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Assignment5

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 7, 8, 9, and 10. 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 7, 8, 9, and 10 to descriptively address and answer the problems below. Important Note: Dot not type the actual written problems within the paper itself.

Survey and compare and possibly test some text mining tools and vendors. Start with clearforest.com and megaputer.com. Also consult with dmreview.com to further identify some text mining products to explore and even test?
Survey and compare and possibly test some Web mining tools and vendors. Identify some Web mining products and service providers that could potentially be useful in a work environment you may want to be part of.
Investigate via a Web search how models and their solutions are used by the U.S. Department of Homeland Security in the “war against terrorism.” Also investigate how other governments or government agencies are using models in their missions.
Search online to find vendors of genetic algorithms and investigate the business applications of their products and even possibly test them where applicable. What kinds of applications are most prevalent and why?
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.

Chapter 7:

Text Analytics, Text Mining, and Sentiment Analysis

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)

7-‹#›

Learning Objectives
Describe text mining and understand the need for text mining
Differentiate between text mining, Web mining, and data mining
Understand the different application areas for text mining
Know the process of carrying out a text mining project
Understand the different methods to introduce structure to text-based data
(Continued…)

7-‹#›

Learning Objectives
Describe sentiment analysis
Develop familiarity with popular applications of sentiment analysis
Learn the common methods for sentiment analysis
Become familiar with speech analytics as it relates to sentiment analysis

7-‹#›

Opening Vignette…
Machine Versus Men on Jeopardy!: The Story of Watson
Situation
Problem
Solution
Results
Answer & discuss the case questions…
Watch it on YouTube!
https://www.youtube.com/watch?v=YLR1byL0U8M

7-‹#›

Questions for
the Opening Vignette
What is Watson? What is special about it?
What technologies were used in building Watson (both hardware and software)?
What are the innovative characteristics of DeepQA architecture that made Watson superior?
Why did IBM spend all that time and money to build Watson? Where is the ROI?

7-‹#›

A High-Level Depiction of IBM Watson’s DeepQA Architecture

7-‹#›

Text Mining Concepts
85-90 percent of all corporate data is in some kind of unstructured form (e.g., text)
Unstructured corporate data is doubling in size every 18 months
Tapping into these information sources is not an option, but a need to stay competitive
Answer: text mining
A semi-automated process of extracting knowledge from unstructured data sources
a.k.a. text data mining or knowledge discovery in textual databases

7-‹#›

Text Analytics and Text Mining

7-‹#›

Data Mining versus Text Mining
Both seek for novel and useful patterns
Both are semi-automated processes
Difference is the nature of the data:
Structured versus unstructured data
Structured data: in databases
Unstructured data: Word documents, PDF files, text excerpts, XML files, and so on
Text mining – first, impose structure to the data, then mine the structured data.

7-‹#›

Text Mining Concepts
Benefits of text mining are obvious, especially in text-rich data environments
e.g., law (court orders), academic research (research articles), finance (quarterly reports), medicine (discharge summaries), biology (molecular interactions), technology (patent files), marketing (customer comments), etc.
Electronic communication records (e.g., Email)
Spam filtering
Email prioritization and categorization
Automatic response generation

7-‹#›

Text Mining Application Area
Information extraction
Topic tracking
Summarization
Categorization
Clustering
Concept linking
Question answering

7-‹#›

Text Mining Terminology
Unstructured or semi-structured data
Corpus (and corpora)
Terms
Concepts
Stemming
Stop words (and include words)
Synonyms (and polysemes)
Tokenizing

7-‹#›

Text Mining Terminology
Term dictionary
Word frequency
Part-of-speech tagging
Morphology
Term-by-document matrix
Occurrence matrix
Singular value decomposition
Latent semantic indexing

7-‹#›

Application Case 7.1
Text Mining for Patent Analysis
What is a patent?
“exclusive rights granted by a country to an inventor for a limited period of time in exchange for a disclosure of an invention”
How do we do patent analysis (PA)?
Why do we need to do PA?
What are the benefits?
What are the challenges?
How does text mining help in PA?

7-‹#›

Natural Language Processing (NLP)
Structuring a collection of text
Old approach: bag-of-words
New approach: natural language processing
NLP is …
a very important concept in text mining
a subfield of artificial intelligence and computational linguistics
the studies of “understanding” the natural human language
Syntax versus semantics-based text mining

7-‹#›

Natural Language Processing (NLP)
What is “Understanding” ?
Human understands, what about computers?
Natural language is vague, context driven
True understanding requires extensive knowledge of a topic
Can/will computers ever understand natural language the same/accurate way we do?

7-‹#›

Natural Language Processing (NLP)
Challenges in NLP
Part-of-speech tagging
Text segmentation
Word sense disambiguation
Syntax ambiguity
Imperfect or irregular input
Speech acts
Dream of AI community
to have algorithms that are capable of automatically reading and obtaining knowledge from text

7-‹#›

Natural Language Processing (NLP)
WordNet
A laboriously hand-coded database of English words, their definitions, sets of synonyms, and various semantic relations between synonym sets.
A major resource for NLP.
Need automation to be completed.
Sentiment Analysis
A technique used to detect favorable and unfavorable opinions toward specific products and services
SentiWordNet

7-‹#›

Application Case 7.2
Text Mining Improves Hong Kong Government’s Ability to Anticipate and Address Public Complaints
Questions for Discussion
How did the Hong Kong government use text mining to better serve its constituents?
What were the challenges, the proposed solution, and the obtained results?

7-‹#›

NLP Task Categories
Information retrieval, information extraction
Named-entity recognition
Question answering
Automatic summarization
Natural language generation & understanding
Machine translation
Foreign language reading & writing
Speech recognition
Text proofing, optical character recognition

7-‹#›

Text Mining Applications
Marketing applications
Enables better CRM
Security applications
ECHELON, OASIS
Deception detection (…)
Medicine and biology
Literature-based gene identification (…)
Academic applications
Research stream analysis

7-‹#›

Application Case 7.3
Mining for Lies!
Deception detection
A difficult problem
If detection is limited to only text, then the problem is even more difficult
The study
analyzed text-based testimonies of persons of interest at military bases
used only text-based features (cues)

7-‹#›

Application Case 7.3
Mining for Lies

7-‹#›

Application Case 7.3
Mining for Lies

7-‹#›

Application Case 7.3
Mining for Lies
371 usable statements are generated
31 features are used
Different feature selection methods used
10-fold cross validation is used
Results (overall % accuracy)
Logistic regression 67.28
Decision trees 71.60
Neural networks 73.46

7-‹#›

Text Mining Applications
(Gene/Protein Interaction Identification)

7-‹#›

Application Case 7.4
Text mining and Sentiment Analysis Help Improve Customer Service Performance
Questions for Discussion
How did the financial services firm use text mining and text analytics to improve its customer service performance?
What were the challenges, the proposed solution, and the obtained results?

