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Designing intelligent social systems 121205

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Designing intelligent social systems 121205

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With emerging technologies and big data, it is now possible to design intelligent social systems. In this presentation, ideas related to designing such systems are presented

With emerging technologies and big data, it is now possible to design intelligent social systems. In this presentation, ideas related to designing such systems are presented

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Designing intelligent social systems 121205

  1. 1. 12/5/12   1  
  2. 2. •  Social  systems  rely  on  primi0ve  technology.   •  Big  Data  has  opened  Big  Opportuni0es.   •  Situa0on  recogni0on  is  a  key  technology.   •  EventShop  may  be  useful  in  designing   Intelligent  Social  Systems.  
  3. 3. Send:   •  Comments.   •  Sugges1ons.   •  Collabora1on  opportuni1es.     •  jain@ics.uci.edu   •  Gmail,  FB,  TwiBer:  jain49  
  4. 4. Intelligent:    displaying  or  characterized  by   quickness  of  understanding,  sound  thought,  or   good  judgment.     Social  Systems:  Social  systems  are  the  paBerns   of  behavior  of  a  group  of  people  possessing   similar  characteris1cs  due  to  their  existence  in   same  society.      
  5. 5. •  Introduc1on   •  Social  Systems   •  Intelligent  Social  Systems   •  Designing  Intelligent  Social  Systems   •  Situa1on  Recogni1on   •  Concept  recogni1ons   •  Personalized  Situa1ons   •  EventShop  
  6. 6. An  Interes0ng  Situa0on     When  we  were  data  poor  –   we  searched  for  words  in   documents.     Now  that  we  are  data  rich  –   should  we  s0ll  search  for   words?     Time  has  come  for  us  to  stop  thinking  data  poor;  really  start   thinking  and  behaving  data  rich.  
  7. 7. Variety   Volume   Big  Data  offers  Big  Opportuni4es.   But,  ….  ?????   7  
  8. 8. Most  aOen0on  by   Top  1.5   Technologists  –  so  far.   Billion   Middle    4  Billion     Middle  of  the   Pyramid  (MOP):     Ready.   BoOom  2  Billion   Not  Ready  
  9. 9. Data  is  Essen0al.       But,  we  are  really  interested  in  its  products:      Informa0on,      Knowledge,  and      Wisdom.   9    
  10. 10. Recognize   Objects   Situa0ons   Knowledge   Observe   Big  Data   Act   Planning   12/5/12   Control   10  
  11. 11. Past is EXPERIENCE Present is EXPERIMENT Future is EXPECTATION Use your Experiences In your Experiments To achieve your Expectations 12/5/12   11  
  12. 12.      Astrology   To   Astronomical   Volumes  of   Data     12/5/12   12  
  13. 13. We  are  immersed  in  Networks  of   •  People   •  Things   •  Events   It  is  now  possible  to  be  Pansophical.     12/5/12   13  
  14. 14. Our  mobile  wireless  infrastructure  can  be   “reality  mined”  to  understand  the  paOerns  of   human  behavior,  monitor  our  environments,   and  plan  social  development.       -­‐-­‐-­‐-­‐  Pentland  in    “Society’s  Nervous  System:   Building  Effec0ve  Government,  Energy,  and   Public  Health  Systems”   Proprietary  and  Confiden1al,  Not  For   12/5/12   14   Distribu1on  
  15. 15. •  Objects  -­‐-­‐  popular  in  the  West.   •  Rela0onships  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.   Geography  of  Thought  by  Richard  NisbeB  
  16. 16. •  Data     •  Objects     •  Rela0onships  and  Events  
  17. 17. •  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  seman1c  gap.  
  18. 18. Events:  Types  and  Granulari1es   •  Conferences   –  Days   •  Sessions   –  Talks   »  Purpose  of  the  talk   •  Wedding   •  An  Earthquake   •  The  Big  Bang   •  World  Wide  Web   •  Yahoo:  Winter  School  2012   •  Me   –  My  Birth,     –  Being  here,  and     –  Dying  in  100  years.  
