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Ramesh Jain
                    with
            Several Collaborators

8/17/2012                           1
    Scarcity: inadequate supply, Insufficiency of
      amount or supply



     Abundance: an extremely plentiful or
      oversufficient quantity or supply




                    Proprietary and Confidential, Not For
8/17/2012                        Distribution               2
Scarcity




            Proprietary and Confidential, Not For
8/17/2012                Distribution               3
Abundance




              Proprietary and Confidential, Not For
8/17/2012                  Distribution               4
Proprietary and Confidential, Not For
8/17/2012                Distribution               5
Proprietary and Confidential, Not For
8/17/2012                Distribution               6
Proprietary and Confidential, Not For
8/17/2012                Distribution               7
We are immersed in Networks of
     People
     Things
     Events



It is now possible to be Pansophical.
 8/17/2012                         8
Past is EXPERIENCE
            Present is EXPERIMENT
            Future is EXPECTATION

                Use your Experiences
                In your Experiments
            To achieve your Expectations

8/17/2012                                  9
Astrology



               To
                 Astronomical
                 Volumes of
                 Data


8/17/2012                       10
Proprietary and Confidential, Not For
8/17/2012                Distribution               11
Have been reporting events as micro-blogs


Sensors and Internet of Things are creating
  and reporting even more events than
  humans are.

 8/17/2012                                   12
   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.
   Data
   Objects
   Relationships and Events
Recognize
                              Objects
                              Situations
            Knowledge
 Observe
 Big Data
                        Act
                         Planning
8/17/2012
                         Control      15
   Take place in the real world.
   Captured using different sensory mechanism.
       Each sensor captures only a limited aspect of the
        event.
   Can be used to bridge the semantic gap.
   Conferences
       Days
         Sessions
           Talks
             Purpose of the talk
   Wedding
   An Earthquake
   The Big Bang
   9/11
   Formation of Google
   Media Lab Trip
   Me
       My Birth,
       Being here, and
       Dying in 100 years.
People
Things
Places
Time
Experiences
Events
    E by Westerman and Jain

    E* by Gupta and Jain
Connecting
  People
Reporting events as micro-blogs


     Massive collection of events.
 Facebook reports 20 Billion updates –
          3 Billion Photos –
             each month.
Time
Does the flap of a butterfly’s wings in Brazil set off a tornado
  in Texas?
Atomic and Composite Events




     Time
Eventshop 120721
Eventshop 120721
Current
            Social
            Networks


            Important
            Unsatisfied
            Needs


8/17/2012                 26
The World as seen through
     Mobile Phones
                      Most attention by
       Top 1.5        Technologists – so
       Billion        far.


                           Middle of the Pyramid
  Middle 3.5 Billion
                                  (MOP):
                              Ready, BUT …


   Bottom 2 Billion
                                     Not Ready
   Resources
       Physical: food, water, goods, …
       Informational: Wikipedia, Doctors, …
       Transportation
       Employment
       Spiritual
   Timeliness
   Efficiency
Connecting            Information
                  People
            Aggregation Situation    Alerts
               and
            Composition
                       And
                        Detection

                                    Queries
            Resources



8/17/2012                                       29
Proprietary and Confidential, Not For
8/17/2012                Distribution               30
Dynamic    Event    Situation




 Static    Object    Scene




          Atomic    Composite
    Situation: An actionable abstraction of
      observed spatio-temporal characteristics
     Allow users to define their own spatio-
      temporal features and create the situation
      detection filters.




8/17/2012                                          32
Level 0: Raw data streams
e.g. tweets, cameras, traffic, weather, …




                                                                     …
               Level 1: Unified
               representation               Properties
                 (STT Data)
                                                           STT
                                                          Stream

                  Level 2:
                Aggregation                 Properties    Emage
                 (Emage)



                   Level 3:
                Symbolic rep.               Properties   Situation
                 (Situations)
(a) Pollen levels (Source: Visual)           (b) Census data (Source: text file)           (c) Reports on ‘Hurricanes’ (source: Twitter stream)




d) Cloud cover (Source: Satellite imagery)    (e) Predicted hurricane path (source: KML)   (f) Open shelters coverage(Source: KML)




