The document discusses utilizing data streams to better connect individual needs with resources, emphasizing the evolving roles of technology, particularly smartphones, in personal health monitoring and data collection. It highlights significant transformations in how personal and societal health data can improve disease models and quality of life, utilizing insights from event stream modeling and co-occurrence detection of health-related factors. The speaker advocates for a more proactive approach in healthcare, leveraging personal data to anticipate and address health issues.

1. © Ramesh JainSlide 1
Using Data Streams to Model Real
You
Ramesh Jain
(Collaborators: Laleh Jalali, Hyungik Oh)
jain@ics.uci.edu

2. © Ramesh JainSlide 2
Society exists only as a mental concept;
in the real world there are only
individuals.
-- Oscar Wilde

3. © Ramesh JainSlide 3
Humans are Smart Sensors.
3
Humans are Smart Actuators
Humans are the goal as well as the
source of Technology.

4. © Ramesh JainSlide 4
The Magic Device: Mobile Phone
Middle 4 Billion
Top 1.5
Billion
Bottom 1.5 Billion
MOP: Improving
Information
Environment
TOP: Strong Information
Environment
BOP: Deprived of
Information
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This century is different from the last.
Should we think differently???

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In 20th century, we tolerated photos
in our textual documents.
In 21st century, you create visual
documents that tolerate text.

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Most Fundamental Problem:
Connecting People’s Needs to
Resources Effectively, Efficiently,
and Promptly in given Situations.

8. © Ramesh JainSlide 8
Major transformation in human
history are a chronicle of building
Social Machines for
How People’s need are
connected to Resources.

9. © Ramesh JainSlide 9
Hunter Gatherer:
You went to Food.
Now food comes to you.

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Until 2000, you
went to make a
call.
Now call finds you.

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Social Life Networks: Using
Connections
Physical
World
And
Informa
tion
Systems
Environment and Resources
Information
Personal Situation and Needs
Information
Match
ing
Action Signals

12. © Ramesh JainSlide 12
EventShop : Geospatial Situation Detection
Situation
Recognition
Data Stream
Ingestion and
aggregation
Database
Predictive
Analytics
Personal EventShop: Life Event Detection
Personal
Situation
Recognition
Database
Personal
Data
Ingestion
Objective Self
Recommendation
Engine
Need- Resource Matcher
Identify Resources and Needs
Resources Needs
Evolving Global Situation
Evolving Personal Situation
Actionable Information
Social Life
Networks

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14. © Ramesh JainSlide 14 ICNC 2015 Anaheim, 14

15. © Ramesh JainSlide 15
Everyone’s Respiratory Health is Different
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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Disruption in Healthcare
We go to the source of
healthcare.
Can healthcare come
to me in time?

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Until recently, you
were a folder.
Now You are Your Data.
Disruption Time

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Time for Disrupting the Undisrupted.
Invented in 1816. Has not changed much since 1940.
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Smartphone is the Personalized 24/7
Recording Stethoscope

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Understanding Self
What do you tell your doctor?

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Personal Health
What’s Lifestyle got to do with it?

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Understanding Self Has Been Evolving
• Anecdotal
• Diarizing data
• Quantified Self
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Objective Self helps in understanding
and predicting situations.
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Data Streams to Objective Self

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REALITY
DATA
MODEL
Modeling
Explain , Prevent , Understand
Predict
ABSTRACTION
[Sensors, Web2.0,
Infrastructures, etc.]
[Conceptual,
Mathematical,
Graphical, Statistical,
etc.]

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Big Data is used for finding models.
Some models are INTERESTING.

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Correlation
(insight)
Hypothesis
Experiment
Design
Test
Correlation is Not Causality

29. © Ramesh JainSlide 29
Hypothesis
Experimen
t Design
Test
Causality
Correlation is Mother of Causality

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Causality is about Event Streams

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Life Events are important for organization of
Diverse Data Streams.
Different
observation
sources help in
recognition and
interpretation of
events.