  19. 19. People   Things   Places   Time   Experiences   Events   E    by  Westerman  and    Jain     E*  by  Gupta  and  Jain  
  20. 20. Sense  making  from  mul1modal   massive  geo-­‐social  data-­‐streams.     20  
  21. 21.   •  Introduc1on   • Social  Systems   •  Intelligent  Social  Systems   •  Designing  Intelligent  Systems   •  Situa1on  Recogni1on   •  Concept  recogni1ons   •  Personalized  Situa1ons   •  EventShop  
  22. 22. Poli0cs   Religion   Educa0on   Health   Economics  
  23. 23. Connec4ng  People  to  Resources     effec4vely,  efficiently,  and  promptly     in  given  situa4ons.  
  24. 24. •  Minimize  hunger  in  the  world.   •  Maximize  female  educa1on  in  India.   •  Minimize  ‘deaths’  in  the  coming  hurricane  in   Florida.   •  Minimize  work-­‐hours  lost  in  traffic  during   week  days  in  Bangalore.  
  25. 25. •  System:   –     A  set  of  diverse  parts  forming  a  whole.   –  Parts  are  put  together  with  a  common  objec1ve/ purpose.   •  Each  part  could  be  considered  a  system.   •  Each  part  plays  a  role  towards  the  system   objec1ve.   •  Designing  the  informa1on  flow  among  parts  is   essen1al  to  make  a  system  work  apprpriately.    
  26. 26. •  A  social  system  is  composed  of  persons  or   groups  who  share  a  common  objec1ve.   •  An  individual  objec1ve  is  usually  a  part  of  the   group’s  objec1ve.    
  27. 27. •  Persons   •  Families   •  Organiza1ons   •  Communi1es:  City,  State,  Country   •  Socie1es   •  Cultures  
  28. 28. •  Top  Down:   –  The  social  system  determines  its  parts.   –  People’s  behavior  determined  by  society.   •  BoBom  Up:   –  The  Society  is  the  sum  of  its  indivduals   –  Individual  ac1ons  determine  the  character  of  the   society.  
  29. 29. •  Each  social  en1ty  is  a  holon.   •  Holon:  Each  en1ty  is  simultaneously  a  part  and  a   whole.   •  A  social  component  is  made  up  of  parts  and  at  the   same  1me  maybe  part  of  some  larger  whole.   •  Any  system  is  by  defini1on  both  part  and  whole.  
  30. 30. •  The  primary  ‘currency’    of  a  social  system  is   informa1on.   •  System  behavior  can  be  understood  as  the   movement  of  informa1on:   –  Within  a  system   –  Between  the  system  and  its  environment   •  Informa1on  is  used  to  sense  as  well  as  to  control  or   act.  
  31. 31.   •  Introduc1on   •  Social  Systems   •  Intelligent  Social  Systems   •  Designing  Intelligent  Systems   •  Situa1on  Recogni1on   •  Concept  recogni1ons   •  Personalized  Situa1ons   •  EventShop  
  32. 32. •  Systems  that  perceive,  reason,  learn,  and  act   intelligently.   •  Adaptability  to  varying  environmental   situa1ons  is  a  key  element  of  intelligent   systems  
  33. 33. •  Social  systems  that  perceive,  reason,  learn,   and  act  intelligently.   •  What  does  ‘perceive’,  ‘reason’,  ‘learn’,  and   ‘act’  mean  in  the  context  of  social  systems?  
  34. 34.   •  Introduc1on   •  Social  Systems   •  Intelligent  Social  Systems   • Designing  Intelligent  Social   Systems   •  Situa1on  Recogni1on   •  Concept  recogni1ons   •  Personalized  Situa1ons   •  EventShop  
  35. 35. •  Desired  state  (Goal)   •  System  model  and  Control  Signal   (Ac0ons)   •  Current  State  (for  Feedback)  
  36. 36.   bserve d  State   O Fe edback Observa0ons   Control   Signals   Events   Real  World  
  37. 37. Social  Networks     Connecting People
  38. 38. Needs  and  Resources   Not  even   FaceBook!  
  39. 39. Current    Social   Networks   Important   Unsa1sfied     Needs   12/5/12   46  
  40. 40. •  Resources     –  Physical:  food,  water,  goods,  …   –  Informa:onal:  Wikipedia,  Doctors,  …   –  Transporta:on   –  Employment   –  Spiritual   •  Timeliness   •  Efficiency  
  41. 41. Connecting Information     People Aggregation Situation Alerts and Detection CompositionAnd Queries Resources 12/5/12   48  
  42. 42.   •  Introduc1on   •  Social  Systems   •  Intelligent  Social  Systems   •  Designing  Intelligent  Systems   • Situa0on  Recogni0on   •  Concept  recogni1ons   •  Personalized  Situa1ons   •  EventShop  
  43. 43. Connec4ng  People  to  Resources     effec4vely,  efficiently,  and  promptly     in  given  situa4ons.  
