building social life networks 130818
DESCRIPTION
This presentation discusses what Social Life Networks are, their current status, and challenges in building them.TRANSCRIPT
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• Why am I excited about things?
• What are We doing?
• What are the challenges?
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What is your research interest?
• Computer Vision
• Multimedia
• Machine Learning
• Social Media
• Intelligent Systems
• Big Data
• Stream Processing
Building a Better Society.
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Events and Entities Exist in the real world.
Events and entities result in Data and Documents
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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, …
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Maslow: Basic Needs
http://www.theappgap.com/collaboration-whats-in-it-for-me.html
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Maslow: Needs and Resources
http://www.theappgap.com/collaboration-whats-in-it-for-me.html
• Financial
• Natural (Food)
• Human Skills
• Health
• Rescue
• Transportation
• Education
• Production
• …
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Social Life Networks
Connecting People to Resources
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Modern Data
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Knowledge Data
Processing
Information/ Decision
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Decisions
Food Friend
What???
Information Data
Knowledge
Observations
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• Desired state (Goal)
• System model and Control Signal (Actions)
• Current State (for Feedback)
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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.
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Social Networks
Connecting
People
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Current Social Networks
Important Unsatisfied Needs
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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
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Social Life Network
Aggregation
and
Composition
Situation
Detection Alerts
Queries
Information
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Fundamental Problem
Connecting People to Resources effectively, efficiently, and promptly
in given situations.
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• 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.
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Smart Social Systems Architecture
Situation Recognition
Evolving Situations
Available Resources
Identified Needs
Need- Resource Matcher
Communication/Control
Unit
Resources
People
Data Sources
Database System
satellite
Environmental Sensor Devices
Social Media
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Concept Recognition: Last Century
23
Environments
Real world Objects
Situations
Activities
Single
Me
dia
SPACE TIME
Scenes Location aware
Visual Objects
Trajectories
Visual Events
Location unaware
Static Dynamic
Location aware
Location unaware
Static Dynamic
Data = Text or Images or Video
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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
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Concept Recognition: This Century
25
Environments
Real world Objects
Situations
Activities
SPACE TIME
Location aware
Location unaware
Static Dynamic
Hete
roge
ne
ou
s Me
dia
Location aware
Location unaware
Static Dynamic
Data is just Data. Medium and sources do not matter.
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• relative position or combination of circumstances at a certain moment.
• The combination of circumstances at a given moment; a state of affairs.
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Situations: Definition
An actionable abstraction of observed spatio-temporal characteristics.
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• 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.
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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.
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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
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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.
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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
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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.
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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.
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Observed State(Situations)
Intelligent Social Systems: Spatial Perspective
Real World Events
Ob
serv
atio
ns
Control Signals
Goal
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Observed State(Situations)
Intelligent Social Systems: People Perspective
Real World Events
Ob
serv
atio
ns
Control Signals
Goal
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• 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))
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Retail Store Locations
Net Catchment area
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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
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Operations
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Pattern Matching
Aggregate
@ Characterization
∏ Filter
Classification
72%
+
+
Growth Rate = 125%
Data Supporting
parameter(s) Output Operator Type
+
Classification method
Property required
Pattern
Mask
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• 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, 𝑟𝑘 = 𝑎𝑟𝑔𝑚𝑖𝑛 (𝑢𝑖. 𝑙𝑜𝑐, 𝑟𝑘. 𝑙𝑜𝑐𝑠)
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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
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Output Ingestor
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 46
Real-Time DataSource (e.g., sensor
streams, geo-image streams)
Near Real-Time DataSource
(e.g., preprocessing data streams, social
media streams)
Raw Event
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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
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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
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Flood level - Shelter
Flood Level Shelter
Classify (Flood level - Shelter)
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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.
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What defines a person?
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Activities
Communications
Media
Persona: Turning Disassociated Data into Meaningful Information
AGGREGATED PERSONAL ANALYTICS
Sensors
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Persona
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Personal EventShop: Micro Situation Detection
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HEALTH PERSONA
Logical Sensor Physiological Sensors Fitness Tracking Sensors
Life Event
Kinetic Event
Physiological Event
Food Event
Personicle
Health Persona Framework
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Life Events Ontology
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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
Tran
spo
rtat
ion
wal
kin
g
wal
kin
g
Tran
spo
rtat
ion
Tran
spo
rtat
ion
Tran
spo
rtat
ion
Home Home Work Caspian
Hav
ing
Bre
akfa
st
Do
ing
the
dis
hes
Co
mm
uti
ng
Co
mm
uti
ng
Wal
kin
g
Wal
kin
g
Mee
tin
g
Mee
tin
g
wo
rkin
g
Wo
rkin
g
Exer
cise
Exer
cise
Wo
rkin
g
Wo
rkin
g
Wo
rkin
g
Wo
rkin
g
Mee
tin
g
Mee
tin
g
Wal
kin
g
Co
mm
uti
ng
Co
mm
uti
ng
Co
mm
uti
ng
Sho
pp
ing
Sho
pp
ing
Co
mm
uti
ng
Par
tyin
g
Par
tyin
g
Par
tyin
g
Par
tyin
g
Par
tyin
g
Co
mm
uti
ng
Co
mm
uti
ng
Co
mm
uti
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Slee
pin
g
Slee
pin
g
Wat
chin
g TV
Cle
anin
g
Personicle
Commute Walk Meet work Exer. work Meet Commute shop party Commute Sleep
GP
S lo
cati
on
tra
ckin
g
Cal
end
ar
Act
ivit
y le
vel
Pers
on
icle
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SLN Architecture
EventShop
Personal EventShop
Evolving Situations
Available Resources
Identified Needs
Need Resource Matcher
Communication/Control
Unit
Resources
People
Data Sources
Database System
satellite
Environmental Sensor Devices
Social Media
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Research Challenges
• Situation Recognition
• Persona and Personal Context
• Chronicle Analytics and Visualization
• Massive Geo-Spatial Heterogeneous Stream Processing
• Dynamic Need-Resource Optimization
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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
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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.
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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.
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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.
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Solution: Tweeting Applications
• Develop focused Apps: Focused MicroBlogs
• Get all information from ‘motivated’ and collaborative users.
• Help them solve their problem.
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WAZE: Outsmarting Traffic, Together
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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.
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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.
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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?
– …
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Live EventShop and Collaboration
• Live EventShop Demo – http://auge.ics.uci.edu/eventshop/
• 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