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Good Insight = Induction + Deduction
Correlational Model

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Good Insight = Induction + Deduction
Causal Model

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Detecting Important Co-occurances

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Life Events and their Attributes

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Learning or Using Learned Knowledge

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Event
Co-occurrence
Detection
High level
Pattern
Formulation
Pattern
Occurrence
Detection
…
Data Streams Event Streams
While pollen is high,
person starts exercise,
within T time units she
gets asthma attack
((exercise ;ωT asthma_attach ) || high_pollen )
Semi-interval Event Sequences
Objective Self Modeling Framework
Analyzing and answering
questions you know to
ask. the “known
unknowns” problem
Gaining insights when you
don’t know what questions
to ask. the “unknown
unknowns” problem
Data-Driven Analysis
Hypothesis-Driven Analysis

38. © Ramesh JainSlide 38
Pattern Formulation Operators
Selection Operation σP
Sequence Operation ( ρ1 ; ρ2 ; … ; ρk )
Conditional Sequence Operation ( ρ1 ;ωΔt1 ρ2 ;ωΔt2 … ; ωΔtk-1 ρk )
Concurrency Operation ( ρ1 ρ2 … ρk )
Alternation Operation ( ρ1 | ρ2 | … | ρk )
Time ( ωΔt ρ )
Co-occurrence ( COρ1 , ρ2 [Δt ] )

39. © Ramesh JainSlide 39
Cycling followed by attending
class within 1 hour.
Behavior Analysis with
Co-occurrence Matrix
Life Events
LifeEvents
Ei and Ej are life events.
Unknown event

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Co-occurrence Between Life Events
Behavior Patterns

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Cause - Effect Pattern Structure
event1 event2
event3
event4
Effect
Time lag between
events
Sequence of events
Events in parallel
Cause 1
Cause 2
Cause 3
(No medication ; Exercise) è Asthma attack
(Exercise Pollen high) è Asthma attack
(Exercise (Pollen high | pollution high)) è Asthma attack
(Exercise Pollution high) è Asthma attack
Exercise è Asthma attack
Δt
Formulate and query complex patterns:

42. © Ramesh JainSlide 42
User Interface for Interactive Knowledge
Discovery and Model Building
Data-Driven
Hypothesis-
Driven

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Asthma Risk Factor Recognition

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Correlation Analysis
Solar radiation
Solar
radiation
Temperature
Wind
PM2.5
Air pressure
Rain
Snow
Humidity
Asthma outbreak
Temperature
Wind
PM2.5
Airpressure
Rain
Snow
Humidity
Asthma
outbreak

45. © Ramesh JainSlide 45
Asthma Risk Factor Recognition
1) Pollution increases suddenly
followed by high wind while
temperature increases slightly will
cause an asthma
Outbreak within 2 days.
2) Thunderstorm followed by
temperature decreases steadily will
cause an asthma outbreak within 1 day.

46. © Ramesh JainSlide 46
Event Stream Modeling
• Apply Symbolic Aggregate
approXimation (SAX) algorithm
with 3 symbols on time-series
data.
a
b
c
• Define meaningful events for
each SAX code.

47. © Ramesh JainSlide 47
Concurrent Co-occurrence Matrix
Asthma outbreak
Event
Environmental
Events
PM2.5_staysHigh
while Asthma
outbreak happens
Seed a hypothesis and investigate it !

48. © Ramesh JainSlide 48
Results
Temperature fluctuation has the most impact in the fall and winter seasons and
it is not a risk factor during spring or summer
During spring and summer, when rain suddenly increases to a very high level,
an asthma outbreak is more probable
The effect of PM2.5 is not noteworthy in
the fall and winter seasons.

49. © Ramesh JainSlide 49
Results (Cont.)
• When PM2.5 increases followed by temperature stay high within 3 days,
then asthma outbreak is probable.
• When wind decreases followed by PM2.5 increases within 5 days, then
asthma outbreak is probable.
• When rain increases followed by PM2.5 stay low within 4 days then an
asthma outbreak is probable.

50. © Ramesh JainSlide 50
Person to Society

51. © Ramesh JainSlide 51
Use Massive Volume of Objective Personal Data for
Building Better Disease Models.
Persona and Societal Health
Use Disease Model and Personal Data for Better Quality of
Life.
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52. © Ramesh JainSlide 52
Need Data.
Need Collaborators.

53. © Ramesh JainSlide 53
Thanks for your time and attention.
For questions: jain@ics.uci.edu