  44. 44. •  rela1ve  posi1on  or  combina1on  of   circumstances  at  a  certain  moment.   •  The  combina1on  of  circumstances  at  a  given   moment;  a  state  of  affairs.  
  45. 45. •  Situa1on  awareness,  or  SA,  is  the  percep1on   of  environmental  elements  within  a  volume  of   1me  and  space,  the  comprehension  of  their   meaning,  and  the  projec1on  of  their  status  in   the  near  future.   •  What  is  happening  around  you  to  understand   how  informa1on,  events,  and  your  own   ac1ons  will  impact  your  goals  and  objec1ves,   both  now  and  in  the  near  future.  
  46. 46. •  Example  1:     –  A  person  shou1ng.   –  1000  people  shou1ng.   •  In  a  contained  building   •  In  main  parts  of  a  city   •  Example  2:   –  One  person  complaining  about  flu.   –  Many  people  from  different  areas  of  a  country   complaining  about  flu.  
  47. 47. Facebook  and  TwiBer   (now  GOOGLE  +)   Have  been  repor0ng  events  as  micro-­‐blogs   Massive  collec1on  of  events.  
  48. 48. Time  
  49. 49. Does  the  flap  of  a  buEerfly’s  wings  in  Brazil  set  off  a   tornado  in  Texas?        
  50. 50. Have  been  repor0ng  events  as  micro-­‐blogs   Sensors  and  Internet  of  Things  are  crea1ng  and   repor1ng    even  more  events  than  humans   are.   12/5/12   57  
  51. 51. FROM TWEETS TO REVOLUTIONS
  52. 52. Atomic  and  Composite  Events   Time  
  53. 53. •  Given  a  plethora  of  event  data.  How  can  we:   –  Disambiguate  relevant  and  irrelevant  events?   –  Combine  events  into  meaningful  representa1ons  ?   –  Allow  inference  and  cascading  effects?   –  Support  different  interpreta1ons  based  on   applica1on  domain?   –  Support  Control  &  decision  making?    
  54. 54. 1.  Inherent  support  for  event-­‐based  (temporal)   reasoning   2.  The  ability  of  the  controller  to  reason  based   on  symbols  (rather  than  just  signals)   3.  Explicit  inclusion  of  domain  seman1cs  (to   support  mul1ple  applica1ons)  
  55. 55. An  ac4onable  abstrac4on  of  observed   spa4o-­‐temporal  characteris0cs.   62  
  56. 56.   •  Introduc1on   •  Social  Systems   •  Intelligent  Social  Systems   •  Designing  Intelligent  Systems   •  Situa1on  Recogni1on   • Concept  recogni0ons   •  Personalized  Situa1ons   •  EventShop  
  57. 57. Data  Type   1950   2000   Time  Line  
  58. 58. Situa0on     2010   Events     Data  Type   1986   Objects     1963   Speech   1962   Character   1959   1950   2000   Time  Line  
  59. 59. Loca1on   Scenes   Environm Trajectories   Situa1ons   aware   ents   Single  Media   Loca1on   Visual   Real  world   Visual   Objects   Objects   Ac1vi1es   Events   unaware   Sta1c   Dynamic   SPACE   TIME   Data  =  Text  or  Images  or  Video   66  
  60. 60. Trajectory   Situa1on   Object   Scene   Event   1960   1970   1980   1990   2000   2010   •  1963:  Object  Recogni1on  [Lawrence  +  Roberts]   •  1967:  Scene  Analysis  [Guzman]   •  1984:  Trajectory  detec1on  [Ed  Chang+  Kurz]   •  1986:  Event  Recogni1on  [Haynes  +  Jain]   •  1988:  Situa1on  Recogni1on  [Dickmanns]  