               Representation for different data sources into a
                     common spatio-temporal format.
S. No Operator                 Input                 Output
1         Selection           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            Temporal E-mage Set   Temporal E-mage Set
5         Characterization :
          •Spatial            •Temporal E-mage Set •Temporal Pixel Set
          •Temporal           •Temporal Pixel Set   •Temporal Pixel Set
6         Pattern Matching 
          •Spatial            •Temporal E-mage Set •Temporal Pixel Set
          •Temporal           •Temporal Pixel Set   •Temporal Pixel Set
                                                                           35
    8/17/2012                                                               35
Experimentation is                                Front End GUI


essential to deal with    New
                          Data
                                      New
                                      Query
                                                               E-mage
                                                               Stream
                                                                                        Alert
                                                                                       Request

evolving unstructured    Source

                                               Back End Controller
sensory data.                                                  E-mage Stream


                                                                                   Personalized
                             Registered       Stream Query Processor
                                                                                    Alert Unit
Inspired by
                              Queries


                                                               E-mage Stream
Photoshop.
                                                                                         User Info

                         Registered
                           Data                   Data Ingestor                     Raw Data
                                                                                     Storage
                          Sources



                                                   API Calls         Raw Spatial
                                                                     Data Stream

                                                   Data Cloud




   8/17/2012                                                                                   36
    Business decision making: Demand-supply
      analysis, opening a new store, offer,…
     Medical : Epidemic monitoring, Asthma,
      pollution effect mitigation
     Disaster relief: (hurricane, flood, fire) directing
      people to appropriate resources.
     Traffic: Suggesting best routes

     Election

8/17/2012                                                   37
Proprietary and Confidential, Not For
8/17/2012                Distribution               38
Proprietary and Confidential, Not For
8/17/2012                Distribution               39
Retail Store
                                                    Locations




                                                    Net Catchment
                                                    area




            Proprietary and Confidential, Not For
8/17/2012                Distribution                               40
Proprietary and Confidential, Not For
8/17/2012                Distribution               41
Planetary scale               1) Macro
   sensing                    situation
Social sensors

Device sensors
                       +
Macro sensors


                                                      2)
                                                Personalized
                       Personal                   situation
                       context
                  Personal life streams

                                           +
                       Profile/
                      Preferences                e.g. High Flu risk                3)
                                                                               Recommend
                                                                                 Actions
                                                     Available resources
                                                                           +
                                          Resource data
into ‘high’ and ‘low ’activity zones.




                       Proprietary and Confidential, Not For
8/17/2012                           Distribution               43
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
  8/17/2012
                             action                        44
8/17/2012   45
Flood         Shelter
                          Classify (Flood level - Shelter)


              Twitter


            Flood Level
              Shelter




8/17/2012                                                    46
8/17/2012   47
Proprietary and Confidential, Not For
8/17/2012                Distribution               48
1.   Alert me when major Allergy outbreak
     happens in my location !
2.   How healthy is today for me ?
3.   What is the best location for me to undertake
     outdoor activities?
ϵ {low,
                                        Allergy Threat Level            mid, high}

                                                  
                                                                            US,
                                                                          24 hrs,
                                                                       1 X 1 lat long




                    Air quality                       Pollen count              Tweet reports




                      Emage                              Emage                               Emage
                 (air quality index)                  (pollen level)                    (number of reports)
                         Δ                                  Δ                                  Δ


                    Air quality                                                         S-t-t (#reports)
   US,                                                 Pollen count
  24 hrs,                                                                                       Δ
1X1lat long
                                           US,
                                         24 hrs,                             US,
                                       1X1 lat long                       24 hours,
   Weather.com                                                                              Twitter
                                                                            2X2
                                                      Pollen.com
                                                                                         Twitter.com
ϵ {low,
                       Personal asthma threat                 mid, high}

                                                      Thresholds
                                                      Low:{0, 0.3],
                                                      Mid: {0.3, 0.7],
                                                      High: {0.7,1}




         Heart rate               Sneezing severity                      Asthma threat level
                ∏                                                                ∏
                                          ∏


       Sensor stream                                                          EventShop
                                        Twitter



Cardio device
                                      Twitter.com                               Pollen.com, AQI.com,
                                                                                        Twitter
Eventshop 120721
Proprietary and Confidential, Not For
8/17/2012                Distribution               53
    Framework tested using applications:
             Store location
             Political campaign
             Flu monitoring
       EventShop system:
               Operators implemented:
                 Selection , Arithmetic & Logical, Aggregation , Grouping,
                  Characterization (spatial + temporal), Pattern Matching
                  (spatial + temporal)
               Applications tested:
                 Thai flood relief
                 Hurricane alerts
                 Safe locations for Asthmatic patients