7-‹#›

Text Mining Process
Context diagram for the text mining process

7-‹#›

Text Mining Process
The three-step text mining process

7-‹#›

Text Mining Process
Step 1: Establish the corpus
Collect all relevant unstructured data (e.g., textual documents, XML files, emails, Web pages, short notes, voice recordings…)
Digitize, standardize the collection (e.g., all in ASCII text files)
Place the collection in a common place (e.g., in a flat file, or in a directory as separate files)

7-‹#›

Text Mining Process
Step 2: Create the Term-by-Document Matrix (TDM)

7-‹#›

Text Mining Process
Step 2: Create the Term-by-Document Matrix (TDM)
Should all terms be included?
Stop words, include words
Synonyms, homonyms
Stemming
What is the best representation of the indices (values in cells)?
Row counts; binary frequencies; log frequencies;
Inverse document frequency

7-‹#›

Text Mining Process
Step 2: Create the Term–by–Document Matrix (TDM)
TDM is a sparse matrix. How can we reduce the dimensionality of the TDM?
Manual – a domain expert goes through it
Eliminate terms with very few occurrences in very few documents (?)
Transform the matrix using singular value decomposition (SVD)
SVD is similar to principle component analysis

7-‹#›

Text Mining Process
Step 3: Extract patterns/knowledge
Classification (text categorization)
Clustering (natural groupings of text)
Improve search recall
Improve search precision
Scatter/gather
Query-specific clustering
Association
Trend Analysis (…)

7-‹#›

Application Case 7.5
(Research Literature Survey with Text Mining)
Mining the published IS literature
MIS Quarterly (MISQ)
Journal of MIS (JMIS)
Information Systems Research (ISR)
Covers 12-year period (1994-2005)
901 papers are included in the study
Only the paper abstracts are used
9 clusters are generated for further analysis

7-‹#›

Application Case 7.5
(Research Literature Survey with Text Mining)

7-‹#›

Application Case 7.5
(Research Literature Survey with Text Mining)

7-‹#›

Application Case 7.5
(Research Literature Survey with Text Mining)

7-‹#›

Text Mining Tools
Commercial Software Tools
IBM SPSS Modler – Text Miner
SAS Enterprise Miner – Text Miner
Statistical Data Miner – Text Miner
ClearForest, …
Free Software Tools
RapidMiner
GATE
Spy-EM, …

7-‹#›

Application Case 7.6
A Potpourri of Text Mining Case Synopses
Alberta’s Parks Division gains insight from unstructured data
American Honda Saves Millions by Using Text and Data Mining
MaspexWadowice Group Analyzes Online Brand Image with Text Mining
Viseca Card Services Reduces Fraud Loss with Text Analytics
Improving Quality with Text Mining and Advanced Analytics

7-‹#›

Sentiment Analysis Overview
Sentiment  belief, view, opinion, conviction
Sentiment analysis  opinion mining, subjectivity analysis, and appraisal extraction
The goal is to answer the question:
“What do people feel about a certain topic?”
Explicit versus Implicit sentiment
Sentiment polarity
Positive versus Negative
… versus Neutral?

7-‹#›

Example –
Real-Time Social Signal (by Attensity)

7-‹#›

Application Case 7.7
Whirlpool Achieves Customer Loyalty and Product Success with Text Analytics
Questions for Discussion
How did Whirlpool use capabilities of text analytics to better understand their customers and improve product offerings?
What were the challenges, the proposed solution, and the obtained results?

7-‹#›

Sentiment Analysis Applications
Voice of the customer (VOC)
Voice of the Market (VOM)
Voice of the Employee (VOE)
Brand Management
Financial Markets
Politics
Government Intelligence
… others

7-‹#›

Sentiment
Analysis
Process

7-‹#›

Sentiment Analysis Process
Step 1 – Sentiment Detection
Comes right after the retrieval and preparation of the text documents
It is also called detection of objectivity
Fact [= objectivity] versus Opinion [= subjectivity]
Step 2 – N-P Polarity Classification
Given an opinionated piece of text, the goal is to classify the opinion as falling under one of two opposing sentiment polarities
N [= negative] versus P [= positive]

7-‹#›

Sentiment Analysis Process
Step 3 – Target Identification
The goal of this step is to accurately identify the target of the expressed sentiment (e.g., a person, a product, an event, etc.)
Level of difficulty  the application domain
Step 4 – Collection and Aggregation
Once the sentiments of all text data points in the document are identified and calculated, they are to be aggregated
Word  Statement  Paragraph  Document

7-‹#›

Sentiment Analysis
Methods for Polarity Identification
Polarity Identification – P vs. N
Can be made at the level of word, term, sentence, paragraph, document
Two competing methods
Using a lexicon
WordNet [wordnet.princeton.edu]
SentiWordNet [sentiwordnet.isti.cnr.it]
Using pre-classified training documents
Data mining / machine learning

7-‹#›

P-N Polarity and S-O Polarity

7-‹#›

Sentiment Analysis and
Speech Analytics
Speech analytics – analysis of voice
Content versus other Voice Features
Two Approaches
The Acoustic Approach
Intensity, Pitch, Jitter, Shimmer, etc.
The Linguistic Approach
Lexical: words, phrases, etc.
Disfluencies: filled pauses, hesitation, restarts, etc.
Higher semantics: taxonomy/ontology, pragmatics
Many uses and use cases exist

7-‹#›

Application Case 7.8
Cutting Through the Confusion: Blue Cross Blue Shield of North Carolina Uses Nexidia’s Speech Analytics to Ease Member Experience in Healthcare
Questions for Discussion
For a large company like BCBSNC with a lot of customers, what does “listening to customer” mean?
What were the challenges, the proposed solution, and the obtained results for BCBSNC?

7-‹#›

End of the Chapter

Questions, comments

7-‹#›

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.

7-‹#›

Trained
models
Question
analysis
Hypothesis
generation
Query
decomposition
Soft
filtering
Hypothesis and
evidence scoring
Synthesis
Final merging
and ranking
Answer and
confidence
………
Hypothesis
generation
Soft
filtering
Hypothesis and
evidence scoring
Answer
sources
Evidence
sources
Primary
search
Candidate
answer
generation
Support
evidence
retrieval
Deep
evidence
scoring
Question
1
2
3
4
5
Information
Retrieval
Information
Extraction
Web Mining
Data Mining
StatisticsComputer Science
Natural Language Processing
TEXT ANALYTICS
Text Mining
Artificial Intelligence
Machine Learning
Management Science
Linguistic

Statements
Transcribed for
Processing
Text Processing
Software Identified
Cues in Statements
Statements Labeled as
Truthful or Deceptive
By Law Enforcement
Text Processing
Software Generated
Quantified Cues
Classification Models
Trained and Tested on
Quantified Cues
Cues Extracted &
Selected
Category Example Cues
Quantity Verb count, noun-phrase count, …
Complexity Avg. no of clauses, sentence length, …
Uncertainty Modifiers, modal verbs, …
Nonimmediacy Passive voice, objectification, …
Expressivity Emotiveness
Diversity Lexical diversity, redundan cy, …
Informality Typographical error ratio
Specificity Spatiotemporal , perceptual information …
Affect Positive affect, negative affect, etc.