  61. 61. Heterogeneous  Media   Loca1on   Environm Situa1ons   aware   ents   Loca1on   Real  world   Objects   Ac1vi1es   unaware   Sta1c   Dynamic   SPACE   TIME   Data  is  just  Data.   Meta-­‐data  is  also  data.    Caste  system  does  not  exist  here.   Medium  and  sources  do  not  maOer.   68  
  62. 62.   •  Introduc1on   •  Social  Systems   •  Real  Time  Social  Systems   •  Designing  Real  Time  Systems   •  Situa1on  Recogni1on   •  Concept  recogni1ons   • Personalized  Situa0ons   •  EventShop  
  63. 63. A)  Situa0on  Modeling   B)  Situa0on  Recogni0on   C)  Visualiza0on,  Personaliza0on, and  Alerts   i)  Visualiza1on   C1   …   ⊕ v2   v3   Personal   context   ii)  Personaliza1on   Personali v5   v6   zed   STT  Stream   situa1on   Available   resources   Emage   iii)  Alerts   Situa1on   70        
  64. 64. Proprietary  and  Confiden1al,  Not  For   12/5/12   71   Distribu1on  
  65. 65. Aggrega1on,     Opera1ons   Alert  level     =  High   Date:  3rd  Jun,  2011   STT  data   Situa1on  Detec1on       User-­‐Feedback   1)      Classifica1on     Tweet:     ‘Please  visit  Dr.  Cureit  at   ‘Urrgh…  sinus’     2)      Control  ac1on   4th  St  immediately’       Loc:  NYC,   Date:  3rd  Jun,  2011   Theme:  Allergy   73  
  66. 66.   •  Introduc1on   •  Social  Systems   •  Intelligent  Social  Systems   •  Designing  Intelligent  Systems   •  Situa1on  Recogni1on   •  Concept  recogni1ons   •  Personalized  Situa1ons   • EventShop  
  67. 67. •  E-­‐mage             –  Visualiza1on   –  Intui1ve  query  and  mental  model   –  Common  spa1o  temporal  data  representa1on   –  Data  analysis  using  media  processing  operators     (e.g.  segmenta1on,  background  subtrac1on,   convolu1on)   76        
  68. 68. •  Spa1o-­‐temporal  element   –  STTPoint  =  {s-­‐t-­‐coord,  theme,  value,  pointer}   •  E-­‐mage   –  g  =  (x,  {(tm,  v(x))}|xϵ X  =  R2  ,  tm ϵ  θ,  and  v(x)  ϵ  V  =  N)   •  Temporal  E-­‐mage  Stream   –  TES=((ti,  gi),  ...,  (tk,  gk))   •  Temporal  Pixel  Stream   –  TPS  =  ((ti,  pi),  ...,  (tk,  pk))   77  
  69. 69. Proprietary  and  Confiden1al,  Not  For   12/5/12   78   Distribu1on  
  70. 70. Proprietary  and  Confiden1al,  Not  For   12/5/12   79   Distribu1on  
  71. 71. Retail  Store   Loca0ons   Net  Catchment   area   Proprietary  and  Confiden1al,  Not  For   12/5/12   80   Distribu1on  
  72. 72. •  Humans  as  sensors   •  Space  +  Time  as  fundamental  axes     •  Real  0me  situa0on  evalua0on  (E-­‐mage  Streams)   (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)   81  
  73. 73. •  Help  domain  experts  externalize  their  internal   models  of  situa1ons  of  interest  e.g.  epidemic.   •  Building  blocks:     –  Operators     –  Operands     •  Wizard:     –  A  prescrip1ve  approach  for  modeling  situa1ons  using  the   operators  and  operands     Singh,  Gao,  Jain:  Situa:on  recogni:on:  An  evolving  problem  for  heterogeneous  dynamic  big   82   mul:media  data,  ACM  Mul0media  ‘12.  