8/17/2012                                                                     54
    Scalability
     Data discovery
     Application discovery
     Conceptual modeling of situations
     Richer operation set
     User experience




                  Proprietary and Confidential, Not For
8/17/2012                      Distribution               55
    Make EventShop Robust
     Develop system to deal with BIG DATA
     Experiment with many applications




                  Proprietary and Confidential, Not For
8/17/2012                      Distribution               56
Proprietary and Confidential, Not For
8/17/2012                Distribution               57

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Eventshop 120721

  • 1. Ramesh Jain with Several Collaborators 8/17/2012 1
  • 2. Scarcity: inadequate supply, Insufficiency of amount or supply  Abundance: an extremely plentiful or oversufficient quantity or supply Proprietary and Confidential, Not For 8/17/2012 Distribution 2
  • 3. Scarcity Proprietary and Confidential, Not For 8/17/2012 Distribution 3
  • 4. Abundance Proprietary and Confidential, Not For 8/17/2012 Distribution 4
  • 5. Proprietary and Confidential, Not For 8/17/2012 Distribution 5
  • 6. Proprietary and Confidential, Not For 8/17/2012 Distribution 6
  • 7. Proprietary and Confidential, Not For 8/17/2012 Distribution 7
  • 8. We are immersed in Networks of  People  Things  Events It is now possible to be Pansophical. 8/17/2012 8
  • 9. Past is EXPERIENCE Present is EXPERIMENT Future is EXPECTATION Use your Experiences In your Experiments To achieve your Expectations 8/17/2012 9
  • 10. Astrology To Astronomical Volumes of Data 8/17/2012 10
  • 11. Proprietary and Confidential, Not For 8/17/2012 Distribution 11
  • 12. Have been reporting events as micro-blogs Sensors and Internet of Things are creating and reporting even more events than humans are. 8/17/2012 12
  • 13. 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.
  • 14. Data  Objects  Relationships and Events
  • 15. Recognize Objects Situations Knowledge Observe Big Data Act Planning 8/17/2012 Control 15
  • 16. Take place in the real world.  Captured using different sensory mechanism.  Each sensor captures only a limited aspect of the event.  Can be used to bridge the semantic gap.
  • 17. Conferences  Days  Sessions  Talks  Purpose of the talk  Wedding  An Earthquake  The Big Bang  9/11  Formation of Google  Media Lab Trip  Me  My Birth,  Being here, and  Dying in 100 years.
  • 18. People Things Places Time Experiences Events E by Westerman and Jain E* by Gupta and Jain
  • 20. Reporting events as micro-blogs Massive collection of events. Facebook reports 20 Billion updates – 3 Billion Photos – each month.
  • 21. Time
  • 22. Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?
  • 23. Atomic and Composite Events Time
  • 26. Current Social Networks Important Unsatisfied Needs 8/17/2012 26
  • 27. The World as seen through Mobile Phones Most attention by Top 1.5 Technologists – so Billion far. Middle of the Pyramid Middle 3.5 Billion (MOP): Ready, BUT … Bottom 2 Billion Not Ready
  • 28. Resources  Physical: food, water, goods, …  Informational: Wikipedia, Doctors, …  Transportation  Employment  Spiritual  Timeliness  Efficiency
  • 29. Connecting Information People Aggregation Situation Alerts and Composition And Detection Queries Resources 8/17/2012 29
  • 30. Proprietary and Confidential, Not For 8/17/2012 Distribution 30
  • 31. Dynamic Event Situation Static Object Scene Atomic Composite
  • 32. Situation: An actionable abstraction of observed spatio-temporal characteristics  Allow users to define their own spatio- temporal features and create the situation detection filters. 8/17/2012 32
  • 33. Level 0: Raw data streams e.g. tweets, cameras, traffic, weather, … … Level 1: Unified representation Properties (STT Data) STT Stream Level 2: Aggregation Properties Emage (Emage) Level 3: Symbolic rep. Properties Situation (Situations)