G
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596 12043 24224 28102042722 397276
D007962
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185851112923017275874279189521623563217825282523
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NPPPNPNPPPNPNPPPNP
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S
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a
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P
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s
e
…expression of Bcl-2 is correlated with insufficient white blood cell death and activation of p53.
Extract
knowledge
from available
data sources
A0
Unstructured data (text)
Structured data (databases)
Context-specific knowledge
Software/hardware limitations
Privacy issues
Tools and techniques
Domain expertise
Linguistic limitations

Establish the Corpus:
Collect & Organize the
Domain Specific
Unstructured Data
Create the Term-
Document Matrix:
Introduce Structure
to the Corpus
Extract Knowledge:
Discover Novel
Patterns from the
T-D Matrix
The inputs to the process
includes a variety of relevant
unstructured (and semi-
structured) data sources such
as text, XML, HTML, etc.
The output of the Task 1 is a
collection of documents in
some digitized format for
computer processing
The output of the Task 2 is a
flat file called term-document
matrix where the cells are
populated with the term
frequencies
The output of Task 3 is a
number of problem specific
classification, association,
clustering models and
visualizations
Task 1Task 2Task 3
FeedbackFeedback
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Document 1
Document 2
Document 3
Document 4
Document 5
Document 6
…
Documents
Terms
1
1
1
2
1
1
1
3
1

Journal
Year
Author(s)
Title
Vol/No
Pages
Keywords
Abstract
MISQ
2005
A. Malhotra,
S. Gosain and
O. A. El Sawy
Absorptive capacity
configurations in
supply chains:
Gearing for partner-
enabled market
knowledge creation
29/1
145-187
knowledge management
supply chain
absorptive capacity
interorganizational
information systems
configuration approaches
The need for continual value
innovation is driving supply
chains to evolve from a pure
transactional focus to
leveraging interorganizational
partner ships for sharing
ISR
1999
D. Robey and
M. C. Boudreau
Accounting for the
contradictory
organizational
consequences of
information
technology:
Theoretical directions
and methodological
implications
2-Oct
167-185
organizational
transformation
impacts of technology
organization theory
research methodology
intraorganizational power
electronic communication
mis implementation
culture
systems
Although much contemporary
thought considers advanced
information technologies as
either determinants or enablers
of radical organizational
change, empirical studies have
revealed inconsistent findings to
support the deterministic logic
implicit in such arguments. This
paper reviews the contradictory
JMIS
2001
R. Aron and
E. K. Clemons
Achieving the optimal
balance between
investment in quality
and investment in self-
promotion for
information products
18/2
65-88
information products
internet advertising
product positioning
signaling
signaling games
When producers of goods (or
services) are confronted by a
situation in which their offerings
no longer perfectly match
consumer preferences, they
must determine the extent to
which the advertised features of
…
…
…
…
…
…
…
…
YEAR
No of Articles
CLUSTER: 1
199419951996199719981999200020012002200320042005
0
5
10
15
20
25
30
35
CLUSTER: 2
199419951996199719981999200020012002200320042005
CLUSTER: 3
199419951996199719981999200020012002200320042005
CLUSTER: 4
199419951996199719981999200020012002200320042005
0
5
10
15
20
25
30
35
CLUSTER: 5
199419951996199719981999200020012002200320042005
CLUSTER: 6
199419951996199719981999200020012002200320042005
CLUSTER: 7
199419951996199719981999200020012002200320042005
0
5
10
15
20
25
30
35
CLUSTER: 8
199419951996199719981999200020012002200320042005
CLUSTER: 9
199419951996199719981999200020012002200320042005
JOURNAL
No of Articles
CLUSTER: 1
ISRJMISMISQ
0
10
20
30
40
50
60
70
80
90
100
CLUSTER: 2
ISRJMISMISQ
CLUSTER: 3
ISRJMISMISQ
CLUSTER: 4
ISRJMISMISQ
0
10
20
30
40
50
60
70
80
90
100
CLUSTER: 5
ISRJMISMISQ
CLUSTER: 6
ISRJMISMISQ
CLUSTER: 7
ISRJMISMISQ
0
10
20
30
40
50
60
70
80
90
100
CLUSTER: 8
ISRJMISMISQ
CLUSTER: 9
ISRJMISMISQ
Identify the target
for the sentiment
Calculate the NP
polarity of the
sentiment
Is there a
sentiment?
Record the Polarity,
Strength, and the
Target of the
sentiment.
Tabulate & aggregate
the sentiment
analysis results
Textual Data
Calculate the
O-S Polarity
YesNo
A statement
Yes
Lexicon
Lexicon
O-S
polarity
measure
N-P Polarity
Target
Step 1
Step 2
Step 3
Step 4
S
–
O

P
o
l
a
r
i
t
y

P-N Polarity
Positive (P)
(+)
Negative (N)
(-)
Objective (O)
Subjective (S)

Chapter 8:

Web Analytics, Web Mining, and Social Analytics

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)

8-‹#›

Learning Objectives
Define Web mining and understand its taxonomy and its application areas
Differentiate between Web content mining and Web structure mining
Understand the internals of Web search engines
Learn the details about search engine optimization
Define Web usage mining and learn its business application
(Continued…)

8-‹#›

Learning Objectives
Describe the Web analytics maturity model and its use cases
Understand social networks and social analytics and their practical applications
Define social network analysis and become familiar with its application areas
Understand social media analytics and its use for better customer engagement

8-‹#›

Opening Vignette…
Security First Insurance Deepens Connection with Policyholders
Situation
Problem
Solution
Results
Answer & discuss the case questions.

8-‹#›

Questions for
the Opening Vignette
What does Security First do?
What were the main challenges Security First was facing?
What was the proposed solution approach? What types of analytics were integrated in the solution?
Based on what you learn from the vignette, what do you think are the relationships between Web analytics, text mining, and sentiment analysis?
What were the results Security First obtained? Were any surprising benefits realized?

8-‹#›

Web Mining Overview
Web is the largest repository of data
Data is in HTML, XML, text format
Challenges (of processing Web data)
The Web is too big for effective data mining
The Web is too complex
The Web is too dynamic
The Web is not specific to a domain
The Web has everything
Opportunities and challenges are great!

8-‹#›

Web Mining
Web mining (or Web data mining) is the process of discovering intrinsic relationships from Web data (textual, linkage, or usage)
Is it the same as data mining on data generated on the Internet?
Web data?
Content, Link, Log, …
Web Mining versus Web Analytics
Look at the simple taxonomy on the next slide

8-‹#›

Web Mining

8-‹#›

Web Content/Structure Mining
Mining the textual content on the Web
Data collection via Web Crawlers/Spiders
Web pages include hyperlinks
Authoritative pages
Hubs
hyperlink-induced topic search (HITS) alg.

8-‹#›

Application Case 8.1
Identifying Extremist Groups with Web Link and Content Analysis
Questions for Discussion
How can Web link/content analysis be used to identify extremist groups?
What do you think are the challenges and the potential solution to such intelligence gathering activities?

