social life networks presentation at fb 110713
DESCRIPTION
Presentation given at Facebook on Social Life NetworksTRANSCRIPT
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and�Middle of the Pyramid�
Ramesh Jain With
Several Collaborators
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1. Networks 2. Computing Networks 3. Social Networks 4. Social Life Networks 5. Major Challenge: Micro-events to Situations 6. Our approach 7. Going Forward
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Different media and information sources Strongly emerging participatory culture Collective knowledge and intelligence of
society
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People Things Events
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Document created by Humans. Text, Music, Movies
Data collected by Humans Photos, Audio, …
Documents, Data, Events
Events happen. Most documents describe events and objects in those. Most data is collected for events.
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Facebook, Twitter, Google +, … Sensor networks
Billions of sensor getting connected
Ambitious projects Planetary Skin by Cisco and NASA Smart Planet by IBM
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Can things in real world be connected to other things?
Does this even make sense?
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Five Senses connect us to the world. We use our sensors (vision, audio, …) to
experience the world. Sensors could be the interface between the
Cyberspace and the Real World. Sensors are placed for ‘detecting events’.
How do you decide what sensors to put at any place?
Would you put a sensor if nothing interesting ever happens at a place?
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Causal
Experiential
Structural
People Things Places Time Experiences Events
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Data Objects Relationships and Events
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Objects -- popular in the West. Relationships and Events – popular in the
East. Objects and Events – seems to be the new
trend.
The Web has re-emphasized the importance of every object and event being connected to others -- East Meets West.
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Consider a Web in which each node Is an event Has informational as well as experiential data Is connected to other nodes using
Referential links Structural links Relational links Causal links
Explicit links can be created by anybody
This EventWeb is connected to other Webs.
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SN are web-based services that allow individuals to: construct a public or semi-public profile within a bounded system, articulate a list of other users with whom they share a connection, and view and traverse their list of connections and those made by others
within the system.
The nature and nomenclature of these connections may vary from site to site.
Professor at University of California, IrvineStudied Electronics and Communications at Indian Institute of Technology, KharagpurLives in Irvine, CaliforniaMarried to Sudha JainKnows English, HindiFrom NagpurBorn on June 8
Professor at University of California, IrvineStudied Electronics and Communications at Indian Institute of Technology, KharagpurLives in Irvine, CaliforniaMarried to Sudha JainKnows English, HindiFrom NagpurBorn on June 8
Node in a SN
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Professor at University of California, IrvineStudied Electronics and Communications at Indian Institute of Technology, KharagpurLives in Irvine, CaliforniaMarried to Sudha JainKnows English, HindiFrom NagpurBorn on June 8
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Connecting People
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My Grandparents R My BFF! OMG!
WSJ May 9, 2011
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Massive collection of events. Have been reporting events as micro-blogs
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Time
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Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?
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FROM TWEETS TO REVOLUTIONS
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Time
Atomic and Composite Events
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Middle 4 Billion
Top 1.5 Billion
Bottom 2 Billion
Middle of the Pyramid (MOP):
Ready, BUT …
Most attention by Technologists – so far.
Not Ready
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Every human society should be provided with the first two.
Other stages follow only after that.
Basic
Highest
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Resources Physical: food, water, goods, … Informational: Wikipedia, Doctors, … Transportation Employment Spiritual
Timeliness Efficiency
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Connecting People
And Resources
Aggregation and
Composition
Situation Detection
Alerts
Queries
Information
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All traditional Persistent Web sources Micro blogs
Status updates Tweets Streams
Micro Events All sensors ‘Chirping’ Internet of Things
People input in any form
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Result of Exponential growth in connectivity Sensor Networks
Evolution of Sharing Culture Technology for Collective Knowledge
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Each Micro-blog: What’s on your mind? What’s happening? Share What’s New …
Really an event reported by Humans. Can associate experiential data along with
information. Time and location can be associated.
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Billions of disparate kinds of sensors being placed everywhere.
Each sensor detects ‘basic events’ and broadcasts it in a simple form.
