©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 1
Mental Distanceand Its Implications for the Design of Software and Data
David A. Marca
University of PhoenixOne Research Drive
Westborough, Mass 01581U.S.A.
July 24, 2010
Welcome!5th
International ConferenceSoftware and Data Technologies
ICSOFT 2010, Athens, Greece, 22-24 July
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 2
Mental Distanceand Its Implications for the Design of Software and Data
David A. Marca
University of PhoenixOne Research Drive
Westborogh, Mass 01581U.S.A.
July 24, 2010
Important Note• Case study is: www.dooce.com.
• It is an excellent social network.
•
One of the best for showing the . traditional perspective of design.
•
The author wishes www.dooce.com
. much continued success!
DEFINITION Mental Distance
Mental distance is a measure of the conceptual similarity between the underlying intention of:
1) a social network conversation2) an online ad.
Long distance = poor ad alignment.
Short distance = good ad alignment.
Chen, 2002Lewis, 2003
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THESIS Language-Action Perspective
Given this definition of mental distance…the language-action perspective can give business:
1) access to small, dynamic niche markets occurring in social networks,
2) a way to create highly aligned online ads for those markets.
Marca, 2010©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 4
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 5
Mental Distanceand Its Implications for the Design of Software and Data……
Part 1 – Prolog●
Social Networks
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Mental Distanceand Its Implications for the Design of Software and Data……
Part 1 – PrologPart 2 – Traditional Perspective
●
Populations●
Connections
●
Conversations●
Long Distance
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Mental Distanceand Its Implications for the Design of Software and Data……
Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate Perspective
●
Inference●
Intention
●
Clusters●
Short Distance
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Mental Distanceand Its Implications for the Design of Software and Data……
Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate PerspectivePart 4 – Software Implications
●
Architecture●
Ontology
●
History●
Agents
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Mental Distanceand Its Implications for the Design of Software and Data……
Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate PerspectivePart 4 – Software ImplicationsPart 5 – Epilog
●
Semantic Web Fit
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Mental Distanceand Its Implications for the Design of Software and Data……
Part 1 – Prolog●
Social Networks
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 11
1. Social Networks Some Early Social Network Sites
Kasavanna, 2009
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1. Social Networks Exponential Growth 2003 to 2008
Cha, 2009
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Mental Distanceand Its Implications for the Design of Software and Data……
Part 1 – PrologPart 2 – Traditional Perspective
●
Populations●
Connections
●
Conversations●
Long Distance
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2. Populations Facebook = 4th Largest Population in the World
Scale, 2008
This week, just surpassed 500M users:http://www.bbc.co.uk/news/technology-10713199
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2. Populations Case Study: www.dooce.com … affluent U.S. Mothers
January 4, 2010
A monolog onwork-life balance.
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3. Connections Typical Structures, Complex, Hard to Analyze
Core –
Periphery Cliques
Scale FreeWatts-Strogatz
(friends)Freeman, 2000
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3. Connections Example: Twitter “Followers” Network
A Twitter “Followers”
Relationship Network
Hopkin, 2010
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3. Connections Example: Twitter “Following” Network
A Twitter “Following”
Relationship Network
A Twitter “Followers”
Relationship Network
Hopkin, 2010
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4. Conversations Example: Twitter “Conversation” Network
A Twitter “Following”
Relationship Network
A Twitter Conversation NetworkA Twitter “Followers”
Relationship Network
Hopkin, 2010
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4. Conversations Case Study: Diverse Family Life Conversations
An early morninglecture in semantics
“Can I have my treat after I finish myCocoa Puffs?”
“I think we need to have a long talkabout what youdefine as a treat.”
face hiddento protectprivacy
January 4, 2010
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4. Conversations Hard to Classify: Many, Varied Conversations
Breakfast
Journals
Exercise
Music
Fatherhood
Baby
January 4, 2010
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4. Conversations Simple Search Organization? ... May Not Work
Breakfastis missing(word not
in title)
January 4, 2010
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5. Long Distance Generalize Profile Poor Ad Alignment …
Laudon, 2004
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5. Long Distance Case Study: Ads Match Profile, Not Conversation
Books
Movie
Hair Spray
January 4, 2010
A monolog onwork-life balance.
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5. Long Distance Generalize Segment Poor Ad Alignment …
Laudon, 2004
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5. Long Distance Case Study: Ads Not Aligned to Breakfast
Books
Movie
Hair Spray
face hiddento protectprivacy
January 4, 2010
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 27
5. Long Distance Case Study: One Breakfast Ad … Poor Alignment
Milk
Books
Movie
Hair Spray
face hiddento protectprivacy
January 4, 2010
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 28
Mental Distanceand Its Implications for the Design of Software and Data……
Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate Perspective
●
Inference●
Intention
●
Clusters●
Short Distance
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6. Inference Case Study: Here is a conversation…
An early morninglecture in semantics
“Can I have my treat after I finish myCocoa Puffs?”