8-‹#›

Search Engines
Google, Bing, Yahoo, …
For what reason do you use search engines?
Search engine is a software program that searches for documents (Internet sites or files) based on the keywords (individual words, multi-word terms, or a complete sentence) that users have provided that have to do with the subject of their inquiry
They are the workhorses of the Internet

8-‹#›

Structure of a
Typical Internet Search Engine

8-‹#›

Anatomy of a Search Engine
Development Cycle
Web Crawler
Document Indexer
Steps
Step 1 – Pre-Processing the Documents
Collecting, organizing, and storing
Step 2 – Parsing the Documents
Step 3 – Creating the Term-by-Document Matrix
How to represent the values (numeric, binary, …)
Term Frequency / Inverse Document Frequency

8-‹#›

Anatomy of a Search Engine
Response Cycle
Query Analyzer
Document Matcher/Ranker
How does Google do it?
Googlebot
Google indexer
Google Query Processor

8-‹#›

Technology Insights 8.1
PageRank Algorithm
PageRank is a link analysis algorithm
 Larry Page
Outcome of a research project at Stanford University in 1996
The “secret sauce” in Google

8-‹#›

Application Case 8.2
IGN Increases Search Traffic by 1500 Percent with SEO
Questions for Discussion
How did IGN dramatically increase search traffic to its Web portals?
What were the challenges, the proposed solution, and the obtained results?

8-‹#›

Search Engine Optimization (SEO)
It is the intentional activity of affecting the visibility of an e-commerce site or a Web site in a search engine’s natural (unpaid or organic) search results
Part of an Internet marketing strategy
Based on knowing how a search engine works
Content, HTML, keywords, external links, …
Indexing based on …
Webmaster submission of URL
Proactively and continuously crawling the Web

8-‹#›

Top 15 Most Popular Search Engines (by eBizMBA, March 2013)

8-‹#›

Methods for
Search Engine Optimization
Search engine recommended techniques (White-Hat SEO)
Producing results based on good site design, accurate content (for users, not engines)
Search engine disapproved techniques (Black-Hat SEO)
Spamdexing? (search spam, search engine spam, or search engine poisoning)
Deception (what is shown is different to human and machine/spider)

8-‹#›

Application Case 8.3
Understanding Why Customers Abandon Shopping Carts Results in $10 Million Sales Increase
Situation
Problem
Solution
Results

8-‹#›

Web Usage Mining
 Web Analytics!
Extraction of information from data generated through Web page visits and transactions…
data stored in server access logs, referrer logs, agent logs, and client-side cookies
user characteristics and usage profiles
metadata, such as page attributes, content attributes, and usage data
Clickstream data, clickstream analysis

8-‹#›

Web Usage Mining
Web usage mining applications
Determine the lifetime value of clients
Design cross-marketing strategies across products
Evaluate promotional campaigns
Target electronic ads and coupons at user groups based on user access patterns
Predict user behavior based on previously learned rules and users’ profiles
Present dynamic information to users based on their interests and profiles
…

8-‹#›

Web Usage Mining
(Clickstream Analysis)

8-‹#›

Application Case 8.4
Allegro Boosts Online Click-Thru Rates by 500 Percent with Web Analysis
Questions for Discussion
How did Allegro significantly improve clickthrough rates with Web analytics?
What were the challenges, the proposed solution, and the obtained results?

8-‹#›

Web Analytics Metrics
Provides near-real-time data to deliver invaluable information to …
Improve site usability
Manage marketing efforts
Better document ROI, …
Web analytics metric categories:
Web site usability: How were they using my Web site?
Traffic sources: Where did they come from?
Visitor profiles: What do my visitors look like?
Conversion statistics: What does all this mean for the business?

8-‹#›

Web Analytics Metrics
– Web Site Usability
Web Site Usability
Page views
Time on site
Downloads
Click map
Click paths
Traffic Source
Referral Web sites
Search engines
Direct
Offline campaigns
Online campaigns

8-‹#›

Web Analytics Metrics
– Web Site Usability
Visitor Profiles
Keywords
Content groupings
Geography
Time of day
Landing page
Conversion Statistics
New visitors
Returning visitors
Leads
Sales/conversions
Abandonment rates

8-‹#›

A Web Analytics Dashboard

8-‹#›

Web Analytics Maturity Model
Maturity  degree of proficiency, formality, and optimization of business models
Business Intelligence Maturity Model (TDWI)
Management Reporting ➔ Spreadmarts ➔ Data Marts ➔ Data Warehouse ➔ Enterprise Data Warehouse ➔ BI Services
Business Analytics Maturity Model (INFORMS)
Descriptive Analytics ➔ Predictive Analytics ➔ Prescriptive Analytics
Web analytics maturity model  next slide…

8-‹#›

Web Analytics Maturity Model

8-‹#›

Web Analytics Tools
Plenty of them exist, and numbers are increasing (Web-based versus downloadable)
Google Web Analytics (google.com/analytics)
Yahoo! Web Analytics (web.analytics.yahoo.com)
Open Web Analytics (openwebanalytics.com)
Piwik (PIWIK.ORG)
FireStats (firestats.cc)
Site Meter (sitemeter.com)
Woopra (woopra.com)
AWStats (awstats.org)
Snoop (reinvigorate.net) …

8-‹#›

Putting It All Together—A Web Site Optimization Ecosystem
Two-Dimensional View of the Inputs for Web Site Optimization
Goal:
Customer Experience Management (CEM)
Voice of Customer (VOC)

8-‹#›

Web Mining Success Stories
Amazon.com, Ask.com, Scholastic.com, …
A Process View of the Web Site Optimization Ecosystem

8-‹#›

Voice of the Customer Strategy Framework (Attensity.com)

8-‹#›

Social Analytics
Social Network Analysis
Social Network – social structure composed of individuals linked to each other
Analysis of social dynamics
Interdisciplinary field
Social psychology
Sociology
Statistics
Graph theory

8-‹#›

Social Analytics
Social Network Analysis
Social Networks help study relationships between individuals, groups, organizations, societies
Self organizing
Emergent
Complex
Typical social network types
Communication networks, community networks, criminal networks, innovation networks, …

8-‹#›

Application Case 8.5
Social Network Analysis Helps Telecommunication Firms (TELCOs)
Questions for Discussion
How can social network analysis be used in the telecommunications industry?
What do you think are the key challenges, potential solution, and probable results in applying SNA in telecommunications firms?

