social life networks (eventshop and personal event shop)
TRANSCRIPT
Social Life Networks: EventShop and Personal Event Shop
Experiential Systems LaboratoryUniversity of California, Irvine
Siripen Pongpaichet, Laleh Jalali, Mengfan Tang(Adviser: Ramesh Jain)
Outline
• Social Life Networks• EventShop• Personal EventShop• Predictive Analytics
Fundamental ProblemWeb 1.0 Connecting People to Documents
Web 2.0 Connecting People to People
“Social Life Network”
Connecting Needs to ResourcesEffectively, Efficiently, and Promptly
In given situations.
EventShop : Global Situation Detection
Situation Recognition
Evolving Global Situation
Personal Situation
Recognition
Personal EventShop
Evolving Personal Situation
Need- Resource Matcher
Recommendation Engine
PersonaDatabase
Resources
Needs
Data Ingestion
Wearable Sensors
Calendar
Location….
Dat
a So
urce
s
….
Data Ingestion
and aggregation
Database Systems
Satellite
Environmental Sensor Devices
Social Network
Internet of Things
Actionable Information
Big Data “NEED” Big Insight
Value
Figure Ref: http://damfoundation.org/2012/09/big-data-big-deal/
Extracting VALUE
from Data
WHERE It Matters
and
WHEN it Matters
To WHOM it Matters
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Related Services
http://google.org/crisismap/sandy-2012
Mash Up: Google Crisis Maps
http://google.org/crisismap/2013-uttrakhand-floods
one-touch SOS
Mobile Applications
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“EventShop” towards SLN
7/03/2013[Pongpaichet 2013] EventShop: recognizing situations in web data streams
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From Heterogeneous Data Sources to Situation Recognition
7/03/2013
Example Notification / Alerts:
You are currently in the area where there is a high chance of flooding,
these are available shelters within 10 miles around you.Space
Time Situation
Resources
People
(Space, Time, Theme)
[Pongpaichet 2013] EventShop: recognizing situations in web data streams
Data Unified Format: STT
• Each data source is transformed into unified data format, S-T-T (Space - Time - Theme)
• Then each STT data stream is aggregated to form E-mage (Event-Image) stream
“iphone” popularity in USA from Tweets data source
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E-mage Algebra & Operators
Pattern Matching
Aggregatioin
@ Characterization
∏ Filter
Segmentation
72%
+
+
Growth Rate = 125%
Data Supporting parameter(s) OutputOperator Type
+
Segmentation methods
Property required
Pattern
Mask
7/03/2013
EventShop Web UI
Multi-Spatio-Temporal Bounding Boxes and Resolutions
• “Pyramid of E-mage” resolution is introduced to represent the real world in E-mage at different (zoom) levels.
• Each Stel (a pixel in the E-mage) represents a single fixed ground location.
• Precision vs Computational Cost
Rasterization and Error Propagation
• Data Error Factors:– Uncertainty of data stream– Data loss during data aggregation– Uncertainty during data conversion– Data error during data conversion
• To design the most optimum situation recognition model, we need to find the new cost evaluation method that will consider both data accuracy and computational cost.
Personal EventShop
• Understanding a person• Personicle: Personal Chronicle• Detecting evolving personal situation• Recommending action
Wearable 2013:Data Data Everywhere, nor any Insight to Use.
http://www.medhelp.org/
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HEALTH PERSONA
Logical SensorPhysical Sensors
Life Event
Motion Event
Physiological Event
Food Event
Personicle
Health Persona Framework:Humans as Actuators.
Environmental Sensors
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Event Categories
• Event Definition: ▫ NOT abnormal observation. ▫ NOT point event. ▫ BUT interval event
Motion event Stream
Physiological event Stream
Personicle
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Input Data Manager
Raw Data StreamsEvent Streams** Predictive
Data Analytics
Personal Data Warehouse
Pers
onal
Dat
a So
urce
s
* location stream, activity stream, motion stream, calendar stream** life-event stream, motion-event stream, physiological-event stream, food-event stream
Personal EventShop Platform
Data Streams *
Data Analytics
Persona
Profile Info.Asthma History
Domain knowledge
Asthma AttackSymptoms & Severity
Recom. Engine
Evolving personal situation
Moves app
Nike Fuel
Foursquare
Google Calendar
Personicle
Actionable Information
Environmental Factors
Activity Detection
Location Data Stream
Accelerometer Data Stream
Feature Extraction
Learning and Inference
Data Collection
Extracted Feature Set
Recognition Model
• Frequency Domain • Energy • Entropy• Coefficients Sum• DC Component
• Time Domain• Mean , Median• Std Deviation• Min, Max, Range• Average Absolute
Difference• Average Resultant Variation
Activity Level
Asthma Triggers
Indoor Allergens
Outdoor Allergens
Smoke
Air Pollution
Chemical Irritants
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Predictive Techniques for Preventive Advise
•Take your inhaler•Drive instead of walking•Skip school/ work
today!
Insight: Exercise Triggers My Asthma
• Red: “Severe asthma attack”.• Orange: High activity level.
• Exercise triggers my asthma!
Personicle
Activity Stream
t1 t2 t3 t4 t5
Enrich Personalized Asthma Risk
• Predict air quality at air quality measuring sites.
• Interpolate air quality at the locations not covered by measuring sites.
• Predict personalized asthma risk by using EventShop and Personal EventShop.
Daily Ozone Data
Ref- http://www.arb.ca.gov/aqmis2/aqmis2.php
Time-Series Prediction
Autoregressive model:
K-Nearest Neighbors Regression
Air Quality Index
A limited number of sites.
How can we predict the air quality in the area not covered by the sites?
Historical air quality data
Interpolate Air Quality Index
The Learned models trained on multiple data sources
Learned models
Interpolate Air Quality Index
Personalized Asthma Risk Prediction
• The predictive model– Correlation with Personicle
• Temporal correlation in a location• Geo-correlation between locations
– Two sets of features• Spatially-related• Temporally-related
– Machine learning methods• Supervised learning