“I think we need to have a long talkabout what youdefine as a treat.”
face hiddento protectprivacy
January 4, 2010
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 30
6. Inference Natural Language Inference Engine Concepts
An early morninglecture in semantics
“Can I have my treatafter I finish myCocoa Puffs?”
“I think we need to have a long talkabout what youdefine as a treat.”
wantstreat
Horvitz, 1999
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6. Inference Product Knowledge Inference Engine Things
An early morninglecture in semantics
“Can I have my treatafter I finish myCocoa Puffs?”
“I think we need to have a long talkabout what youdefine as a treat.”
wantstreat
hadcereal
Ghani, 2002
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6. Inference Word Associations Inference Engine Meaning
An early morninglecture in semantics
“Can I have my treatafter I finish myCocoa Puffs?”
“I think we need to have a long talkabout what youdefine as a treat.”
wantstreat
hadcereal
serioustalk
Andrews, 2005
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6. Inference Social Context Inference Engine Meaning
An early morninglecture in semantics
“Can I have my treatafter I finish myCocoa Puffs?”
“I think we need to have a long talkabout what youdefine as a treat.”
wantstreat
hadcereal
wantssugar
hadsugar
serioustalk
Ehrlich, 2007
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6. Inference Intention Ontology Inference Engine Intent
An early morninglecture in semantics
“Can I have my treatafter I finish myCocoa Puffs?”
“I think we need to have a long talkabout what youdefine as a treat.”
wantstreat
hadcereal
wantssugar
hadsugar
serioustalk
stopsugar
HealthyChild
Banks, 2002
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7. Intention Three Types…
Heidegger, 1962
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7. Intention Intention for Being, Declaration: say who you are
HealthyChild
Being
Self Backstrom, 2006
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7. Intention Intention for Acting, Goal-oriented: do what you say
HealthyChild
Being
Self
CorrectDiet
Acting
Others Sabatar, 2002
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7. Intention Intention for Having, Possession-oriented: get results
HealthyChild
CorrectDiet
HealthyCereal
Being Acting Having
Self Others Things Recker, 2005
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7. Intention Intention for Being is context for Intention for Acting
HealthyChild
CorrectDiet
HealthyCereal
Being Acting Having
Self Others ThingsHeidegger, 1962
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7. Intention Intention for Acting is context for Intention for Having
HealthyChild
CorrectDiet
HealthyCereal
Being Acting Having
Self Others ThingsHeidegger, 1962
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8. Clusters A Conversation Cluster Creates an Intention
.
..
.
. .
Bergstrom, 2009
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8. Clusters Conversation 1: Articulating Need
.
..
.
. .
Need
Cohen, 2002
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8. Clusters Conversation 2: Forming “Right” Relationships
.
..
.
. .
Need
Relationship
Wang, 2004
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8. Clusters Conversation 3: Creating Possibilities
.
..
.
. .
Need
Relationship
Possibility
Denning, 2003
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8. Clusters Conversation 4: Determining Opportunities
.
..
.
. .
Need
Relationship
Possibility
Opportunity
Denning, 2003
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8. Clusters Conversation 5: Making Decisions
.
..
.
. .
Need
Relationship
Possibility
Opportunity
Decision
Dougherty, 1992
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8. Clusters Conversation 6: Taking Appropriate Action
Need
PossibilityDecision
Opportunity
Action Relationship
Winograd, 1998
HealthyChild
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9. Short Distance Intention Local Bias Niche Personalization
Laudon, 2004
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9. Short Distance #1. Declare an Intention for Being to Social Network
Need
PossibilityDecision
Opportunity
Action Relationship
Being Acting Having
Self Others Things
HealthyChild
Backstrom, 2006
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9. Short Distance #2. Group Agrees Upon an Intention for Acting
Need
PossibilityDecision
Opportunity
Action Relationship
Being Acting Having
Self Others Things
HealthyChild
CorrectDiet
Hill, 2006
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9. Short Distance #3. Group Agrees Upon an Intention for Having
Need
PossibilityDecision
Opportunity
Action Relationship
Being Acting Having
Self Others Things
HealthyChild
CorrectDiet
HealthyCereal
Praise, 2008
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9. Short Distance (Note Match with Classical Marketing-Sales Process)
Need
FunctionFeaturePurchase
Preferences
Loyalty Awareness
Being Acting Having
Self Others Things
HealthyChild
CorrectDiet
HealthyCereal
Ren, 2003
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9. Short Distance #4. Group Agrees Upon Product Preferences
Being Acting Having
Self Others Things
HealthyChild
CorrectDiet
nutrients
nosugar
willeat
HealthyCereal
Kamaladevi, 2010
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9. Short Distance #5. Products are Matched to Preferences
nutrients
nosugar
willeat. . .