  74. 74.  Knowledge  or  data  driven  building  blocks   Growth  rate     (Flu  reports)   Feature   TwiBer-­‐Flu   Data  source   -­‐Emage  (#Reports)   Representa1on  level   Thresholds   (0,  50)   Meta-­‐data   83  
  75. 75. Get_components (v){ v ϵ  {  Low,   Mid,  High}   f1   ⊕   1)  Identify output state space <USA,    5  mins,   0.01x  0.01>     2)  Identify S-T bounds v2   v3   3)  Define component f2   v4   @ ⊕ ∏ features: v=f(v1, …, vk) Emage   v5   v6   Emage   •  If (type = imprecise) ∏ @ Δ Δ –  identify learning data source, method     4)  ForEach (feature vi) { D1   Emage   Emage   D2        If (atomic) Δ Δ •  Identify Data source. D2   D3   •  Type, URL, ST bounds •  Identify highest Rep. level reqd. •  Identify operations Else Get_components(vi) 84  
  76. 76. 3)  Instan1ate   2)  Revise   1) Model     Epidemic   ϵ  {Low,  mid,  high},   Outbreaks     <USA,  5  mins,  0.01x   Classifica1on:   0.01>   Thresh  (30,70)   Growing  Unusual   ac1vity   Mul1ply   Unusual   Ac1vity?   Growth  Rate   Subtract   Historical   Current  ac1vity   Emage     ac1vity  level   level   (#reports  ILI)   Subtract   Normalize   Emage  (Historical   [0,100]   avg)   Emage  Emage     TwiBer-­‐Flu     Emage   (#reports  ILI)   (#reports  ILI)   (popula1on)   TwiBer.com   <USA,  5  mins,     TwiBer-­‐Avg   0.01x  0.01>   DB,     TwiBer.com   TwiBer-­‐Flu   CSV-­‐   Census.gov,     TwiBer.com   TwiBer-­‐Flu   <USA,  5  mins,     <USA,  5  mins,     Popula1on   <USA,  5  mins,     <USA,  5  mins,     0.01x  0.01>   0.01x  0.01>   0.01x  0.01>   85         0.01x  0.01>  
  77. 77. Level  0:  Raw  data  streams     e.g.  tweets,  cameras,  traffic,  weather,  …   …   Level  1:  Unified   representa1on   Proper1es (STT  Data)   STT  Stream   Level  2:   Aggrega1on   Proper1es Emage   (Emage)     Opera1ons   Level  3:   Symbolic  rep.   Proper1es Situa1on   (Situa1ons)   86  
  78. 78. Suppor1ng   Operator  Type   Data   parameter(s)   Output   Filter   + Mask   Aggregate   + Classifica1on   Classifica1on   method   Characteriza1on   Property     Growth  Rate   required   =  125%   PaBern  Matching   + 72%   PaBern   87        
  79. 79. Suppor1ng   Operator  Type   Data   parameter(s)   Output   1)  Data  into  right   representa1on   Transform   …   Spa1o-­‐temporal   window   Filter   + Mask   Aggregate   + 2)  Analyze  data  to   Classifica1on   derive  features     Classifica1on   method   Characteriza1on   Property     Growth  Rate   required   =  125%   PaBern  Matching   + PaBern 72%     {Features}   3)  Use  features  to   Learn   f   Learning     f   evaluate  situa1ons   {Situa1on}   method   88        
  80. 80. S.  No Operator Input Output 1 Filter  ∏ Temporal  E-­‐mage  Stream Temporal    E-­‐mage  Stream 2 Aggrega0on  ⊕ K*Temporal  E-­‐mage  Stream Temporal  E-­‐mage  Stream 3 Classifica0on  γ Temporal  E-­‐mage  Stream Temporal  E-­‐mage  Stream 4 Characteriza0on  :  @           •  Spa0al     •  Temporal  E-­‐mage  Stream   •  Temporal  Pixel  Stream   •  Temporal   •  Temporal  Pixel  Stream •  Temporal  Pixel  Stream 5 PaOern  Matching  ψ           •  Spa0al     •  Temporal  E-­‐mage  Stream   •  Temporal  Pixel  Stream   •  Temporal   •  Temporal  Pixel  Stream •  Temporal  Pixel  Stream Singh, Gao, Jain: Social Pixels: Genesis and Evaluation, ACM Multimedia 89   ‘10.  
