher

with a training set of examples of input and output

• Unsupervised (or

Self-Supervised)

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Supervised Learning

• Each element in a training

set is paired with an acceptable response

• Network makes successive

passes through the examples

• The weights adjust toward

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A Supervised Neural Network (An

Example)

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Unsupervised Learning

• No external factors can

influence adjustment of input’s weights

• No advanced indication of

correct or incorrect answers

• Adjusts through direct

conflict with new

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Business Applications (1)

• Risk management

– _{Evaluate commercial loan applications}

– _{NN trained on thousands of applications, half of}

which were approved and the other half rejected by the bank’s loan officers

– Through supervised learning, NN learned to pick

risks that constitute a bad loan

– Identifies loan applicants who are likely to

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Business Applications (2)

• Predicting Foreign Exchange Fluctuations:

– A set of relevant indicators were

identified, used as inputs to NN

– NN was trained for exchange rates of US

dollar against Swiss franc and Japanese yen, using data from first 6 months of 1990. Then it was tested over an 8- to 11-week period

– Results revealed return on capital of about

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Business Applications (3)

• Advance Evaluations:

– Neural network uses the data in the

advance loan application

– It estimates value of the property based on

the immediate neighborhood, the city, and the country

– The system comes up with a valuation for

the property and a risk analysis for the loan.

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Association Rules

• A KB tool that generates a set of

rules to help understanding relationships that exist in data

• Types:

– Boolean rule

– Quantitative rule

– Multi-dimensional rule

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Boolean Rule (An Example)

• A rule that examines the

presence or absence of items

• For example, if a customer buys

a PC and a 17” monitor, then he will buy a printer. Presence of items (a PC and 17” monitor)

implies presence of the printer in the customer’s buying list

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Quantitative Rule (An Example)

• A rule that considers the

quantitative values of items

• For example, if a customer

earns between $30,000 and $50,000 and owns an

apartment worth between $250,000 and $500,000, he will buy a 4-door automobile

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Multi-dimensional Rule

• A rule that refers to a multitude of

dimensions

• If a customer lives in a big city and

earns more than $35,000, then he will buy a cellular phone

• This rule involves 3 attributes:

living, earning, and buying.

Therefore, it is a

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Inferences for Knowledge

Management

• Cost / benefit analysis

– Tangible costs - user training, hardware + software, backup, support, maintenance

– Intangible costs - user resistance and learning curve

• Quality Assurance

– Capability of initial design

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User Interface

(Web browser software installed on each user’s PC)

Authorized access control

(e.g., security, passwords, firewalls, authentication)

Collaborative intelligence and filtering

(intelligent agents, network mining, customization, personalization)

Knowledge-enabling applications

(customized applications, skills directories, videoconferencing, decision support systems,

group decision support systems tools)

Transport

(e-mail, Internet/Web site, TCP/IP protocol to manage traffic flow)

Middleware

(specialized software for network management, security, etc.)

The Physical Layer

(repositories, cables)

Databases Data warehousing

(data cleansing, data mining) Groupware (document exchange, collaboration) Legacy applications (e.g., payroll) 1 2 3 4 5 6 7