Fiss, 2007
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9. Short Distance #6. Ad Alignment: Consistency, Relevance, Branding
Cereal
No MovieNo Books
Milk
face hiddento protectprivacy
January 4, 2010
Bundle
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 56
Mental Distanceand Its Implications for the Design of Software and Data……
Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate PerspectivePart 4 – Software Implications
●
Architecture●
Ontology
●
History●
Agents
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 57
10. Architecture #1. Parse: Conversation Word Neighborhoods
Natural LanguageProcessing
Conversation
Phrases
Mayfield, 2003
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10. Architecture #2. Infer: Word Neighborhoods Possible Intent
Natural LanguageProcessing
Inference Engine:Intention
Conversation
Phrases
Possible Intentions
Lee, 2001
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10. Architecture #3. Infer: Possible Intent Verified (Shared) Intent
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Conversation
Phrases
Possible Intentions
Verified Intention
Paek, 2000
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10. Architecture #4. Tag: Verified (Shared) Intent Niche Market
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Conversation
Phrases
Possible
Intentions
Verified Intention
Niche
Hunscher, 2006
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10. Architecture #5. Connect: Niche Market Preferences Ads
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Hogg, 2009
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10. Architecture #6. Ad Click Collective Bargaining Purchase
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase Lin, 2010
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11. Ontology “Conversation” and Its Types + Their Elements
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase
ConversationOntology TYPES:
●
Need●
Relationship●
Possibility●
Opportunity●
Decision●
ACTION
ELEMENTS:●
Request●
Promise●
Offer●
Counter●
…
Weigand, 2008
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11. Ontology “Intention” and Its Types + Elements (for e-Business)
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase
IntentionOntology
ConversationOntology TYPES:
●
Being●
Doing●
HAVING
ELEMENTS:●
Intent to Buy●
Product●
Preferences●
Order●
Pricing●
…
Barbosa, 2005
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11. Ontology Shared Intent, Agreement, Grounded Understanding
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase
InteractionOntology
IntentionOntology
ConversationOntology DIMENSIONS:
●
Intent●
Agreement●
UNDERSTANDING
ELEMENTS:●
Utterance●
Presentation●
Acceptance●
Confirmation●
…
Paek, 2000
See also Milo demonstration at:http://www.bbc.co.uk/news/10623423
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11. Ontology Marketing Mix Approach Elements are Intention-based
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
A;igned
Ads
Purchase
Niche MarketOntology
InteractionOntology
IntentionOntology
ConversationOntology MARKETING MIX:
●
Product●
Pricing●
Distribution●
COMMUNICATION
ELEMENTS:●
Agents
(understand…each niche market’s…intentions)●
Bundle
(built for one… intended
use)●
Ads
(High Alignment)●
Brand Equity…(product function =… intended use)●
…
Engelbach, 2006
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11. Ontology Content-based Access using Intentions + Distances
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase
Online AdOntology
Niche MarketOntology
InteractionOntology
IntentionOntology
ConversationOntology ACCESS TO:
●
Product Catalogs●
Yellow Pages
LIMITED BY:●
Constant Updates●
Rigid Set of Terms●
Large Vocabulary
SOLUTION:●
Vocabulary:…terms
= Intentions●
Relationships:…semantics among …terms = distances …between intentions
Guarino, 1999
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11. Ontology Intentions = Non-Ambiguous Preferences Mapping
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase
NegotiationOntology
Online AdOntology
Niche MarketOntology
InteractionOntology
IntentionOntology
ConversationOntology MAPPING:
●
Buyer preferences●
Seller preferences●
Intention-based map
NEGOTIATION:●
Non-ambiguity●
Use intentions in the …Yellow Pages and…Catalogs
BEYOND PRICE:●
Satisfy Need…●
Satisfy Intended Use
Beam, 1996
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12. History Reuse: Words Neighborhoods Intentions
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase
WordNeighborhoods
NegotiationOntology
Online AdOntology
Niche MarketOntology
InteractionOntology
IntentionOntology
ConversationOntology
Craven, 2000
WORDS…●
Assumptions●
Inter-relationships●
Collections
CONTEXT:●
Specific Topic●
The Conversation
CAUTION:●
Generic Associations●
Prior attempts to use…those assumptions…without success.