8-‹#›

Social Analytics
Social Network Analysis Metrics
Connections
Homophily
Multiplexity
Network closure
Propinquity
Segmentation
Cliques and social circles
Clustering coefficient
Cohesion
Distribution
Bridge
Centrality
Density
Structural holes
Tie strength

8-‹#›

Social Media
Definitions and Concepts
Enabling technologies of social interactions among people
Relies on enabling technologies of Web 2.0
Takes on many different forms
Internet forums, Web logs, social blogs, microblogging, wikis, social networks, podcasts, pictures, video, and product reviews
Different types of social media
Based on media research and social process

8-‹#›

Different Types of Social Media
Collaborative projects (e.g., Wikipedia)
Blogs and microblogs (e.g., Twitter)
Content communities (e.g., YouTube)
Social networking sites (e.g., Facebook)
Virtual game worlds (e.g., World of Warcraft), and
Virtual social worlds (e.g., Second Life)
–Kaplan and Haenlein (2010)

8-‹#›

Social versus Industrial Media
Web-based social media are different from traditional/industrial media, such as newspapers, television, and film
Differentiating characteristics
Quality
Reach
Frequency
Accessibility
Usability
Immediacy
Updatability

8-‹#›

How Do People Use Social Media?
Different engagement levels

8-‹#›

Application Case 8.6
Measuring the Impact of Social Media at Lollapalooza

Questions for Discussion
How did C3 Presents use social media analytics to improve its business?
What were the challenges, the proposed solution, and the obtained results?

8-‹#›

Social Media Analytics
It is the systematic and scientific ways to consume the vast amount of content created by Web-based social media outlets, tools, and techniques for the betterment of an organization’s competitiveness
Fastest growing movement in analytics
Social Media
Tweeter
Facebook
LinlkedIn
…
Insights
Solutions
Course of Actions
…

8-‹#›

Social Media Analytics
HBR Analytic Services survey (HBR, 2010)
75% of the companies did not know where their customers are talking about them
31% do not measure effectiveness of social media
only 23% are using social media analytics tools
7% are able to integrate social media into marketing
Measuring the Social Media Impact
Descriptive analytics – simple counts/statistics
Social network analysis
Advanced analytics – predictive analytics, text mining

8-‹#›

Best Practices in
Social Media Analytics
Think of measurement as a guidance system, not a rating system
Track the elusive sentiment
Continuously improve the accuracy of text analysis
Look at the ripple effect
Look beyond the brand
Identify your most powerful influencers
Look closely at the accuracy of your analytic tool
Incorporate social media intelligence into planning

8-‹#›

Application Case 8.7
eHarmony Uses Social Media to Help Take the Mystery Out of Online Dating

Questions for Discussion
How did eHarmony use social media to enhance online dating?
What were the challenges, the proposed solution, and the obtained results?

8-‹#›

Social Media Analytics
Tools and Vendors
Attensity360
Radian6/Salesforce Cloud
Sysomos
Collective Intellect
Webtrends
Crimson Hexagon
Converseon
SproutSocial …
Twitter
Facebook
YouTube
LinkedIn
Flickr
…

8-‹#›

Social Media Analytics

8-‹#›

End of the Chapter

Questions, comments

8-‹#›

360 Customer View
Log Analysis
Marketing AttributionCustomer Analytics
Social Media Analytics
Search Engines Optimization
Page RankInformation Retrieval
Search Engines
Social Network Analysis
Clickstream Analysis
Social Analytics
Semantic WebsWeb Analytics
Graph Mining
Sentiment Analysis
Web Structure Mining
Source:the unified
resource locator (URL)
links contained in the
Web pages
Web Content Mining
Source:unstructured
textual content of the
Web pages (usually in
HTML format)
Web Usage Mining
Source:the detailed
description of a Web
site’s visits (sequence
of clicks by sessions)
Data
Mining
Text
Mining
WEB MINING
Query Analyzer
Document
Matcher/Ranker
Web Crawler
Document
Indexer
Scheduler
Cashed / Indexed
Documents DB
User
World Wide Web
S
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Q
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Responding CycleDevelopment Cycle
M
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I
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Weblogs
Website
Pre-Process Data
Collecting
Merging
Cleaning
Structuring
-Identify users
-Identify sessions
-Identify page views
-Identify visits
Extract Knowledge
Usage patterns
User profiles
Page profiles
Visit profiles
Customer value
How to better the data
How to improve the Web site
How to increase the customer value
User /
Customer
Web
Analytics
Voice of
Customer
Customer Experience
Management
Customer Interaction
on the Web
Analysis of Interactions
Knowledge about the Holistic
View of the Customer
Creators
Critics
Joiners
Collectors
Spectators
Inactives
Time
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Chapter 9:

Model-Based Decision Making: Optimization and Multi-Criteria Systems

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)

9-‹#›

Learning Objectives
Understand the basic concepts of analytical decision modeling
Describe how prescriptive models interact with data and the user
Understand some different, well-known model classes
Understand how to structure decision making with a few alternatives
Describe how spreadsheets can be used for analytical modeling and solution
(Continued…)

9-‹#›

Learning Objectives
Explain the basic concepts of optimization and when to use them
Describe how to structure a linear programming model
Describe how to handle multiple goals
Explain what is meant by sensitivity analysis, what-if analysis, and goal seeking
Describe the key issues of multi-criteria decision making

9-‹#›

Opening Vignette…
Midwest ISO Saves Billions by Better Planning of Power Plant Operations and Capacity Planning
Company background
Problem description
Proposed solution
Results
Answer & discuss the case questions…

9-‹#›

Questions for
the Opening Vignette
In what ways were the individual companies in Midwest ISO better off being part of MISO as opposed to operating independently?
The dispatch problem was solved with a linear programming method. Explain the need of such method in light of the problem discussed in the case.
What were the two main optimization algorithms used? Briefly explain the use of each algorithm.

9-‹#›

Decision Support Systems Modeling
DSS modeling (optimization & simulation) contribute to organizational success. Examples include:
Pillowtex (see ProModel, 2013),
Fiat (see ProModel, 2006),
Procter & Gamble (see Camm et al., 1997),
and others.
INFORMS publications such as Interfaces, ORMS Today, and Analytics magazine have plenty of such example cases

9-‹#›

Application Case 9.1
Optimal Transport for ExxonMobil Downstream Through a DSS
Questions for Discussion
List three ways in which manual scheduling of ships could result in more operational cost as compared to the tool developed.
In what other ways can ExxonMobil leverage the decision support tool developed to expand and optimize their other business operations?
What are some strategic decisions that could be made by decision makers using the tool developed?

9-‹#›

Major Modeling Issues
Problem identification and environmental analysis (information collection)
Variable identification
Influence diagrams, cognitive maps
Forecasting/predicting
More information leads to better prediction
Multiple models: An MSS can include several models, each of which represents a different part of the decision-making problem
Categories of models >>>
Model management – DBMS vs. MBDM

9-‹#›

Categories of Models

9-‹#›

Model Categories
Static and Dynamic Models
Static Analysis
Single snapshot of the situation
Single interval
Steady state
Dynamic Analysis
Dynamic models
Evaluate scenarios that change over time
Time dependent
Represents trends and patterns over time
More realistic: Extends static models

9-‹#›

Application Case 9.2
Optimal Transport for ExxonMobil Downstream Through a DSS
Company
Problem description
Proposed solution
Results

9-‹#›

Model Categories
Current Trends in Modeling
Development of Model/Solution Libraries
NEOS Server for Optimization
neos.mcs.anl.gov/neos/index.html
Resources link at informs.org
lionhrtpub.com/ORMS.shtml
Web-based modeling (optimization/simulation/…)
Multidimensional analysis (modeling)
Influence Diagrams

