This document discusses connecting people, things, events, and information through networks. It proposes representing real-world events as "emages" or social images that aggregate raw sensor and social media data into aggregated representations over time and space. These emages could then be analyzed and transformed using relational algebra operators to detect patterns and situations. Alerts and recommendations could be generated by matching micro events to macro situations. The goal is to develop applications and services centered around real-time analysis of events and experiences to benefit individuals and society.

1. and
Middle of the Pyramid
Ramesh Jain
With
Several Collaborators

2. 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

3.  Different media and information sources
 Strongly emerging participatory culture
 Collective knowledge and intelligence of
society

4.  People
 Things
 Events

5. Documents, Data, Events
 Document created by Humans.
 Text, Music, Movies
 Data collected by Humans
 Photos, Audio, …
 Events happen.
 Most documents describe events and objects in those.
 Most data is collected for events.

6.  Facebook, Twitter, Google +, …
 Sensor networks
 Billions of sensor getting connected
 Ambitious projects
 Planetary Skin by Cisco and NASA
 Smart Planet by IBM

7.  Can things in real world be connected to other
things?
 Does this even make sense?

8.  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?

9. People
Structural
Things
Places
Causal
Time
Experiences
Experiential
Events

10.  Data
 Objects
 Relationships and Events

11.  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.

12.  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.

13.  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.
Node in a SN Professor at
University of California, IrvineStudied
Electronics and Communications at Indian
Institute of Technology, KharagpurLives in
Professor at
University of California,
IrvineStudied Electronics and
Communications at Indian
Institute of Technology,
Irvine, CaliforniaMarried to Sudha JainKnows
English, HindiFrom NagpurBorn on June 8
KharagpurLives in Irvine,
CaliforniaMarried to Sudha
JainKnows English,
HindiFrom NagpurBorn on
June 8

14. 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

15. Connecting
People

16. My Grandparents
R My BFF! OMG!
WSJ May 9, 2011

17. Have been reporting events as micro-blogs
Massive collection of events.

18. Time

19. Does the flap of a butterfly’s wings in Brazil set off a tornado
in Texas?

20. FROM TWEETS TO REVOLUTIONS

21. Atomic and Composite Events
Time

22. Most attention by
Top 1.5 Technologists – so
Billion far.
Middle of the Pyramid
Middle 4 Billion
(MOP):
Ready, BUT …
Bottom 2 Billion
Not Ready

24. Highest
Basic
Every human society should be provided with the first
two.
Other stages follow only after that.

26.  Resources
 Physical: food, water, goods, …
 Informational: Wikipedia, Doctors, …
 Transportation
 Employment
 Spiritual
 Timeliness
 Efficiency

27. Connecting Information
People
Aggregation Situation Alerts
and Detection
CompositionAnd
Queries
Resources

28.  All traditional Persistent Web sources
 Micro blogs
 Status updates
 Tweets
 Streams
 Micro Events
 All sensors ‘Chirping’
 Internet of Things
 People input in any form

29.  Result of
 Exponential growth in connectivity
 Sensor Networks
 Evolution of Sharing Culture
 Technology for Collective Knowledge

30.  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.

31.  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.

32.  ‘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

33. Representa*ons Examples
More
abstrac*on,
Level
3:
Number of accidents
Proper*es
Symbolic
Rep.
Less
detail (Events)
Characteriza*ons
Level
2:
Average speed,
Transforma*ons Aggrega*on
Proper*es Occupancy rate
(Emage)
Level
1:
Unified
representa*on
Proper*es Speed at Exit 7
(STT
Data)
Less
abstrac*on,
More
detail
Level 0: Raw data
Loop
…
sensors
e.g.
Waze,
511
33

34. Representa*ons Examples
More
abstrac*on,
Level
3:
Proper*es Badly affected areas
Symbolic
Rep.
Less
detail (Events)
Characteriza*ons
Level
2:
Mean traffic smoke
Transforma*ons Aggrega*on
Proper*es exposure time
(Emage)
Per capita Asthma
tweets
Level
1:
Unified
representa*on
Proper*es Pollen count in NYC
(STT
Data)
Less
abstrac*on,
More
detail
Level 0: Raw data
Tweets Pollen
Fire
Traffic congestion
counts reports
34

35.  From Micro-behavior to Macro-behavior
 Studied in many fields:
 Economics
 Thermodynamics
 Systems Biology
 Web facilitates this for many novel applications

36.  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.

38. S.
No
Operator
Input
Output
1
Selection
σ
Temporal
Temporal
E-­‐mage
Set
E-­‐mage
Set
2
Arithmetic
&
K*Temporal
E-­‐mage
Temporal
E-­‐mage
Set
Logical⊕
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
E-­‐mage
Set
• Temporal
Pixel
Set
• Temporal
τ
• Temporal
Pixel
Set
• Temporal
Pixel
Set
6
Pattern
Matching
ψ
• Spatial
φ
• Temporal
E-­‐mage
Set
• Temporal
Pixel
Set
• Temporal
τ
• Temporal
Pixel
Set
• Temporal
Pixel
Set
38

39.  Spatio temporal variation: Event detection

41. into ‘high’ and ‘low ’activity zones.

42. Macro situation
Alert Level=High
Date=12/09/10
Micro event Situational
Control Action
e.g. “Arrgggh, I controller
“Please visit
have a sore
nearest CDC
throat” • Goal
center at 4th St
(Loc=New York, • Macro Situation
immediately”
Date=12/09/10) • Rules
Level 1 personal threat + Level 3 Macro threat -> Immediate
action

43. 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

44.  Brand monitoring
 Epidemic monitoring
 Political campaigns
 Decision making: e.g. iphone new store

45.  Asthma
 Wildfires
 Traffic
 Dating
 Coupons
 …

46.  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.

47.  Food
 Drinks
 Movies
 Concerts
 Academic
 Professional

49. 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.

50. Health Education Agriculture Social
For addressing all life elements.

51.  Resource ingestion
 Situation analysis
 ‘Real Time’ matching of needs
and availability of resources
 Interaction environments
 User engagement, … and many
others

52.  Event Based
 Experience Centric
 Centered around YOU
 No Country Left Behind

53. Contact: jain@ics.uci.edu