Develop a system to process these micro-events and make them useful.
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‘Chirps’ could be of different types Define behaviors like:
Heavy traffic Popular event going on People leaving X area Violence starting . . .
Use for Macro-behvior analysis
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Less abstrac*on,
More detail
More abstrac*on,
Less detail
Characteriza*ons
Transforma*ons
Level 1: Unified representa*on (STT Data)
Level 2: Aggrega*on (Emage)
Level 3: Symbolic Rep.
(Events)
Proper*es
Proper*es
Proper*es
Representa*ons
Level 0: Raw data
Examples
Speed at Exit 7
Number of accidents
Average speed, Occupancy rate
Loop sensors
…
e.g. Waze, 511
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Less abstrac*on,
More detail
More abstrac*on,
Less detail
Characteriza*ons
Transforma*ons
Level 1: Unified representa*on (STT Data)
Level 2: Aggrega*on (Emage)
Level 3: Symbolic Rep.
(Events)
Proper*es
Proper*es
Proper*es
Representa*ons
Level 0: Raw data
Examples
Pollen count in NYC
Badly affected areas
Mean traffic smoke exposure time
Tweets Traffic congestion Pollen counts
Fire reports
Per capita Asthma tweets
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From Micro-behavior to Macro-behavior Studied in many fields:
Economics Thermodynamics Systems Biology
Web facilitates this for many novel applications
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Divide space (world) into small Pixels of appropriate size.
Assume that each event is a particle of a specific type. Create a Social Image for specific type of events.
A time-ordered sequence of these emages will be similar to a video representing spatio-temporal changes in events of that type.
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S. No Operator Input Output 1 Selection σ Temporal
E-‐mage Set Temporal E-‐mage Set
2 Arithmetic & Logical⊕
K*Temporal E-‐mage Set
Temporal E-‐mage Set
3 Aggregation α Temporal E-‐mage set Temporal E-‐mage Set 4 Grouping γ Temporal E-‐mage Set Temporal E-‐mage Set 5 Characterization :
• Spatial φ • Temporal τ
• Temporal E-‐mage Set • Temporal Pixel Set
• Temporal Pixel Set • Temporal Pixel Set
6 Pattern Matching ψ • Spatial φ • Temporal τ
• Temporal E-‐mage Set • Temporal Pixel Set
• Temporal Pixel Set • Temporal Pixel Set
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Spatio temporal variation: Event detection
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into ‘high’ and ‘low ’activity zones.
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Situational controller
• Goal • Macro Situation • Rules
Micro event e.g. “Arrgggh, I
have a sore throat”
(Loc=New York, Date=12/09/10)
Macro situation
Control Action “Please visit nearest CDC
center at 4th St immediately”
Date=12/09/10
Alert Level=High
Level 1 personal threat + Level 3 Macro threat -> Immediate action
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1. For centralized agencies Most of what we have done so far
2. For individuals who subscribe Asthma
3. Alerts based on (implicit subscription): user’s (FB) interests, events attending, trips, sports, music, fan pages… Maybe we can derive asthma, from FB details?
4. I’m bored! What’s around me? (based on a generic interest set) NowLedger
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Brand monitoring Epidemic monitoring Political campaigns Decision making: e.g. iphone new store
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Asthma Wildfires Traffic Dating Coupons …
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Concerts, Campaigns, Memorabilia, Book stores, (anything you are a fan of)
Your friends Only show content whose ‘information’ is high.
If your friend normally lives 500 miles away and is NOW within 5 miles then alert. If he is always within 2 miles, don’t alert.
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Food Drinks Movies Concerts Academic Professional
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Direct the innovation and R&D towards the needs of the World’s middle class – the
Middle of the Pyramid (MOP).
Expand the Middle to cover the Bottom.
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Health Education Agriculture Social
For addressing all life elements.
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Resource ingestion Situation analysis ‘Real Time’ matching of needs
and availability of resources Interaction environments User engagement, … and many
others
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Event Based Experience Centric Centered around YOU
No Country Left Behind
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Contact: [email protected]