9-‹#›

Structure of Mathematical Models
for Decision Support
Decision
Variables
Mathematical
Relationships
Uncontrollable
Variables
Result
Variables

Non-Quantitative Models (Qualitative)
Quantitative Models: Mathematically links decision variables, uncontrollable variables, and result variables
Independent Variables
Dependent Variable
Intermediate
Variables

9-‹#›

Examples – Components of Models

9-‹#›

The Structure of a
Mathematical Model
The components of a quantitative model are linked together by mathematical (algebraic) expressions—equations or inequalities.
Example – Profit –
whereP= profit, R= revenue, and C= cost
Example – Simple Present-Value –
whereP= present value, F= future cash-flow, i= interest-rate, and n = number of period (years)

9-‹#›

Modeling and Decision Making –
Under Certainty, Uncertainty, and Risk
Certainty
Assume complete knowledge
All potential outcomes are known
May yield optimal solution
Uncertainty
Several outcomes for each decision
Probability of each outcome is unknown
Knowledge would lead to less uncertainty
Risk analysis (probabilistic decision making)
Probability of each of several outcomes occurring
Level of uncertainty => Risk (expected value)

9-‹#›

Modeling and Decision Making –
Under Certainty, Uncertainty, and Risk
The Zones of Decision Making

9-‹#›

Application Case 9.3
American Airlines Uses Should-Cost Modeling to Assess the Uncertainty of Bids for Shipment Routes
Questions for Discussion
Besides reducing the risk of overpaying or underpaying suppliers, what are some other benefits AA would derive from its “should be” model?
Can you think of other domains besides air transportation where such a model could be used?
Discuss other possible methods with which AA could have solved its bid overpayment and underpayment problem.

9-‹#›

Decision Modeling with Spreadsheets
Spreadsheet
Most popular end-user modeling tool
Flexible and easy to use
Powerful functions (add-in functions)
Programmability (via macros)
What-if analysis and goal seeking
Simple database management
Seamless integration of model and data
Incorporates both static and dynamic models
Examples: Microsoft Excel, Lotus 1-2-3

9-‹#›

Application Case 9.4
Showcase Scheduling at Fred Astaire East Side Dance Studio
Company
Problem description
Proposed solution
Results

9-‹#›

Excel spreadsheet – static model example: (Simple loan calculation of monthly payments)

Static model example:

9-‹#›

Excel spreadsheet – Dynamic model example:
Simple loan calculation of monthly payments and effects of prepayment

9-‹#›

Optimization
via Mathematical Programming
Mathematical Programming
A family of tools designed to help solve managerial problems in which the decision maker must allocate scarce resources among competing activities to optimize a measurable goal
Optimal solution: The best possible solution to a modeled problem
Linear programming (LP): A mathematical model for the optimal solution of resource allocation problems. All the relationships are linear.

9-‹#›

Application Case 9.5
Spreadsheet Model Helps Assign Medical Residents
Company
Problem description
Proposed solution
Results

9-‹#›

LP Problem Characteristics
Limited quantity of economic resources
Resources are used in the production of products or services
Two or more ways (solutions, programs) to use the resources
Each activity (product or service) yields a return in terms of the goal
Allocation is usually restricted by constraints

9-‹#›

Linear Programming Steps
Identify the …
Decision variables
Objective function
Objective function coefficients
Constraints
Capacities / Demands / …
Represent the model
LINDO: Write mathematical formulation
EXCEL: Input data into specific cells in Excel
Run the model and observe the results

9-‹#›

Modeling in LP – An Example
The Product-Mix Linear Programming Model
MBI Corporation
Decision variable: How many computers to build next month?
Two types of mainframe computers: CC-7 and CC-8
Constraints: Labor limits, Materials limit, Marketing lower limits
CC-7 CC-8 Rel Limit
Labor (days) 300 500 <= 200,000 /mo Materials ($) 10,000 15,000 <= 8,000,000 /mo Units 1 >= 100
Units 1 >= 200
Profit ($) 8,000 12,000 Max
Objective: Maximize Total Profit / Month

9-‹#›

LP Solution –
Algebraic Formulations

9-‹#›

LP Solution with Excel
Decision Variables:
X1: unit of CC-7
X2: unit of CC-8
Objective Function:
Maximize Z (profit)
Z=8000X1+12000X2
Subject To
300X1 + 500X2  200K
10000X1 + 15000X2  8000K
X1  100
X2  200

9-‹#›

Illustrating the Power of Spreadsheet Modeling
Election Resource Allocation Problem

Analysis of “swing states” for the 2012 election…

9-‹#›

Common Optimization Models
Product-mix problems (how many of each product to produce for max profit)
Transportation (minimize cost of shipments)
Assignment (best matching of objects)
Investment (maximizing rate of return)
Network optimization models for planning and scheduling
Replacement (capital budgeting), …

9-‹#›

Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
Multiple Goals
Simple-goal vs. multiple goals
Vast majority of managerial problems has multiple goals (objectives) to achieve
Attaining simultaneous goals
Methods of handling multiple goals
Utility theory
Goal programming
Expression of goals as constraints, using LP
A points system

9-‹#›

Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
Certain difficulties may arise when analyzing multiple goals
Difficult to obtain a single organizational goal
The importance of goals change over time
Goals and sub-goals are viewed differently
Goals change in response to other changes
Dynamics of groups of decision makers
Assessing the importance (priorities)
…

9-‹#›

Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
Sensitivity analysis
It is the process of assessing the impact of change in inputs on outputs
Helps to …
eliminate (or reduce) variables
revise models to eliminate too-large sensitivities
adding details about sensitive variables or scenarios
obtain better estimates of sensitive variables
alter a real-world system to reduce sensitivities
…
Can be automatic or trial and error

9-‹#›

Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking
What-if analysis
Assesses solutions based on changes in variables or assumptions (scenario analysis)
What if we change our capacity at the milling station by 40% [what would be the impact]
Goal seeking
Backwards approach, starts with the goal and determines values of inputs needed
Example is break-even point determination
In-order to break even (profit = 0), how many products do we have to sell each month

9-‹#›

Decision Analysis with Decision Tables and Decision Trees
Decision Tables – a tabular representation of the decision situation (alternatives)
Investment Example
Goal: maximize the yield after one year
Yield depends on the status of the economy (the state of nature)
Solid growth
Stagnation
Inflation

9-‹#›

Decision Table –
Investment Example: Possible Situations
1. If solid growth in the economy, bonds yield 12%; stocks 15%; time deposits 6.5%
2. If stagnation, bonds yield 6%; stocks 3%; time deposits 6.5%
3. If inflation, bonds yield 3%; stocks lose 2%; time deposits yield 6.5%

9-‹#›

Payoff decision variables (alternatives)
Uncontrollable variables (states of economy)
Result variables (projected yield)
Tabular representation:

Decision Table
Investment Example: Decision Table

9-‹#›

Decision Table
Investment Example: Treating Uncertainty
Optimistic approach
Pessimistic approach
Treating Risk/Uncertainty:
Use known probabilities
Expected values

