Building Social Life Networks 130818


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This presentation discusses what Social Life Networks are, their current status, and challenges in building them.

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Building Social Life Networks 130818

  1. 1. 8/21/2013 1
  2. 2. • Why am I excited about things? • What are We doing? • What are the challenges?
  3. 3. What is your research interest? • Computer Vision • Multimedia • Machine Learning • Social Media • Intelligent Systems • Big Data • Stream Processing Building a Better Society.
  4. 4. Events and Entities Exist in the real world. Events and entities result in Data and Documents
  5. 5. What is the most Fundamental Problem in Society? Connecting People to Resources Effectively, Efficiently, and Promptly in given Situations. Hint: Economics, Health Care, Politics, Computer Science, Operations Research, …
  6. 6. Maslow: Basic Needs
  7. 7. Maslow: Needs and Resources • Financial • Natural (Food) • Human Skills • Health • Rescue • Transportation • Education • Production • …
  8. 8. Social Life Networks Connecting People to Resources
  9. 9. Modern Data
  10. 10. 8/21/2013 10 Knowledge Data Processing Information/ Decision
  11. 11. Decisions Food Friend What??? Information Data Knowledge Observations
  12. 12. • Desired state (Goal) • System model and Control Signal (Actions) • Current State (for Feedback)
  13. 13. Key Steps 1. Identify Situation 2. Determine Needs 3. Determine Resources 4. Develop best resource management approach 5. Communicate/Actuate decisions 6. Go to 1.
  14. 14. Social Networks Connecting People
  15. 15. Current Social Networks Important Unsatisfied Needs 8/21/2013 17
  16. 16. Motivation Location Based Mobile Applications Ongoing Archived Database System satellite Environmental Sensor Devices Internet of Things Social Media Social Life Network Experts People Governmental Agencies Situations
  17. 17. Social Life Network Aggregation and Composition Situation Detection Alerts Queries Information
  18. 18. Fundamental Problem Connecting People to Resources effectively, efficiently, and promptly in given situations.
  19. 19. • Social observations are now possible with little latency. • We can design social systems with feedback. • Situation Recognition and Need-Availability identification of resources is a major challenge.
  20. 20. Smart Social Systems Architecture Situation Recognition Evolving Situations Available Resources Identified Needs Need- Resource Matcher Communicati on/Control Unit Resources People D a t a S o u r c e s Database System satellite Environmental Sensor Devices Social Media
  21. 21. Concept Recognition: Last Century 23 Environm ents Real world Objects Situations Activities SingleMedia SPACE TIME ScenesLocation aware Visual Objects Trajectories Visual Events Location unaware Static Dynamic Location aware Location unaware Static Dynamic Data = Text or Images or Video
  22. 22. Visual Concept Recognition: Quick History • 1963: Object Recognition [Lawrence + Roberts] • 1967: Scene Analysis [Guzman] • 1984: Trajectory detection [Ed Chang+ Kurz] • 1986: Event Recognition [Haynes + Jain] • 1988: Situation Recognition [Dickmanns] 1960 1970 1980 1990 2000 2010 Object Scene Trajectory Event Situation
  23. 23. Concept Recognition: This Century 25 Environm ents Real world Objects Situations Activities SPACE TIME Location aware Location unaware Static Dynamic HeterogeneousMedia Location aware Location unaware Static Dynamic Data is just Data. Medium and sources do not matter.
  24. 24. • relative position or combination of circumstances at a certain moment. • The combination of circumstances at a given moment; a state of affairs.
  25. 25. Situations: Definition An actionable abstraction of observed spatio-temporal characteristics. 27
  26. 26. 8/21/2013 28
  27. 27. • Example 1: – A person shouting. – 1000 people shouting. • 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.
  28. 28. Micro-events: Sensors detecting and chirping (broadcasting) events • 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.
  29. 29. Example: Cameras in a city • ‘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
  30. 30. From micro events to situations Thermodynamics provides a framework for relating the microscopic properties of individual atoms and molecules to the macroscopic or bulk properties of materials that can be observed in everyday life.
  31. 31. Data Types in Situation Recognition • Static Data: – POIs (location of hospitals) – Population – Technical data of hospitals – Contact persons in committees or health organization, and – All information on support-potentials for personnel, material and infrastructure • Dynamic Data: – Current disease data – Twitter/Google Trends – Environmental data – Personal Individual data
  32. 32. Two Big Challenges • Data Ingestion to efficiently extract data from the Web and make them available for later computation is not-trivial. • Stream Processing Engine to bridge the semantic gap between high level concept of situations and low level data streams.