  81. 81. •  Select  E-­‐mages  of  US  for  theme  ‘Obama’.   –  ∏spa1al(region=[24,-­‐125],[24,-­‐65])   (TEStheme=Obama)   •  Iden1fy  3  clusters  for  each  E-­‐mage  above.   –  γkmeans(3)  (∏spa1al(region=[24,-­‐125],[24,-­‐65])(TEStheme=Obama))   •  Show  me  the  speed  for  each  cluster  of  ‘Katrina’  e-­‐ mages   ( –  @speed @epicenter (γkmeans(n=3)  (∏spa1al(region=[24,-­‐125],[24,-­‐65])  (TEStheme=Katrina))))   •  How  similar  is  paBern  above  to  ‘exponen1al  increase’?   –  ψexp-­‐increase(@speed(@epicenter(γkmeans(n=3)  (∏spa1al(region=[24,-­‐125],[24,-­‐65])   (TEStheme=Katrina))))   90  
  82. 82. Personalized  situa0on:  An  ac4onable  integra4on  of   a  user's  personal  context  with  surrounding   spa4otemporal  situa4on.   1)  Macro   situa0on Macro     Personal   2)  Personalized   data-­‐sources   Context   situa0on Profile  +   Preferences     3)  Personalized   alerts   User     Available   data   resources   Resource   data   IF  person  ui  <is-­‐in>  (PSj)  THEN  <connect-­‐to>  rk       91  
  83. 83. Personalized  Situa1on  Recogni1on:   Operators   Suppor1ng   Operator  Type   Data   parameter(s)   Output   Filter   …   + User  loca1on   Aggregate   …   + …   …   Classifica1on   Classifica1on   …   method …     Characteriza1on   …   Property     Growth  Rate   required   =  125%   PaBern  Matching   …   + Match=  42%   PaBern   92        
  84. 84. •  IF   𝑢𝑖   𝑖𝑠𝑖𝑛   𝑧𝑗     𝑇𝐻𝐸𝑁   𝑐𝑜𝑛𝑛𝑒𝑐𝑡  ( 𝑢𝑖,   𝑛𝑒𝑎𝑟𝑒𝑠𝑡(𝑢𝑖,   𝑟𝑘))     1)  𝑖 𝑠𝑖𝑛(𝑢𝑖,   𝑧𝑗)  → 𝑚𝑎𝑡𝑐ℎ( 𝑢𝑖,   𝑟𝑘))                         𝑓:( 𝑈× 𝑍)→( 𝑈× 𝑅)   •  U  =  Users     •  Z  =  Personalized  Situa1ons   •  R  =  Resources   2)  𝑛 𝑒𝑎𝑟𝑒𝑠𝑡(𝑢𝑖,   𝑟𝑘)= 𝑎𝑟𝑔𝑚𝑖𝑛  ( 𝑢𝑖. 𝑙𝑜𝑐,     𝑟𝑘. 𝑙𝑜𝑐𝑠)   93  
  85. 85. Billions  of  data  sources.     Selec0ng  and  combining  appropriate  sources  to   detect  situa0ons.     Interac0ons  with  different  types  of  Users    Decision  Makers                        Individuals     12/5/12   94  
  86. 86. 12/5/12   95  
  87. 87. Front  End  GUI New New E-­‐mage Alert Data Query Stream Request Source Back  End  Controller E-­‐mage  Stream Personalized   Registered Stream  Query  Processor Queries Alert  Unit E-­‐mage  Stream User  Info Registered Data Data  Ingestor Raw  Data Storage Sources API  Calls Raw  Spatial   Data  Stream Data  Cloud 12/5/12   96  
  88. 88. 11/28/2012   97  
  89. 89. Emage -­‐theme  :  String STTPoint -­‐start  :  Date -­‐theme  :  String -­‐end  :  Date -­‐value  :  double -­‐latUnit  :  double Iterator -­‐start  :  Date Iterator -­‐longUnit  :  double Iterator -­‐end  :  Date -­‐swLat  :  double -­‐latUnit  :  double -­‐swLong  :  double +hasNext()  :  bool -­‐longUnit  :  double +hasNext()  :  bool -­‐neLat  :  double +hasNext()  :  bool +next()  :  <unspecified> -­‐latitude  :  double +next()  :  <unspecified> -­‐neLong  :  double +next()  :  <unspecified> -­‐longitude  :  double -­‐image FrameParameters -­‐timeWindow  :  long 1..* 1..* -­‐syncTime  :  long -­‐latUnit  :  double -­‐Parameterize STTPointIterator EmageIterator STMerger -­‐longUnit  :  double -­‐swLat  :  double +hasNext()  :  bool 1 1 +hasNext()  :  bool 1..* 1 +hasNext()  :  bool -­‐swLong  :  double 1 1 +next()  :  STTPoint +next()  :  Emage +next()  :  Emage -­‐neLat  :  double -­‐neLong  :  double 1 1 Wrapper DBSTTPointIterator VisualImageIterator ResolutionMapper -­‐initResolution +setURL() +setURL() -­‐finalResolution +setTheme() +setTheme() +setParas() +setParas() TwitterWrapper FlickrWrapper CSVWrapper KMLWrapper +setBagOfWords() +setBagOfWords() 11/28/2012   +setColors() 98  
  90. 90. Macro  situa0on   Alert  Level=High   Date=12/09/10   Micro  event   Situa0onal  controller   Control  Ac0on   e.g.  “Arrgggh,  I     “Please  visit   have  a  sore  throat”   • Goal     nearest  CDC   (Loc=New  York,   • Macro  Situa1on     center  at  4th  St   Date=12/09/10)   • Rules   immediately”   Level  1  personal  threat  +  Level  3  Macro  threat  -­‐>  Immediate  ac0on     12/5/12   99  
  91. 91. •  What  personal  informa1on  can  be  shared?   •  How  should  it  be  shared  to  benefit  the  user?   •  Developing  an  architecture  for  personal   informa1on  management.  
