Objective self modeling real you

© Ramesh Jain Slide 1

Using Data Streams to Model Real You

Ramesh Jain (Collaborators: Laleh Jalali, Hyungik Oh)

[email protected]


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


Humans are Smart Sensors.

3

Humans are Smart Actuators

Humans are the goal as well as the source of Technology.


TheMagicDevice:MobilePhone

Middle 4 Billion

Top 1.5 Billion

Bottom 1.5 Billion

MOP: Improving Information

Environment

TOP: Strong Information Environment

BOP: Deprived of Information

4


This century is different from the last.

Should we think differently???


In 20th century, we tolerated photos in our textual documents.

In 21st century, you create visual documents that tolerate text.


Most Fundamental Problem:

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


Major transformation in human history are a chronicle of building

Social Machines for

How People’s need are connected to Resources.


Hunter Gatherer: You went to Food.

Now food comes to you.


Until 2000, you went to make a call.

Now call finds you.


Social Life Networks: Using Connections

Physical World

And

Informa

tion Systems

Environment and Resources Information

Personal Situation and Needs Information

Matching

Action Signals


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

© Ramesh Jain Slide 13 4/13/16 13

© Ramesh Jain Slide 14 ICNC 2015 Anaheim, CA.

14


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.


Disruption in Healthcare

We go to the source of healthcare.

Can healthcare come to me in time?


Until recently, you were a folder.

Now You are Your Data.

Disruption Time


Time for Disrupting the Undisrupted.

Invented in 1816. Has not changed much since 1940. 18


Smartphone is the Personalized 24/7 Recording Stethoscope


Understanding Self

What do you tell your doctor?


Personal Health

What’s Lifestyle got to do with it?


Understanding Self Has Been Evolving

•  Anecdotal •  Diarizing data

•  Quantified Self

22


Objective Self helps in understanding and predicting situations.

23


Data Streams to Objective Self


REALITY

DATA

MODEL

Modeling

Explain , Prevent , Understand Predict

ABSTRACTION

[Sensors, Web2.0, Infrastructures, etc.]

[Conceptual, Mathematical, Graphical, Statistical, etc.]



Big Data is used for finding models.

Some models are INTERESTING.


Correlation (insight)

Hypothesis

Experiment Design

Test

Correlation is Not Causality


Hypothesis

Experiment Design

Test

Causality

Correlation is Mother of Causality


Causality is about Event Streams


Life Events are important for organization of Diverse Data Streams.

Different observation sources help in recognition and interpretation of events.


Good Insight = Induction + Deduction

Correlational Model


Good Insight = Induction + Deduction

Causal Model


Detecting Important Co-occurances


Life Events and their Attributes


Learning or Using Learned Knowledge


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


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 ] )


Cycling followed by attending class within 1 hour.

Behavior Analysis with Co-occurrence Matrix

Life Events

Life

Eve

nts

Ei and Ej are life events.

Unknown event


Co-occurrence Between Life Events

Behavior Patterns


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:


User Interface for Interactive Knowledge Discovery and Model Building

Dat

a-D

riven

H

ypot

hesi

s-D

riven


Asthma Risk Factor Recognition


Correlation Analysis

Solar radiation So

lar

radi

atio

n

Temperature Wind

PM2.5

Air pressure Rain

Snow Humidity

Asthma outbreak

Tem

pera

ture

W

ind

PM2.

5

Air

pres

sure

R

ain

Snow

Hum

idity

Ast

hma

outb

reak


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.


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.


Concurrent Co-occurrence Matrix

Asthma outbreak Event

Envi

ronm

enta

l Ev

ents

PM2.5_staysHigh while Asthma outbreak happens

Seed a hypothesis and investigate it !


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.


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.


Person to Society


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.

51


Need Data. Need Collaborators.


Thanks for your time and attention.

For questions: [email protected]

Date post:	06-Jan-2017
Category:	Health & Medicine
Upload:	ramesh-jain
View:	326 times
Download:	6 times

Objective self modeling real you

Health & Medicine