9-‹#›

Decision Table
Investment Example: Multiple Goals
Multiple goals
Yield, safety, and liquidity

9-‹#›

Decision Trees
Graphical representation of relationships
Multiple criteria approach
Demonstrates complex relationships
Cumbersome, if many alternatives exists
Tools include
Mind Tools Ltd., mindtools.com
TreeAge Software Inc., treeage.com
Palisade Corp., palisade.com

9-‹#›

Decision Trees – An Example

9-‹#›

Multi-Criteria Decision Making with Pairwise Comparisons
Having more than one criterion makes decision-making process complicated
Usually some type of weighing algorithm is used to analyze such problems
The Analytic Hierarchy Process
Developed by Thomas Saaty (1995, 1996)
A very popular technique for MCDM
Popular Tools – ExpertChoice.com
Web-based Tools – Web-HIPRE (hipre.aalto.fi)

9-‹#›

Application Case 9.6
U.S. HUD Saves the House by Using AHP for Selecting IT Projects
Company
Problem description
Proposed solution
Results

9-‹#›

Tutorial –
Applying AHP Using Web-HIPRE
Goal: select the most appropriate movie
Identify some criteria for making this decision
The main and sub-criteria for movie selection are
a. Genre: Action, Comedy, Sci-Fi, Romance
b. Language: English, Hindi
c. Day of Release: weekday, weekend
d. User/Critics Rating: High, Average, Low
Alternatives are the following current movies:
SkyFall, The Dark Knight Rises, The Dictator, Dabaang, Alien, and DDL

9-‹#›

Tutorial –
Applying AHP Using Web-HIPRE
Step 1: define the goal, criteria, and alternatives
Web-HIBRE allows defining all of these and relationships within an easy-to-use Web-based interface.

9-‹#›

Tutorial –
Applying AHP Using Web-HIPRE
Step 2: the main criteria are then ranked as they relate to the goal
A comparative ranking scale from 1 to 9 (with ascending order of importance) is used
The ranking is done using a Pairwise comparison procedure (i.e., divide-and-concur) between any two criteria for all combinations of twos
The tool readily normalizes the rankings of each of the main criteria over one another to a scale ranging from 0 to 1 and then calculates the row averages to arrive at an overall importance rating ranging from 0 to 1

9-‹#›

Tutorial –
Applying AHP Using Web-HIPRE

9-‹#›

Tutorial –
Applying AHP Using Web-HIPRE
Step 3: All of the subcriteria related to each of the main criteria are then ranked with their relative importance over one another
Step 4: Each alternative is ranked with respect to all of the subcriteria that are linked with the alternatives in a similar fashion using the relative scale of 0–9; then the overall importance of each alternative is calculated
Step 5: The final result are obtained from the composite priority analysis involving all the subcriteria and main criteria

9-‹#›

Tutorial –
Applying AHP Using Web-HIPRE

9-‹#›

Tutorial –
Applying AHP Using Web-HIPRE

9-‹#›

Tutorial –
Applying AHP Using Web-HIPRE

9-‹#›

End of the Chapter

Questions, comments

9-‹#›

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Chapter 10:

Modeling and Analysis: Heuristic Search Methods and Simulation

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support
(10th Edition)

10-‹#›

Learning Objectives
Explain the basic concepts of simulation and heuristics, and when to use them
Understand how search methods are used to solve some decision support models
Know the concepts behind and applications of genetic algorithms
Explain the differences among algorithms, blind search, and heuristics
(Continued…)

10-‹#›

Learning Objectives
Understand the concepts and applications of different types of simulation
Explain what is meant by system dynamics, agent-based modeling, Monte Carlo, and discrete event simulation
Describe the key issues of model management

10-‹#›

Opening Vignette
System Dynamics Allows Fluor
Corporation to Better Plan for Project and Change Management
Background
Problem description
Proposed solution
Results
Answer & discuss the case questions…

10-‹#›

Questions for
the Opening Vignette
Explain the use of system dynamics as a simulation tool for solving complex problems.
In what ways was it applied in Fluor Corporation to solve complex problems?
How does a what-if analysis help a decision maker to save on cost?
In your own words, explain the factors that might have triggered the use of system dynamics to solve change management problems in Fluor Corporation…

10-‹#›

Problem-Solving Search Methods
Search: choice phase of decision making
Search is the process of identifying the best possible solution / course of action [under limitations such as time, …]
Search techniques include
analytical techniques,
algorithms,
blind searching, and
heuristic searching

10-‹#›

Problem-Solving Search Methods

10-‹#›

Problem-Solving Search Methods
– Algorithmic/Heuristic
Cuts the search space
Gets satisfactory solutions more quickly and less expensively
Finds good enough feasible solutions to complex problems
Heuristics can be
Quantitative
Qualitative (in ES)
Traveling Salesman Problem see the example next >>>

10-‹#›

Traveling Salesman Problem
What is it?
A traveling salesman must visit customers in several cities, visiting each city only once, across the country. Goal: Find the shortest possible route.
Total number of unique routes (TNUR):
TNUR = (1/2) (Number of Cities – 1)!
Number of Cities TNUR
5 12
6 60
9 20,160
20 1.22 1018

10-‹#›

Traveling Salesman Problem

10-‹#›

Traveling Salesman Problem
Rule 1: Starting from home base, go to the closest city
Rule 2: Always follow an exterior route

10-‹#›

Application Case 10.1
Chilean Government Uses Heuristics to Make Decisions on School Lunch Providers
Questions for Discussion
What were the main challenges faced by JUNAEB?
What operation research methodologies were employed in achieving homogeneity across territorial units?
What other approaches could you use in this case study?

10-‹#›

When to Use Heuristics
When to Use Heuristics?
Inexact or limited input data
Complex reality
Reliable, exact algorithm not available
Computation time excessive
For making quick decisions
Limitations of Heuristics!
Cannot guarantee an optimal solution

10-‹#›

Tabu search
Intelligent search algorithm
Genetic algorithms
Survival of the fittest

Simulated annealing
Analogy to Thermodynamics

Ant colony and other Meta-heuristics
Modern Heuristic Methods

10-‹#›

Genetic Algorithms
It is a popular heuristic search technique
Mimics the biological process of evolution
Genetic algorithms
Software programs that “learn/search” in evolutionary manner, similar to the way biological systems evolve
An efficient, domain-independent search heuristic for a broad spectrum of problem domains
Main theme: Survival of the fittest
Moving toward better and better solutions by letting only the fittest parents create the future generations

10-‹#›

Evolutionary Algorithm
10010110
01100010
10100100
10011001
01111101
. . .
. . .
. . .
. . .

10010110
01100010
10100100
10011101
01111001
. . .
. . .
. . .
. . .

