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Eventshop 120721

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Presentation at NIST on EventShop and its role in Social Life Networks.

Presentation at NIST on EventShop and its role in Social Life Networks.

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  • 1. Ramesh Jain with Several Collaborators8/17/2012 1
  • 2.  Scarcity: inadequate supply, Insufficiency of amount or supply  Abundance: an extremely plentiful or oversufficient quantity or supply Proprietary and Confidential, Not For8/17/2012 Distribution 2
  • 3. Scarcity Proprietary and Confidential, Not For8/17/2012 Distribution 3
  • 4. Abundance Proprietary and Confidential, Not For8/17/2012 Distribution 4
  • 5. Proprietary and Confidential, Not For8/17/2012 Distribution 5
  • 6. Proprietary and Confidential, Not For8/17/2012 Distribution 6
  • 7. Proprietary and Confidential, Not For8/17/2012 Distribution 7
  • 8. We are immersed in Networks of  People  Things  EventsIt 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 Expectations8/17/2012 9
  • 10. Astrology To Astronomical Volumes of Data8/17/2012 10
  • 11. Proprietary and Confidential, Not For8/17/2012 Distribution 11
  • 12. Have been reporting events as micro-blogsSensors 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 Planning8/17/2012 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. PeopleThingsPlacesTimeExperiencesEvents E by Westerman and Jain E* by Gupta and Jain
  • 19. Connecting People
  • 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
  • 24. Current Social Networks Important Unsatisfied Needs8/17/2012 26
  • 25. 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
  • 26.  Resources  Physical: food, water, goods, …  Informational: Wikipedia, Doctors, …  Transportation  Employment  Spiritual Timeliness Efficiency
  • 27. Connecting Information People Aggregation Situation Alerts and Composition And Detection Queries Resources8/17/2012 29
  • 28. Proprietary and Confidential, Not For8/17/2012 Distribution 30
  • 29. Dynamic Event Situation Static Object Scene Atomic Composite
  • 30.  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
  • 31. Level 0: Raw data streamse.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)
  • 32. (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.
  • 33. S. No Operator Input Output1 Selection  Temporal Temporal E-mage Set E-mage Set2 Arithmetic & K*Temporal E-mage Temporal E-mage Set Logical Set3 Aggregation α Temporal E-mage set Temporal E-mage Set4 Grouping  Temporal E-mage Set Temporal E-mage Set5 Characterization : •Spatial  •Temporal E-mage Set •Temporal Pixel Set •Temporal  •Temporal Pixel Set •Temporal Pixel Set6 Pattern Matching  •Spatial  •Temporal E-mage Set •Temporal Pixel Set •Temporal  •Temporal Pixel Set •Temporal Pixel Set 35 8/17/2012 35
  • 34. Experimentation is Front End GUIessential to deal with New Data New Query E-mage Stream Alert Requestevolving unstructured Source Back End Controllersensory data. E-mage Stream Personalized Registered Stream Query Processor Alert UnitInspired by Queries E-mage StreamPhotoshop. User Info Registered Data Data Ingestor Raw Data Storage Sources API Calls Raw Spatial Data Stream Data Cloud 8/17/2012 36
  • 35.  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  Election8/17/2012 37
  • 36. Proprietary and Confidential, Not For8/17/2012 Distribution 38
  • 37. Proprietary and Confidential, Not For8/17/2012 Distribution 39
  • 38. Retail Store Locations Net Catchment area Proprietary and Confidential, Not For8/17/2012 Distribution 40
  • 39. Proprietary and Confidential, Not For8/17/2012 Distribution 41
  • 40. Planetary scale 1) Macro sensing situationSocial sensorsDevice 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
  • 41. into ‘high’ and ‘low ’activity zones. Proprietary and Confidential, Not For8/17/2012 Distribution 43
  • 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 8/17/2012 action 44
  • 43. 8/17/2012 45
  • 44. Flood Shelter Classify (Flood level - Shelter) Twitter Flood Level Shelter8/17/2012 46
  • 45. 8/17/2012 47
  • 46. Proprietary and Confidential, Not For8/17/2012 Distribution 48
  • 47. 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?
  • 48. 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, Δ1X1lat long US, 24 hrs, US, 1X1 lat long 24 hours, Weather.com Twitter 2X2 Pollen.com Twitter.com
  • 49. 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 TwitterCardio device Twitter.com Pollen.com, AQI.com, Twitter
  • 50. Proprietary and Confidential, Not For8/17/2012 Distribution 53
  • 51.  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 patients8/17/2012 54
  • 52.  Scalability  Data discovery  Application discovery  Conceptual modeling of situations  Richer operation set  User experience Proprietary and Confidential, Not For8/17/2012 Distribution 55
  • 53.  Make EventShop Robust  Develop system to deal with BIG DATA  Experiment with many applications Proprietary and Confidential, Not For8/17/2012 Distribution 56
  • 54. Proprietary and Confidential, Not For8/17/2012 Distribution 57