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12. History Reuse: Contexts + Phrases Intentions
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase
WordNeighborhoods
IntentionNetworks
NegotiationOntology
Online AdOntology
Niche MarketOntology
InteractionOntology
IntentionOntology
ConversationOntology
Paek, 1999
Context = Breakfast
1) Treat Sugar
2) No Treat No Sugar
3) No Sugar Health
CAUTION:Reasoning under uncertainty is likely.
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12. History Build: Self-Organizing Map of “Similarity Distances”
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase
WordNeighborhoods
IntentionNetworks
IntentionDistances
NegotiationOntology
Online AdOntology
Niche MarketOntology
InteractionOntology
IntentionOntology
ConversationOntology
Chen, 2002
HEALTH01. WELL-BEING
03. WELLNESS
07. VITALITY
12. FITNESS
18. STRENGTH
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12. History Use: Intention (Short Distance) More Preferences
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase
WordNeighborhoods
IntentionNetworks
IntentionDistances
NicheMarkets
NegotiationOntology
Online AdOntology
Niche MarketOntology
InteractionOntology
IntentionOntology
ConversationOntology HEALTH
-
Nutrients-
No Sugar-
Will Eat
HEALTH
01. WELL-BEING-
Vitamins-
Omega-3
Cohen, 2000
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12. History Organize: Taxonomy + Configuration + Qualities
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase
WordNeighborhoods
IntentionNetworks
IntentionDistances
NicheMarkets
ProductCatalogs
NegotiationOntology
Online AdOntology
Niche MarketOntology
InteractionOntology
IntentionOntology
ConversationOntology
Brafman, 2007Buchner, 1999
CEREAL
- Nutrients- No Sugar
- Nutrients- No Sugar- Small Size
- Nutrients- No Sugar- Small Size- Will Eat
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12. History Negotiation Success + Strategic Delay Low Cost
Natural LanguageProcessing
Inference Engine:Intention
Analysis: ConfirmShared Intent
Inference Engine:Niche Market
Inference Engine:Preferences Ads
Agent: CollectiveBargaining
Conversation
Phrases
Possible Intentions
Verified Intention
Niche
Aligned Ads
Purchase
WordNeighborhoods
IntentionNetworks
IntentionDistances
NicheMarkets
ProductCatalogs
PurchaseNegotiations
NegotiationOntology
Online AdOntology
Niche MarketOntology
InteractionOntology
IntentionOntology
ConversationOntology
Mok, 2005Winoto, 2002
STRATEGIC DELAY
©OpenProcess, Inc.
13. Agents Hybrid = Knowledge Base + Collaborative Filtering
Internet Extranet Intranet Information
SocialNetwork
ProductCompany
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 75
Tran, 2000
Mental Distance, 76
13. Agents Knowledge Base: Standard Taxonomy, All Configs.
Internet Extranet Intranet Information
SocialNetwork
ProductCompany
PrivateCatalog
PublicCatalog
ConfigurationExperts
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 76
CIAgent
Public Offering
Public Offering
Guttman, 1998
Mental Distance, 77
13. Agents Knowledge Base: Similar Products + Configurations
Internet Extranet Intranet Information
SocialNetwork
ProductCompany
PrivateCatalog
PublicCatalog
ConfigurationExperts
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 77
CIAgent
Public Offering
Public Offering
Recommendation
CompetitiveInformation
Tran, 2000
©OpenProcess, Inc.
13. Agents Collaborative Filtering: Intention-based Preferences
Internet Extranet Intranet Information
SocialNetwork
ProductCompany
PublicCatalog
IntentionAgent
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Online Ads
PrivateCatalog
ConfigurationExperts
CIAgent
Public Offering
Recommendation
Preferences
CompetitiveInformation
Public Offering
Ravi, 2010
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13. Agents Buy Step 1: Ad Conversation for Having Prefs.