Selection
Reproduction
. Crossover
. Mutation
Current
generation
Next
generation
Elitism

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Each candidate solution is called a chromosome
A chromosome is a string of genes
Chromosomes can copy themselves, mate, and mutate via evolution
In GA we use specific genetic operators
Reproduction
Crossover
Mutation
GA Structure and GA Operators

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Genetic Algorithms
– Example: The Vector Game
Description of the Vector Game
Identifying a string of 5 binary digits
Default Strategy: Random Trial and Error
Improved Strategy: Use of Genetic Algorithms
In an iterative fashion, using genetic algorithm process and genetic operators, find the opponent’s digit sequence
See your book for functional details

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Item: 1 2 3 4 5 6 7
Benefit: 5 8 3 2 7 9 4
Weight: 7 8 4 10 4 6 4
Knapsack holds a maximum of 22 pounds
Need to fill it for maximum benefit (one per item)
Solutions take the form of a string of 1’s
Example Solution: 1 1 0 0 1 0 0
Means choose items 1, 2, 5:
Weight = 21, Benefit = 20
Evolver solution works in Microsoft Excel… 
A Classic GA Example:
The Knapsack Problem

10-‹#›

Define the objective function and constraint(s)

10-‹#›

Identify the decision variables and their characteristics

10-‹#›

Observe and analyze the results

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Observe and analyze the results

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The Knapsack Problem at Evolver
Monitoring the solution generation process…

10-‹#›

Genetic Algorithms
Limitations of Genetic Algorithms
Does not guarantee an optimal solution (often settles in a sub optimal solution / local minimum)
Not all problems can be put into GA formulation
Development and interpretation of GA solutions requires both programming and statistical skills
Relies heavily on the random number generators
Locating good variables for a particular problem and obtaining the data for the variables is difficult
Selecting methods by which to evolve the system requires experimentation and experience

10-‹#›

Genetic Algorithm Applications
Dynamic process control
Optimization of induction rules
Discovery of new connectivity topologies (NNs)
Simulation of biological models of behavior
Complex design of engineering structures
Pattern recognition
Scheduling, transportation, and routing
Layout and circuit design
Telecommunication, graph-based problems, …

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Simulation
Simulation is the “appearance” of reality
It is often used to conduct what-if analysis on the model of the actual system
It is a popular DSS technique for conducting experiments with a computer on a comprehensive model of the system to assess its dynamic behavior
Often used when the system is too complex for other DSS techniques

10-‹#›

Application Case 10.3
Simulating Effects of Hepatitis B Interventions
Questions for Discussion
Explain the advantage of operations research methods such as simulation over clinical trial methods in determining the best control measure for Hepatitis B.
In what ways do the decision and Markov models provide cost-effective ways of combating the disease?
Discuss how multidisciplinary background is an asset in finding a solution for the problem described in the case.
Besides healthcare, in what other domain could such a modeling approach help reduce cost?

10-‹#›

Imitates reality and captures its richness both in shape and behavior
“Represent” versus “Imitate”
Technique for conducting experiments
Descriptive, not normative tool
Often to “solve” [i.e., analyze] very complex systems/problems
Simulation should be used only when a numerical optimization is not possible
Major Characteristics of Simulation

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Advantages of Simulation
The theory is fairly straightforward
Great deal of time compression
Experiment with different alternatives
The model reflects manager’s perspective
Can handle wide variety of problem types
Can include the real complexities of problems
Produces important performance measures
Often it is the only DSS modeling tool for non-structured problems

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Disadvantages of Simulation
Cannot guarantee an optimal solution
Slow and costly construction process
Cannot transfer solutions and inferences to solve other problems (problem specific)
So easy to explain/sell to managers, may lead to overlooking analytical solutions
Software may require special skills

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Simulation Methodology
Steps:
1. Define problem 5. Conduct experiments
2. Construct the model 6. Evaluate results
3. Test and validate model 7. Implement solution
4. Design experiments

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Simulation Types
Probabilistic/Stochastic vs. Deterministic Simulation
Uses probability distributions
Time-dependent vs. Time-independent Simulation
Monte Carlo technique (X = A + B) [A, B, and X are all distributions]
Discrete Event vs. Continuous Simulation
Simulation Implementation
Visual Simulation and/or Object-Oriented Simulation

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Visual interactive modeling (VIM), also called Visual Interactive Simulation or Visual interactive problem solving
Uses computer graphics to present the impact of different management decisions
Often integrated with 3G and GIS
Users can perform sensitivity analysis
Static or dynamic (animation) systems
Virtual reality, immersive, …
Visual Interactive Simulation (VIS)

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Traffic at an Intersection from the Orca Visual Simulation

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Application Case 10.4
Improving Job-Shop Scheduling Decisions Through RFID: A Simulation-Based Assessment
Background
Problem description
Proposed solution
Results

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SIMIO Simulation Software

10-‹#›

SIMIO Simulation Software

10-‹#›

SIMIO Simulation Software

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Simulation Software
A comprehensive list can be found at
orms-today.org/surveys/Simulation/Simulation.html
Simio LLC, simio.com
SAS Simulation, sas.com
Lumina Decision Systems, lumina.com
Oracle Crystal Ball, oracle.com
Palisade Corp., palisade.com
Rockwell Intl., arenasimulation.com …

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System Dynamics Modeling
Macro-level simulation models in which aggregate values and trends are considered
Objective is to study the overall behavior of a system over time as a whole
Evolution of the various components of the system over time and as a result of interplay between the components over time
First introduced by Forrester (1958)
A widely used technique in operations research and management science

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System Dynamics Modeling

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Agent-Based Modeling
Agent – an autonomous computer program that observes and acts on an environment and directs its activity toward achieving specific goals
Relatively new technology
Other names include
Software agents
Wizards
Knowbots, Both
Intelligent software robots (Softbots) …

10-‹#›

Agent-Based Modeling
Agent-based modeling (ABM) is a simulation modeling technique to support complex decision systems where a system is modeled as a set of autonomous decision-making units called agents
A bottom-up approach to simulation modeling
Agent-based modeling platforms
SWARM (www.swarms.org),
Netlogo (http://ccl.northwestern.edu/netlogo),
RePast/Sugarscape (www.repast.sourceforge.net),
…

10-‹#›

Application Case 10.5
Agent-Based Simulation Helps Analyze Spread of a Pandemic Outbreak
Questions for Discussion
What are the characteristics of an agent-based simulation model?
List the various factors that were fed into the agent-based simulation model described in the case.
Elaborate on the benefits of using agent-based simulation models.
Besides disease prevention, in which other situations could agent-based simulation be employed?

10-‹#›

End of the Chapter

Questions, comments

10-‹#›

Start
Represent problem’s
chromosome structure
Generate initial solutions
(the initial generation)
Select elite solutions; carry
them into next generation
Test:
Is the solution
satisfactory?
Stop-
Deploy the
solution
Select parents to reproduce;
apply crossover and mutation
Next
generation
of solutions
Offspring
Elites
Yes
No
Staff time saved
E-Rx
E-Note
adverse drug
event (ADE)
ADE correction
cost
patient
treatment time
medical records
storage
+
+
–
–
+
–
–
–
–
+
+
–
radiology
performance
laboratory
performance
+
+
–
–
+
–
–
–
–
–
staff training
compliance via
EHR
+
–
+
+
+
–
–
–

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