  • 34. (a) Pollen levels (Source: Visual) (b) Census data (Source: text file) (c) Reports on ‘Hurricanes’ (source: Twitter stream) d) Cloud cover (Source: Satellite imagery) (e) Predicted hurricane path (source: KML) (f) Open shelters coverage(Source: KML) Representation for different data sources into a common spatio-temporal format.
  • 35. S. No Operator Input Output 1 Selection  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  Temporal E-mage Set Temporal E-mage Set 5 Characterization : •Spatial  •Temporal E-mage Set •Temporal Pixel Set •Temporal  •Temporal Pixel Set •Temporal Pixel Set 6 Pattern Matching  •Spatial  •Temporal E-mage Set •Temporal Pixel Set •Temporal  •Temporal Pixel Set •Temporal Pixel Set 35 8/17/2012 35
  • 36. Experimentation is Front End GUI essential to deal with New Data New Query E-mage Stream Alert Request evolving unstructured Source Back End Controller sensory data. E-mage Stream Personalized Registered Stream Query Processor Alert Unit Inspired by Queries E-mage Stream Photoshop. User Info Registered Data Data Ingestor Raw Data Storage Sources API Calls Raw Spatial Data Stream Data Cloud 8/17/2012 36
  • 37. Business decision making: Demand-supply analysis, opening a new store, offer,…  Medical : Epidemic monitoring, Asthma, pollution effect mitigation  Disaster relief: (hurricane, flood, fire) directing people to appropriate resources.  Traffic: Suggesting best routes  Election 8/17/2012 37
  • 38. Proprietary and Confidential, Not For 8/17/2012 Distribution 38
  • 39. Proprietary and Confidential, Not For 8/17/2012 Distribution 39
  • 40. Retail Store Locations Net Catchment area Proprietary and Confidential, Not For 8/17/2012 Distribution 40
  • 41. Proprietary and Confidential, Not For 8/17/2012 Distribution 41
  • 42. Planetary scale 1) Macro sensing situation Social sensors Device sensors + Macro sensors 2) Personalized Personal situation context Personal life streams + Profile/ Preferences e.g. High Flu risk 3) Recommend Actions Available resources + Resource data
  • 43. into ‘high’ and ‘low ’activity zones. Proprietary and Confidential, Not For 8/17/2012 Distribution 43
  • 44. 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 8/17/2012 action 44
  • 45. 8/17/2012 45
  • 46. Flood Shelter Classify (Flood level - Shelter) Twitter Flood Level Shelter 8/17/2012 46
  • 47. 8/17/2012 47
  • 48. Proprietary and Confidential, Not For 8/17/2012 Distribution 48
  • 49. 1. Alert me when major Allergy outbreak happens in my location ! 2. How healthy is today for me ? 3. What is the best location for me to undertake outdoor activities?
  • 50. ϵ {low, Allergy Threat Level mid, high}  US, 24 hrs, 1 X 1 lat long Air quality Pollen count Tweet reports Emage Emage Emage (air quality index) (pollen level) (number of reports) Δ Δ Δ Air quality S-t-t (#reports) US, Pollen count 24 hrs, Δ 1X1lat long US, 24 hrs, US, 1X1 lat long 24 hours, Weather.com Twitter 2X2 Pollen.com Twitter.com
  • 51. ϵ {low, Personal asthma threat mid, high}  Thresholds Low:{0, 0.3], Mid: {0.3, 0.7], High: {0.7,1} Heart rate Sneezing severity Asthma threat level ∏  ∏ ∏ Sensor stream EventShop Twitter Cardio device Twitter.com Pollen.com, AQI.com, Twitter
  • 53. Proprietary and Confidential, Not For 8/17/2012 Distribution 53
  • 54. Framework tested using applications:  Store location  Political campaign  Flu monitoring  EventShop system:  Operators implemented:  Selection , Arithmetic & Logical, Aggregation , Grouping, Characterization (spatial + temporal), Pattern Matching (spatial + temporal)  Applications tested:  Thai flood relief  Hurricane alerts  Safe locations for Asthmatic patients 8/17/2012 54
  • 55. Scalability  Data discovery  Application discovery  Conceptual modeling of situations  Richer operation set  User experience Proprietary and Confidential, Not For 8/17/2012 Distribution 55
  • 56. Make EventShop Robust  Develop system to deal with BIG DATA  Experiment with many applications Proprietary and Confidential, Not For 8/17/2012 Distribution 56
  • 57. Proprietary and Confidential, Not For 8/17/2012 Distribution 57