Internet Extranet Intranet Information
SocialNetwork
ProductCompany
PrivateCatalog
PublicCatalog
NicheAgent
IntentionAgent
ConfigurationExperts
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 79
CIAgent
Public Offering
Intention +Preferences
AdClick
Recommendation
Online Ads
Preferences
CompetitiveInformation
Public Offering
Varian, 2010
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13. Agents Buy Step 2: Preferences Vendors + Their Channels
Internet Extranet Intranet Information
SocialNetwork
ProductCompany
PrivateCatalog
PublicCatalog
NicheAgent
IntentionAgent
ConfigurationExperts
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 80
CIAgent
Public Offering
Intention +Preferences
AdClick
CompanyAgent
ChannelAgent
Selection
Recommendation
Online Ads
Preferences
CompetitiveInformation
Public Offering
Pavlou, 2006
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13. Agents Buy Step 3: Use Successful Negotiation Strategies
Internet Extranet Intranet Information
SocialNetwork
ProductCompany
PrivateCatalog
PurchaseNegotiations
PublicCatalog
NicheAgent
IntentionAgent
ConfigurationExperts
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 81
CIAgent
Public Offering
Intention +Preferences
AdClick
CompanyAgent
ChannelAgent
Bargaining
Recommendation
Online Ads
Preferences
CompetitiveInformation
Public Offering
Successful Strategies
Lee, 1998
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13. Agents Buy Step 4: New Configurations Might Be Offered
Internet Extranet Intranet Information
SocialNetwork
ProductCompany
PrivateCatalog
PurchaseNegotiations
PublicCatalog
NicheAgent
IntentionAgent
ConfigurationExperts
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 82
CIAgent
Public Offering
Intention +Preferences
AdClick
CompanyAgent
ChannelAgent
Bargaining
Recommendation
Online Ads
Preferences
CompetitiveInformation
Public Offering
Private Offering
Configuration Need
Successful Strategies
Kim, 2008
13. Agents Buy Step 5: Publish Previously Non-public Configs.
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 83
Internet Extranet Intranet Information
SocialNetwork
ProductCompany
Recommendation
CompetitiveInformation
Public Offering
PurchaseNegotiations
PublicCatalog
NicheAgent
IntentionAgent
Intention +Preferences
AdClick
Successful Strategies
PrivateOfferings
ConfigurationExperts
CIAgent
CompanyAgent
ChannelAgent
Bargaining
PrivateCatalog
Online Ads
Preferences
Private Offering
Configuration Need
Public Offering
Dubrovsky, 1991
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Mental Distanceand Its Implications for the Design of Software and Data……
Part 1 – PrologPart 2 – Traditional PerspectivePart 3 – Alternate PerspectivePart 4 – Software ImplicationsPart 5 – Epilog
●
Semantic Web Fit
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14. Semantic Web Fit Message + Transport Layers
HTTP, FTP, SMTP, …
SOAP
WS Security Secure Messaging
XML Messaging
Transport
Web Services Stack
Yu, 2008
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 86
14. Semantic Web Fit Semantic-driven Computation: Intention + Distance
HTTP, FTP, SMTP, …
SOAP
WS Security Secure Messaging
XML Messaging
Transport1.
Sem
antic
s
Web Services Stack Extensions
Bussler, 2002
©OpenProcess, Inc. Do not copy. Do not distribute. Mental Distance, 87
14. Semantic Web Fit Auto Service Composition: Catalogs + Yellow Pages
HTTP, FTP, SMTP, …
SOAP
WS Security
WSDL Service Description
Secure Messaging
XML Messaging
Transport1.
Sem
antic
s
2. Catalog of Configurations
Web Services Stack Extensions
UDDI Service Publication 2. Product Yellow Pages
Kajan, 2004
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Semantic Web Fit Ontology-driven Profiling: Intention disambiguation niche preferences pinpoint ads simple discovery.
HTTP, FTP, SMTP, …
SOAP
WS Security
WSDL
UDDI
UDDI / WS Inspection Service Discovery
Service Publication
Service Description
Secure Messaging
XML Messaging
Transport1.
Sem
antic
s
3. Highly Targeted Ads
2. Product Yellow Pages
2. Catalog of Configurations
Web Services Stack Extensions
Parkhomenko, 2003
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Semantic Web Fit Semantic Composability: Intention-based Configurations
Service Composition / Flow
HTTP, FTP, SMTP, …
SOAP
WS Security
WSDL
UDDI
UDDI / WS Inspection
Trading Partner Agreement
BPEL
Service Agreement
Service Discovery
Service Publication
Service Description
Secure Messaging
XML Messaging
Transport
Ordinary Transact / Deliver
1. S
eman
tics 5. Agent-based Bargaining
3. Highly Targeted Ads
2. Product Yellow Pages
2. Catalog of Configurations
Web Services Stack Extensions
Medjahed, 2003
Thank you!LinkedIn
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Mental Distanceand Its Implications for e-Business Software
David A. Marca
University of Phoenix2310 Crossroads Drive
Madison, WI 53718U.S.A.
July 24, 2010
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