  92. 92. Macro  situa1on  model   Asthma  Threat   c  ϵ  {Low,  mid,  high},   level   [USA,    6  hrs,   0.1x  0.1]       Air  Quality   Pollen  Count   Allergy  reports   Emage     Emage  (Pollen   Emage  (Number  of   (AQI.)   Level)   reports)   Visual-­‐   Visual-­‐   TwiOer-­‐Allergy   Air  quality   Pollen  level   Weather.com,   TwiOer  API,   [USA,    6  hrs,   Pollen.com,   [USA,    6  hrs,   [USA,    6  hrs,   0.1x  0.1]   0.1x  0.1]   0.1x  0.1]   102  
  93. 93. Personal   c  ϵ  {Low,  mid,   threat  level   high}   Classifica0on:   Thresh(30,70)   And   Physical  exer0on   Asthma  threat   level   Normalize   Normalize  (0,   (0,  100)   100)   TPS  (Asthma)   TPS  (Funf)   UserLoc   Funf-­‐ac0vity   EventShop   [USA,    6  hrs,   Phone  sensors,     0.1x  0.1]   (relaxMinder  app),   [USA,    6  hrs,   0.1x  0.1]   103    /  
  94. 94. Personal   threat  level   c  ϵ  {Low,  mid,   high}   Classifica0on:   Thresh(30,70)   And   Physical   Asthma   exer0on   threat  level   Normalize   Normalize   (0,  100)   (0,  100)   TPS   TPS  (Funf)   (Asthma)   UserLoc   Funf-­‐ac0vity   EventShop     [USA,    6  hrs,   Phone  sensors,     0.1x  0.1]   (relaxMinder  app),   [USA,    6  hrs,   0.1x  0.1]   104        
  95. 95. Classify (Flood level - Shelter Flood Shelter) Twitter Flood Level Shelter 12/5/12   107  
  96. 96. 12/5/12   108  
  97. 97. Proprietary  and  Confiden1al,  Not  For   12/5/12   109   Distribu1on  
  98. 98. Outline   •  Introduc1on   •  Social  Systems   •  Real  Time  Social  Systems   •  Designing  Real  Time  Systems   •  Situa1on  Recogni1on   •  Concept  recogni1ons   •  Personalized  Situa1ons   •  EventShop   • Going  Forward  
  99. 99. •  Social  observa1ons  are  now  possible  with  liBle   latency.   •  Now  possible  to  design  social  systems  with   feedback.   •  Situa1on  Recogni1on  and  Need-­‐Availability   iden1fica1on  of  resources  becomes  a  major   challenge.   •   EventShop  is  a  step  in  the  direc1on  of   implemen1ng  Social  Life  Networks.  
  100. 100. Useful  Links   •  Demo:   –  hBp://auge.ics.uci.edu/eventshop   •  Data  Defini1on  Language  Schema   –  hBp://auge.ics.uci.edu/eventshop/documents/ EventShop_DDL_Schema   •  Query  Language  Schema   –  hBp://auge.ics.uci.edu/eventshop/documents/ EventShop_QL_Schema   •  Example  Query  in  JSON   –  hBp://auge.ics.uci.edu/eventshop/documents/ EventShop_Example_Query   11/28/2012   112  
  101. 101. Thanks  for  your  1me  and  aBen1on.   For  ques1ons:  jain@ics.uci.edu  

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