  33. 33. S Social Networks 2-D spatial Grid at time T Database Systems Global Sensors Phone Apps Internet of Things (S, T, T) S Uses Application semantics to combine different data items.
  34. 34. Observed State(Situations) Intelligent Social Systems: Spatial Perspective Real World Events Observations Control Signals Goal
  35. 35. Observed State(Situations) Intelligent Social Systems: People Perspective Real World Events Observations Control Signals Goal
  36. 36. • Spatio-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)) 38
  37. 37. 8/21/2013 39
  38. 38. 8/21/2013 40
  39. 39. 8/21/2013 41 Retail Store Locations Net Catchment area
  40. 40. Level 1: Unified representation (STT Data) Level 3: Symbolic rep. (Situations) Properties Properties Properties Level 0: Raw data streams e.g. tweets, cameras, traffic, weather, … Level 2: Aggregation (Emage) … STT Stream Emage Situation 42 Operations
  41. 41. 43  Pattern Matching Aggregate  @ Characterization ∏ Filter  Classification 72% + + Growth Rate = 125% Data Supporting parameter(s) OutputOperator Type + Classification method Property required Pattern Mask
  42. 42. • IF 𝑢𝑖 𝑖𝑠𝑖𝑛 𝑧𝑗 𝑇𝐻𝐸𝑁 𝑐𝑜𝑛𝑛𝑒𝑐𝑡 (𝑢𝑖, 𝑛𝑒𝑎𝑟𝑒𝑠𝑡 𝑢𝑖, 𝑟𝑘 ) 1) 𝑖𝑠𝑖𝑛 𝑢𝑖, 𝑧𝑗 → 𝑚𝑎𝑡𝑐ℎ(𝑢𝑖, 𝑟𝑘)) 𝑓: (𝑈 × 𝑍) → (𝑈 × 𝑅) • U = Users • Z = Personalized Situations • R = Resources 2) 𝑛𝑒𝑎𝑟𝑒𝑠𝑡 𝑢𝑖, 𝑟 𝑘 = 𝑎𝑟𝑔𝑚𝑖𝑛 (𝑢𝑖. 𝑙𝑜𝑐, 𝑟 𝑘. 𝑙𝑜𝑐𝑠) 44
  43. 43. 8/21/2013 45 Billions of data sources. Environment for Selecting, and Combining appropriate sources to detect situations. Prediction for Pro-active actions Interactions with different types of Users Decision Makers Individuals
  44. 44. OutputIngestor EventSource Parser Data Adapter Emage Generator (+resolution mapper) Processing EvShop Internal Storage Query Parser Query Rewriter Event Stream Processing Executor ᴨ ᴨ Action Parser Register EventSource Register Continuous Query Situation Emage Visualization (Dashboard) Actuator Communication Action Control Event Property & Other Information (e.g., spatio-temporal pattern) µ Data Access Manager Live Stream Archived Stream Situation Stream Real-Time DataSource (e.g., sensor streams, geo-image streams) Near Real-Time DataSource (e.g., preprocessing data streams, social media streams) Raw Event
  45. 45. Input Manager External Event Preprocessing (Collaboration) 47 Real-Time Sensor Streams e.g., Cloud Satellite Images Real-Time Sensor Streams e.g., Wind Speed, Traffic Flow RealTime DataSource 1D Data Wrapper STT to Emage 2D Data Wrapper Data Adapter Emage Generator Emage Emage Factory STT Emage Raw Social Media Streams e.g., Twitter, News RSS Feed NearRealTime DataSource Event Model Wrapper STT to Emage Data Adapter Emage Generator Emage Emage Factory STT Topic Event Detection Abnormal Event Detection Raw Sensor Streams e.g., PM2.5 data “EventModel” Streams e.g., suddenly change of data trend within time window Emage Store STT Store Metadata Store EventSource Parser Interface (Optional) RealTime Emage Streams NearRealTime Emage Streams Processing Manager ES Descriptor ES Control (Start/Stop/ View ES) Users Input Automatic Data/Events Flow InitialResolution AggregationFunc Metadata Theme AdapterType SourceURL TimeWindow Parameters
  46. 46. 48 Stream Processing Engine Operators Manager Built-in Operators User-Defined Operators ᴨ ᴨ µ Data Access ᴨ ᴨ µ Data Access ᴨ ᴨ µ Data Access Input Manager (Accessing External Data) Event Stream Executor Operators Nodes Internal Storage (Accessing Internal Data) AsterixDB, SciDB, MongoDB Emage Store STT Store Metadata Store Query Parser Interface Query Descriptor Query Control (Start/Stop/ View) Real-time/ near real-time Emage Streams Archived Emage Streams Situation Streams Emage Interpolation Function Emage Conversion Final Resolution Parameter Operators Operators Store Parameters Retrieve Parameters Query Rewriter Execution Plan
  47. 47. 8/21/2013 49
  48. 48. 8/21/2013 51 Flood level - Shelter Flood Level Shelter Twitter Classify (Flood level - Shelter)
  49. 49. 8/21/2013 52
  50. 50. Personalized Alerts Disaster Situation Assimilation and Control Environmental Resources and Historic Data Governmental Agencies Internet of Things Social Sources Experts Users All Users are not EQUAL.
  51. 51. What defines a person?
  52. 52. Activities Communications Media Persona: Turning Disassociated Data into Meaningful Information AGGREGATED PERSONAL ANALYTICS Sensors 55 Persona
  53. 53. Personal EventShop: Micro Situation Detection
  54. 54. HEALTH PERSONA Logical Sensor Physiological SensorsFitness Tracking Sensors Life Event Kinetic Event Physiological Event Food Event Personicle Health Persona Framework
  55. 55. Life Events Ontology
  56. 56. Meeting with development group Yoga with Jena Meeting with test group Mina’s birthday party 9:30-11:00 12 - 1 4:00- 5:00 Transportation walking walking Transportation Transportation Transportation Home HomeWork Caspian HavingBreakfast Doingthedishes Commuting Commuting Walking Walking Meeting Meeting working Working Exercise Exercise Working Working Working Working Meeting Meeting Walking Commuting Commuting Commuting Shopping Shopping Commuting Partying Partying Partying Partying Partying Commuting Commuting Commuting Sleeping Sleeping WatchingTV Cleaning Personicle Commute Walk Meet work Exer. work Meet Commute shop party Commute Sleep GPSlocationtracking Calendar Activity levelPersonicle
  57. 57. SLN Architecture EventShop Personal EventShop Evolving Situations Available Resources Identified Needs Need Resource Matcher Communicati on/Control Unit Resources People D a t a S o u r c e s Database System satellite Environmental Sensor Devices Social Media
  58. 58. Research Challenges • Situation Recognition • Persona and Personal Context • Chronicle Analytics and Visualization • Massive Geo-Spatial Heterogeneous Stream Processing • Dynamic Need-Resource Optimization
  59. 59. Situation Recognition • Next Frontier in Concept Recognition • Heterogeneous Geo-spatial Dynamic Data • Social data and IoT become a key element • Application and domain semantics • Model definitions • High dimensionality • Unification of data: Social-Cyber-Physical
  60. 60. Persona and Personal Context • Not only Logs of Keyboard and Surfing. • You Log and explore every thing. – Entity resolution on TURBO • Many new data processing and unification challenges.
  61. 61. Single Tri-axial Accelerometer Example: Activity Recognition Multiple Accelerometers Activity Classification Features: mean, variance, standard deviation, energy, entropy Correlation between axis, discrete FFT coefficients Activities: running, walking, lying, sitting, ascending stairs, descending stairs, cycling Location Calendar History ….. Context Activities: running, walking, lying, sitting, ascending stairs, descending stairs, cycling, shopping, commuting, doing housework, office work, lunch routine.
  62. 62. MicroBlogs and Twitter: LIMITATIONS • Very LOW Signal-to-Noise ratio: High Noise-to-Signal ratio • Focus on being broad platform. • Difficult to extract SIGNAL. • Information must be extracted from limited text.
  63. 63. Solution: Tweeting Applications • Develop focused Apps: Focused MicroBlogs • Get all information from ‘motivated’ and collaborative users. • Help them solve their problem.
  64. 64. WAZE: Outsmarting Traffic, Together
  65. 65. Chronicle Analytics • Enterprise Warehouse were for late 20th Century – Planetary Warehouses are defining this century. • Big data is important because it collects everything that happens to build ‘Prediction Machines’. • Machine learning and visualization are the key tools.
  66. 66. Massive Geo-Spatial Heterogeneous Stream Processing • Why does the DATA become so BIG? • And it will keep getting BIGGER. • We have to go beyond Batch Processing as primary computing approach. • Should be of great interest to Social Media researchers.
  67. 67. Dynamic Need-Resource Optimization • What are the fundamental problems in Computer Science? – Time and memory compexity – Operating systems, networks, storage management, algorithms, … • What is the main concern in – Economics? – Healthcare? – Politics? – …
  68. 68. Live EventShop and Collaboration • Live EventShop Demo – • Current Collaborators & Plan – Cyber-Physical Cloud Computing Project • NICT, NIST – SLN4MOP Project • Sri Lanka Farmers; Prof. Ginige in Sydney leading – Open Source EventShop by mid-year of 2013 • HCL
  69. 69. Thanks for your time and attention. For questions: